Forem: Imad

Ultimate Guide: Statistics for Data Science(Beginners to Advanced)

Imad — Fri, 06 Oct 2023 13:25:27 +0000

STATISTICS is The Most Essential Part of Data Science, Machine Learning, MLOps and data engineering discipilines.

In this Age of AI and Generative AI, data is being generated and used at an unprecedented rate to make the world more exciting to live.

Someone needs to process that data, extract the insights from that data and make predictions for better outcomes using Machine Learning and Deep Learning. To process, extract insights and predict outcomes, we need statistics.

How?

Let’s find out:

First of ALL.

What is Statistics?

Statistics is the process of using data to understand the world around us. It involves collecting data, summarizing it and using it to make predictions about the population from where data is extracted.

For Example: An organization might use statistics to understand the demographics of its employees, the effectiveness of their work or factors that lead to enhanced work performance.

Role of Statistics in Data Science

The core of data science is Machine Learning and deep learning which uses algorithms which in turn are based on Statistics. Data science problems cannot be solved if you do not have a grasp of statistical concepts.

Without any doubt, Statistics can be hard for some people and some people are innately professional due to their previous experience. Hard concepts include complex mathematical notations, greek notation and complicated equations that make it hard to develop an interest in the subject.

But this complexity can be addressed with simple, clear and concise explanations of those concepts by leveraging some profound books and courses that are mentioned in the resources portion of this article.

From Exploratory data analysis to choosing a machine learning algorithm to designing hypothesis testing experiments, statistics is a must-have for anyone diving into data analysis, data science, data engineering and working with LLMs.

Why Should You Ace Statistics?

Data is the new currency of the world that is mandatory for the smallest of companies to the largest organizations to manage tasks, use insights and predict the incoming outcomes for better business and work.

Statistics help answer these questions:

What features are important in raw data?
What features can make a better model?
How should we measure the performance of that model?
What are already known outcomes and what we can achieve more?
How can we fine-tune the model to make it more efficient?

Statistics in Data Science Project Lifecycle

Statistics is involved in every step of the data science project lifecycle. Here is how:

Defining the Problem

The most basic yet important part of the data science project lifecycle is defining the problem. Because the most important part of predictive modelling is understanding the problem and carefully defining it.

Precisely defining the problem helps in deciding what kind of problem we would be dealing with and what techniques we can use during the next steps of the cycle.

However, problem defining is not straightforward because most of the time, the problem is not laid out until we explore the data. So for beginners, it may require them to be somewhat proficient in the EDA(Exploratory Data Analysis).

Exploring the Data

Data Exploration involves data collection and gaining a deep understanding of the distribution of data variables and their relationship with other variables.

Here, if you are proficient in a specific domain that comes in handy because you can already have an idea what kind of data variables you will be dealing with. For Example, if someone is from a finance background, he/she might not need to google the variables in the data like credit, FICO Score etc.

Statistic concepts that are used here, are descriptive statistics.

Data Cleaning and Preprocessing

Often the data we are given or collected is not very useful for conducting experiments on it. For example: there could be missing values, data errors (from bad observations of devices) and unformatted data(Observation of different scales).

Data Cleaning requires outlier detection and missing value imputation from statistics.

Further, Data preprocessing is used to make data available in a confined structure that would be useful for model selection.

Data processing can be done efficiently if you have a good grasp of data sampling, feature selections, scaling and encoding.

Model Selection and Evaluation

First, to predict an outcome, a model needs to be selected and that model’s evaluation for its learning methods. In the field of statistics, Experimental design is a subfield that deals with the selection and evaluation process of models which requires a profound understanding of Statistical hypothesis tests and estimation statistics.

Building and Fine-Tuning Model

Once the model is selected, data is cleaned data is pipelined to that machine learning algorithm to test different hypotheses. Keep in mind that every machine learning model has hyperparameters that enable the data scientist to completely Fine-tune for a better prediction of outcomes.

Complete the Statistics course outline for All levels (Beginners to Advanced)

Beginner Level

Module 1: Introduction to Statistics

What is statistics?
Types of data
Descriptive statistics
Inferential statistics

Module 2: Probability

Basic probability concepts
Probability distributions
Bayes’ theorem

Module 3: Hypothesis Testing

Null and alternative hypotheses
Type I and Type II errors
Common statistical tests

Intermediate Level

Module 4: Linear Regression

Simple linear regression
Multiple linear regression
Model evaluation

Module 5: Logistic Regression

Logistic regression basics
Model interpretation
Applications in data science

Module 6: Decision Trees

Decision tree algorithms
Model selection and evaluation
Applications in data science

Advanced Level

Module 7: Clustering

Clustering algorithms
Model selection and evaluation
Applications in data science

Module 8: Time Series Analysis

Time series forecasting models
Model evaluation
Applications in data science

Module 9: Natural Language Processing

NLP basics
Statistical methods for NLP
Applications in data science

Practical Learning Tips for Statistics

The top-down technique and the bottom-up approach are the two basic ways to learn statistics for data science.

Top-Down Method

The top-down strategy includes starting with a broad understanding of statistics before delving further into the particular concepts and techniques required for data science. For those who are already proficient in other branches of mathematics, such as calculus and linear algebra, this method works well. You can start by enrolling in a general statistics course or reading a general statistics book to learn statistics for data science utilizing the top-down method. You can start learning more advanced statistical techniques for data science, such as machine learning and natural language processing, once you have a fundamental foundation in statistics.

Bottom-Up Method

The bottom-up strategy includes beginning with the precise statistical techniques required for data science and working your way up to a broader understanding of statistics. For those who are unfamiliar with other branches of mathematics, this method works well.

You can begin by enrolling in a data science course or reading a data science textbook to learn statistics for data science utilizing the bottom-up method. With the help of these resources, you may typically learn the practical statistical techniques required for data science. You can start learning more about the fundamentals of statistical approaches for data science once you have mastered the fundamentals.

Learning Resouces

Books

**Statistics for Data Science *by *James D. Miller

It provides a comprehensive introduction to statistics for data science. This book is easy to follow and is well-written and covers a wide range of topics for statistical understanding.

**The Elements of Statistical Learning** by Trevor Hastie, Jerome Friedman, and Robert Tibshirani

It is a little bit more advanced book on statistics that covers a wide range of machine-learning algorithms. The book is more biased towards a theoretical understanding of concepts but it is also easy to follow.

**Naked Statistics *by *Charles Wheelan

This book is for people who dread mathematics and can only learn through practical examples of real-life scenarios. You can follow it with The Elements of Statistical Learning, which would make a perfect combination.

**Statistical Learning with Python** by Gareth James, Daniela Witten, Trevor Hastie, and Jonathan Taylor

This Book covers advanced statistical deep-learning topics along with NLP concepts. That makes you stand out in your data science landscape.

Courses

If you are a Course learning maze, You are covered with these courses mentioned below:

**Introduction to Statistics for Data Science** by Stanford University on Coursera
**Statistics with Python** by the University of Michigan on Coursera
**Mathematics for Machine Learning and Data Science** by *DeepLearning.AI on Coursera*

Final Thoughts

The ability to use statistics is crucial for data scientists. You will be able to make sense of the enormous volumes of data that you will come across at work by mastering statistics. You may find a variety of tools to assist you in learning statistics for data science, so pick the strategy and learning resources that suit you best and get started learning right away.

I will be creating more on these topics in future. So follow for more valuable insights and projects on data science, deep learning and artificial intelligence, especially NLP.

You need to know the DATA in Data Science

Imad — Sat, 30 Sep 2023 08:25:30 +0000

Imagine a world without data, where there is no concept of Social Media, No Netflix, No Online Shopping. Would you live in that?

It’s even hard to imagine that, right?

That is the importance of data in our daily life, Because data is everywhere and it’s mandatory for all kinds of businesses which gain insights from data and pivot their strategy depending on those insights.

Data Scientists are the unsung heroes of our daily life dopamine, who leverage their skills in the collection, cleaning and analysis of data to extract valuable insights and tell the algorithms to show us what our desires are, Good or Bad, it is another debate.

So, these heroes need to understand the first word of their heroism which is DATA.

There are two types of data on the scale of measurement in data science: Qualitative and Quantitative

Qualitative Data

Qualitative data is non-numerical data that describes or characterizes something. It can be words, images or videos.

Some examples of Qualitative Data:

Customer satisfaction ratings
Product Reviews
Social Media Posts
Medical Images

Types of Qualitative Data

Nominal Data

Nominal data is the most basic type of data. It is qualitative data that can be categorized but cannot be ordered.

For Example: Eye Colour, Gender and country of origin, Customer ID

Ordinal Data

The type of Qualitative data that can be ordered.

For Example: A survey ranking of customer satisfaction from 1 to 5, education level

Quantitative Data

Quantitative data is numerical data that can be measured and counted.

Some Examples of Quantitative Data

sales figure
Website Traffic
Temperature Readings
Sensor Data

Types of Quantitative Data

Discrete Numerical Data

The type of Quantitative Data that can be counted.

For Example: The number of children in the family or the number of goals scored in a soccer match

Continuous Numerical Data

The type of Quantitative data that can be measured.

For Example: A person’s height or weight of a parcel, the customer’s age, and product price.

Benefits of Classifying the Data Types in Data Science

A better understanding of data: Classifying data types helps us understand what data can be used for. For Example: if we know that a piece of data is continuously numerical, operations like mean and standard deviation can be done on it.
Choosing the right tools: Different tools and algorithms are used for different types of data. If we data is already classified, we can pick the right tools to perform operations on the data.
Improvement in Efficiency: Understanding data types can help improve the efficiency of our model because we are less likely to stray away from the path as we already know what we should be doing with the data.
Effective Communication: Proficiency in getting insights from data from relative data types brings an effective description of our findings as we can clearly communicate the process involved in our findings.

Real-World Examples of Data Types Usage

A data scientist working on a fraud detection model will be using nominal data such as transaction type to identify fraudulent activities.
A marketing analyst working on a customer segmentation campaign will be using ordinal data such as customer purchase history to identify possible customer segmentation.
In a product recommendation system, a data scientist will possibly be using discrete numerical data such as the number of times a customer has viewed some type of product to recommend him.
A data scientist working in a financial company might be working on a risk assessment model for certain investments, and he will be using numerical data like customer age or income to assess the risk of defaulting on the loan.

Final thoughts

Understanding the different data types is mandatory for data scientists. By ensuring your knowledge of data types, you can use the right tools and algorithms that will improve the efficiency and accuracy of the machine learning model.

More core concepts about Data Science and Machine Learning Models are on their way. So, Follow for more.

Thanks for reading, Happy Learning!

Generative AI and its Economy Impact (Realistic Answers)

Imad — Wed, 27 Sep 2023 13:09:08 +0000

A comprehensive look at Generative AI Economy. There are a lot of articles and videos about Generative AI but most of them do not weigh the economic impact it will have on the economic horizon.

Yes, People are talking about losing their jobs to AI which is a great loss for anyone. But, Will it create enough jobs and opportunities for people living now and also for future generations, this is the question we should be curious about, And most people are.

Generative AI is a rapidly moving supercar that is running on the track of Artificial Intelligence that has the capacity to destroy and revolutionize people’s lives realistically, we have to admit both sides of the picture (We will talk about both).

Talking about revolutionizing, it can create content in an instant (Not as efficiently as humans as of right now), write code(Github Copilot), generate images(Midjourney, DALL E2 and 3), and edit videos (Adobe has done some amazing work).

Let’s dive in:

Positive Side of Generative AI with real-world examples

Despite different challenges in formulating and regulating AI, it has been a great ride for all of us (It’s the truth).

Accelerating Innovation

AI can be used to generate ideas and content at a really fast speed, we all have seen it. It can also be used to accelerate the innovation process, yes it can be a useful tool or colleague to researchers for the benefit of humanity.

Jeez(Scary huh but exciting at the same time)

How can it be useful for innovation, you can already have an idea but let’s take an example.

Currently, In the healthcare industry, generative AI is being used to develop new drugs, treatments and medical diagnoses. A company called **Deepmind has developed a generative AI system** that can predict the structure of proteins in humans, which in turn could be used for the predictive treatment of different diseases.

Let’s talk about efficiency improvement. Shall we?

Improving Efficiency

Generative AI can be used to automate a lot of tasks and jobs (we will talk about it later), like customer support jobs. It can automate a handful of tasks in the marketing and product development industry. Since we are on the positive side now, it can free the human workforce to work on better, artistically creative and more complex(work, work, work never ends) strategic tasks.

In the finance industry, scams are common and we have been a victim of them for as long as our history goes(I am not talking about your ex’s scam). But, AI can stop it (Financial scams), how? We can securely integrate AI models (Supervised or unsupervised ML Models) in financial transactions to check the behavioural aspects of transactions which can predict the probability of scams. By the way, it can also used to detect phishing scams (either love-related or financial), thus you are safe emotionally and monetarily.

Talking about scams, Goldman Sachs is using Generative AI to develop new trading strategies to put everyone out of business. Just kidding, they cannot put everyone out of business or otherwise, there will be an uprising against them (Realistically, History tells us that).

Sea of Amazing Products

AI can be (is being actually) used to create products and services that were not possible before and in a really short period. It’s better than innovation, right?

For Example, Generative AI can be used to create personalized educational material(How to learn Python with books for book wizards or courses for visual learners or a combination of both ), Custom clothing (Just like the elite class) and new forms of entertainment (Bye Bye everything, but it depends on your personal preference).

Talking about amazing things, General Motors is using (or taking a good amount of help) generative AI to design a JET Engine. (Now that is Something good)

But now, it is the TIME.

Time to go for the dark side.

Negative Side of Generative AI

Everything has a negative side somehow (Duality works that way in our real world). We cannot assume everything is like sunshine and rainbows.

Job Displacement

It is one of the biggest concerns about Generative AI that it could lead to widespread job displacement(800 million to be exact by 2030) because every company and organization regardless of its size and market share wants to reduce its labour cost.

The reality is, that Generative AI will become more efficient for complex tasks, and jobs will be lost in the name of automation. This could be even the loss of millions of jobs around the world particularly in industries that are heavily reliant on labour.

This could include manual labour workers and some jobs related to data like data entry or data analysis (it will be polished). These are just such examples. But the fact is, some jobs related to building AI systems will also be automated. Yup, AI Engineers and data scientists will transform themselves as well but they cannot be properly automated though. (It is what it is)

Economic Inequality

Well, that is something no one has ever wondered I suppose. Generative AI is a complex tool to build and manage, that requires sophisticated knowledge from humans which comes at a good cost. That means, people who are already wealthy can use and would use it (Why wouldn’t they) to gain more money, power and sustainability for their wealth. And they are already most likely to be Top 1 or 2% (My observation).

These people or organizations will have an unfair advantage concerning their possible competitors or small businesses which will lead to greater market share and their dominance in the business. Just like the Goldman Sachs example I gave above.

Horrors of War and Misinformation

Many of us have seen deep fakes of Obama a million times and Trump’s photos and videos were so widespread that the government had to intervene and clear the fog on the matter. These are just small ripples for a possible humongous Tsunami because the manipulation of information has become so easy and effective at the same time with Generative AI that the smallest mistakes can initiate a Global War or Crisis.

Generative AI can create hoaxes and propaganda that could undermine people’s trust in any organization, company or Country and tear apart social cohesion.

Steps to Improve the Negative Impact of Generative AI

There are still some measures we can take to minimize the Negative impact of Generative AI.

Invest in education and training of people in transitioning them to new jobs or upgrading them so that they are less likely to spin in the swirl of automation and lose their jobs.
Regulate Generative AI to prevent its use in the scamming industry, malicious purposes, defamation or cyberbullying.
People who have already lost their jobs to AI should be taken care of either through compensation **or better **give them new jobs by equipping them with new skills related to Generative AI.
Support the development of new industries and businesses that are creating new jobs and economic opportunities either with Generative AI or other fields.

By taking these steps, we can minimize the risk of Negative Impacts of Generative AI.

Factors That Limit Generative AI Economic Impact

The time it will take the Generative AI to be widely adopted. AI is still in the early stages of development and it will consume its fair share of time to be merged in society and business.
The availability of Skilled People in Generative AI can also limit its growth. Generative AI systems are sophisticated and require special skills to train and use them.
Generative AI requires a good amount of money to build and deploy. So, the people or organizations who have access to vast amounts of wealth will enjoy economic control. That could also limit the Generative AIs’ Economic Impact.
Generative is trained on a set of data and information but if the information is biased towards a particular set of people, it will create biased responses against those people that could slow the growth of Generative AI.

Final Thoughts

One way to ensure Generative AI is used for the good of people and humanity is to make sure that it is used ethically and responsibly. This can be ensured by creating proper guidelines for its use and implementing those guidelines. It should also be ensured that no specific group of people take advantage of the economic aspect of Generative Artificial Intelligence but everyone is given a good opportunity to benefit from it.

If you have enjoyed it, Do give a Follow for the Upcoming Articles.

Thanks for Reading, Happy Learning!

Why You Should Not Become a Data Scientist?

Imad — Fri, 22 Sep 2023 09:56:11 +0000

“Sexiest Job in the world”. This is the title that has been given to data science. Without any doubt, it is the most amazing job that pays really well. It is a very lucrative job monetarily because entry-level data scientists make on average $84K a year and depending upon experience and negotiation skills it goes up to 400K a year. Yeah, that is right.

But we are not here to discuss this, Are We?

We are here to answer the question: Why you should not become a data scientist?

The answer is divided into 3 Parts

Technical Skills
Soft Skills
Human Nature

Reasons not to Become a Data Scientist

Everyone has his own abilities and standards to handle problems and only a few people choose to master skills by leveraging the problems that are bestowed upon them.

Technical skills

Math and Statistics:

The field of data science is heavily focused on maths and statistics, so you’re unlikely to love the work if you’re not a fan of these disciplines. By the way, you do not have to be a math nerd. You just have to handle math problems happily because you need statistics and probability for Machine Learning and Deep Learning Algorithms.

Coding:

Data scientists need to be able to code in order to clean and analyze data, build machine-learning models, and create visualizations. If you’re not comfortable programming, this could be a major barrier to entry. Python or Julia are the easiest possible languages for data science but Python is recommended as it has much better support from the Python community.

Constant learning:

Change is inevitable and Data science is a rapidly evolving field, so it’s important to learn new tools and techniques on a regular basis for example, Large Language Models are the talk of the day and businesses have to conduct experiments and research to figure out what they can do with it. So, If you’re not comfortable with constant learning, data science might not be the right career for you.

Soft skills

Communication:

Data scientists need to be able to communicate their findings to both technical and non-technical audiences. If you’re not comfortable speaking and writing clearly, this could be a challenge. In every field, Communication is one of the key indicators of people’s success. We are perceived as we communicate with our appearance and words.

Team Work:

Data scientists often work on cross-functional teams with engineers, product managers, and other data scientists. Because collaboration with your stakeholders is mandatory for the project’s success. If you prefer to work independently, data science might not be the best fit for you.

Wizard:

Data science problems are often complex and ambiguous, and there is no one right answer. You might find yourself convicted of knowing nothing and then figuring out most of the things yourself or with a bit of help. Therefore, If you’re looking for a job with clear-cut answers, data science might not be the right choice.

Naturalistic Preferences

Work-life Balancer:

Data scientists often have to work long hours, especially when they’re working on tight deadlines. Building machine learning algorithms is gonna take some time and the only thing that data scientists can do to reduce that time is to work tirelessly and meet the deadlines. So, If you’re looking for a job with a good work-life balance, data science is not your best option.

Not Coolheaded:

Data science can be a stressful job, especially when you’re working on high-stakes projects. These projects could be really sensitive either ethically or financially with congested deadlines and with uneasiness of not knowing the outcome at all. However, if you’re not good at handling stress, data science might not be the right career for you.

Competitive:

Data science is a very competitive field, and it can be difficult to find a job, especially if you’re just starting out. I mean, it pays really well, it is the sexiest job of the 21st century, and it will be competitive. you need to be comfortable with competition, otherwise, data science is gonna throw you down.

Final thoughts

Data science is an amazing career for people who are enthusiastic about math, statistics, coding, and solving problems. However, it’s important to be realistic about the challenges and demands of the job before deciding whether or not to pursue a career in data science.

Now if you think, you have the guts to endure the stressful and ambiguous world of data science. Congratulations, you are officially signed up for an exciting career and the only thing you need is the perseverance and Course Outline for the Data Science Mastery.

Now Go, Outshine the SUN!

Evolution of Applied Data Scientists in Data Science

Imad — Thu, 21 Sep 2023 10:12:28 +0000

Noise about Applied Scientists has started to rise in the data field. Even, Amazon has been hiring them for a long time.

Data Science is an evolving domain, which brings a lot of exciting and complex things to the data field. As data science evolves, it will bring different aspects and working modules with it. Gone are the days, when a data scientist had to be more efficient in building machine learning Algorithms, now only utilizing the pre-existing models.

‘Woah’

That is a Bold Statement. But, it is in the realm of reality.

Okay, Let’s lay the foundation of understanding, being an Applied Scientist.

Who is an Applied Scientist?
What does an Applied Scientist do?
Difference Between Applied Scientist and Data Scientist
Examples of Work Differences

Who is an Applied Scientist?

A scientist who focuses on the practical application of scientific knowledge to solve real-world issues is known as an Applied Scientist. They utilise scientific methods to create and test new ideas before working to put those ideas into action in the actual world. Applied Scientists work in a variety of fields such as technology, healthcare, energy, and manufacturing.

Skills Needed for Applied Scientist

Excellent analytical and problem-solving abilities
The capacity to deal with and analyse massive datasets
Python, R, or MATLAB (Not Much) programming abilities are required.
Understanding of machine learning and artificial intelligence
Ability to explain technical topics to both technical and non-technical audiences
Ability to operate both individually and collaboratively

Tools and technologies

Specific Tools and Technologies Applied Scientists Use:

Laboratory equipment: Applied scientists working in chemistry or biology may gather and analyse data using laboratory equipment such as microscopes, pipettes, and centrifuges.
Engineering software: Applied scientists working in the field of engineering may create and model products and systems using engineering software such as CAD software or FEA software.
Manufacturing software: production applied scientists may utilise manufacturing software such as CAM software or ERP software to design and optimise production operations.

What does an Applied Scientist do?

Improve the performance of software and hardware systems by developing new algorithms and machine learning models.
Analyse data to spot trends and patterns that may be utilised to enhance corporate operations, create new products, or solve societal issues.
Create and carry out experiments to put fresh theories and hypotheses to the test.
Create and test updated gadgets and technologies.
Collaborate with engineers and other experts to incorporate new scientific findings into commercial services and products.

It seems a bit odd as if we are going the same path as a data scientist. But there is a difference between the two. Let’s find out.

Difference Between Applied Scientist and Data Scientist

Here’s a simplified explanation of the difference between data science and applied science:

Imagine a medical research team discovering a new drug for a rare disease. To determine its safety and effectiveness, a lot of data needs to be collected and analyzed.

Data scientists specialize in collecting, cleaning, and analyzing data using statistical and Machine Learning techniques to extract valuable insights. After analyzing the data, data scientists share their findings with researchers and assist in interpreting the results. This helps researchers make decisions about the drug’s development, including whether to proceed with clinical trials.

On the other hand, An applied scientist may collaborate with researchers to create a new machine-learning model for identifying patients who are probably to react to the medicine. The applied scientist may also collaborate with the researchers to create a novel clinical trial design that is more efficient and successful in establishing the drug’s safety and efficacy.

In short, data scientists are concerned with inventing and implementing new data science techniques, whereas applied scientists are concerned with using current methods and tools to tackle specific issues.

To help you grasp the gap between data scientists and applied scientists, consider the following analogy:

Data scientists are similar to chefs in that they create new recipes and culinary techniques.
Applied scientists are similar to cooks in that they employ pre-existing recipes and culinary techniques to make delectable meals for their consumers.

Examples of Work Differences

Here are some examples of work differences between Data scientists and Applied Scientists

A data scientist at a tech firm may create a machine learning model to predict customer turnover using Python and scikit-learn.
A healthcare company’s applied scientist may use R and TensorFlow to create a deep-learning model to identify illnesses from medical photos.
A data scientist in finance may utilise Hive and Spark to analyse enormous datasets of financial transactions to uncover patterns and trends.
A manufacturing applied scientist may use Hadoop and Python to create a system to forecast machine failures in a manufacturing facility.

These are some examples of work differences between data scientists and Applied scientists, the specific tools and technologies may vary depending on the nature of the project, company and problem.

Final thoughts

Although, Applied scientists seem like enjoying a two-way journey on a single-way ticket, but this is not the case. You ask Why is that? Well, PhDs in subjects like computer science, mathematics, physics or engineering are a common endeavour for applied scientists. So, they already have a profound knowledge of their domains. But, at the same time, it is not necessary as there is always a focus on skills and domain knowledge.

Hurrah, You read it ALL!

If you have any questions, comment and I will be happy to answer your queries.

Thanks and Happy Learning!

Opportunities in AI by Andrew Ng

Imad — Wed, 20 Sep 2023 10:22:02 +0000

AI is the New Electricity.

It is a general-purpose technology that is applicable to all fields in equal terms. Just as Electricity brought transformation in every bit of industry, AI has started bringing that kind of transformation in the industry. There are opportunities along with some risks in AI and generative AI. Let’s talk about Andrew Ng’s perspective on this:

Andrew Ng’s Talk on Opportunities in AI

Andrew Ng, a Coursera co-founder, chief scientist at Baidu and prominent AI specialist, spoke on the opportunities in AI on July 26, 2023, at Stanford University. Ng discussed a wide range of issues in his discussion, including:

Foundational Concepts Behind Opportunities in AI

Andrew Ng first laid out the Foundational concepts behind the opportunities in AI.

Trends in AI technologies and tools

Ng began his presentation by delving headlong into the ever-changing field of artificial intelligence. He emphasised how swiftly AI has advanced in the past few years(2010–2020), with crucial developments coming left and right. Consider it an epidemic of growth. New machine learning algorithms, oceans of data to explore, and the rise of deep learning — it’s as if AI has discovered its purpose.

Supervised learning and generative AI:

If artificial intelligence were an Avengers team, supervised learning and generative AI would be Iron Man and Captain America. Ng revealed some of their superpowers. Supervised learning teaches robots to perform amazing things like recognising animals in photographs and comprehending your voice instructions. One main example Ng used of restaurant review sentiment analysis. In contrast, generative AI builds things out of thin air. Consider it conjuring up images of art, music, or even complete novels. But the amazing thing is that generative AI uses Supervised machine learning.

The adoption of AI

Ng’s presentation was not just about futuristic technology; he anchored it in reality. AI is no longer the stuff of science fiction. It’s in healthcare, banking, manufacturing, and pretty much everywhere else.

However, there is a catch. We need more talented AI mages and certain spellcasting rules if AI is to truly perform its magic. Just like Google, Meta and Amazon made their way into people’s daily lives. But, the rate at which people are adopting AI whether it is in supervised learning, generative AI, Unsupervised or reinforcement learning, will double, triple or quadruple in the next three years. But, it all depends on investments from venture capitalists, and investment firms to bid on the AI which is a really smart thing to do at this time.

Opportunities in AI

Ng identified a number of opportunities in AI, including:

AI for productivity

Many jobs that are presently handled by people can be automated with AI, freeing up workers to focus on more creative and strategic work. Large Language Models (LLMs) are great but people are not taking benefit from it as much as they should. What Andrew meant was, that people need to use these language models more for developing things that is, use LLMs as a developer tool like custom AI applications.

AI for new products and services

AI may be utilised to build previously unimaginable services and products. AI may be used to create more efficient self-driving cars, personalised medical procedures, and new kinds of entertainment. Ng mentioned, that there will be fads along the way. Some products or services will shoot to the sky in no amount of time but they will diminish as soon as they reach their limits. It happened for the iPhone Flash App, iPhone integrated the Flash and Kaboom the app went out of business.

So, we do not have to worry about the apps rolling in and out but have to keep our focus on making new features available for the greater good of people and businesses(Both do not seem obvious but it is possible).

AI for social good

AI has the potential to address some of the world’s most urgent issues, including climate change and poverty. AI, for example, may be utilised to produce more efficient energy systems and new educational possibilities. The reality is, that AI has not been widely adopted yet for the greater good.

For this, developers need to use AI technology as a tool to solve problems in a unique way. Unique way I say, Tail-end problems that do not seem too big for the businesses in narrow niches like Food Inspection, material grading and crop yield enhancement etc.

Process for Building Startups

Ng also spoke on the process of creating AI enterprises. He emphasised the necessity of starting with a clear problem to solve, and he recommended entrepreneurs to focus on creating goods that people value. He also emphasised the significance of assembling a good team and soliciting user input early and frequently.

According to Andrew Ng, Companies or startups have to integrate AI into the existing businesses for them to flourish and invest more in the AI field which would eventually maximize the rate of development in the field of AI.

At the hardware level requirement for AI applications, Capital Investments needed to be made to compete against the giants of the market like NVIDIA, Intel and AMD. For the Infrastructure level, It is also capital-intensive and concentrated as it is against giants like Google Cloud and AWS. Alongside the Infrastructure, there is the developer level, which is again hyper-competitive, startups can go there but it is better to go to the next level which is really picking the niches.

The next layer I say, is the Application Level, which has enormous potential for building successful businesses, whether that is a SaaS or a combination of a Specific domain like romance, business advice and AI.

AI Risks and Social Impact

Ng also discussed the potential risks of AI, such as the risk of job displacement and the risk of AI being used for malicious purposes. He argued that it is important to develop ethical guidelines for the use of AI and to invest in research on AI safety.

Also, AI today still has some problems with bias, fairness and accuracy but it is changing with the development. In the case of job displacement, higher-wage jobs are being disrupted due to AI technology in contrast to the previous revolution of automotive where lower-wage jobs were disrupted.

Therefore, companies, corporations and governments need to make sure that people whose jobs are displaced should be taken care of. Also, there should be a more efficient effort in stopping AI use for illegal purposes.

Key Notes from the Lecture:

Here is a more conversational expansion of some of the key points from Ng’s talk:

AI is transforming the world

AI is already having a significant influence on our lives, and its impact will only expand in the coming years. Artificial intelligence will be utilized to create novel products and services, automate jobs, and tackle some of the world’s most serious issues. But, there is still time or even decades where one can say AI can do anything a human can do.

AI is a powerful tool, but it is important to use it responsibly

AI is a great tool, but it must be used wisely. We must set ethical rules for the use of AI and invest in AI safety research. AI gradually develops itself as well, but the scenario of AI becoming a Superintelligent system and taking over the human race is not factually realistic for now. But human manipulation is making AI a scary thing for the common folks.

Ng’s presentation provided an intriguing look into the future of artificial intelligence. He expressed explicitly that AI has the ability to improve the world, but he also emphasised the significance of utilising AI responsibly for a better future.

Here are some additional thoughts on the opportunities in AI:

AI is still in its early stages of development: AI is still a relatively new technology, and there is still a lot of room for growth. This means that there are many opportunities for entrepreneurs to create new AI-powered products and services.
AI is becoming more accessible: AI is becoming more accessible to businesses and individuals of all sizes. This is due to the development of cloud-based AI platforms and the increasing availability of open-source AI tools.
AI is being used in a wide range of industries: AI is being used in a wide range of industries, including healthcare, finance, manufacturing, and retail. This means that there are many opportunities for entrepreneurs to apply AI to solve problems in new and innovative ways.

Final Thoughts

If you want to learn more about AI, I recommend watching Ng’s presentation or reading his blog entries and papers. AI is a strong technology with the ability to positively change the world, and it is critical to be aware of its capabilities.

Best Portfolio Projects for Data Science

Imad — Tue, 19 Sep 2023 11:44:48 +0000

Best Portfolio Projects for Data Science

“How can I showcase my data skills to the world?” you may be asking. Fear not, for the solution lies in assembling a sparkling portfolio of data science projects!

A fantastic portfolio is more than simply a showcase of your abilities; it’s your golden ticket to the data-driven paradise or Job where chances abound at every angle. You will be given a treasure trove of the top data science portfolio projects that will have you saying, “Whoa, I can do that too!”

In this Article, All mentioned projects will be your resume or portfolio projects from beginner to advanced level. Along with this, three will be dataset sources and also, how you will approach that project to accomplish and add it to your portfolio.

So, whether you are a novice or a skilled professional working in data science, these projects can take you from a good data scientist to a seasoned pro. Without any wait, let’s get started.

Here is a list of data science portfolio projects from beginner to advanced:

Beginner Portfolio Projects for Data Scientists

Exploratory Data Analysis (EDA):

Methodology: Analyse and find significant insights from a public dataset of your choice. You are free to use whichever programming language but Python is recommended or the data visualisation tool you like. You might, for example, use Airbnb information to discover the most popular cities and neighbourhoods, as well as the most profitable rental units.

Resources for the Airbnb Dataset Project

Market basket analysis:

Methodology: Analyse a collection of client transactions to detect trends in consumer behaviour. This may be used to provide product suggestions, enhance shop layouts, and launch other marketing campaigns. For instance, you might examine a grocery shop dataset to see which goods are frequently purchased together.

Resources for Analysis

Predictive Modeling with Linear Regression:

Methodology: Select a dataset with a defined goal variable, such as property prices or vehicle MPG. Using packages such as Scikit-Learn, create a basic linear regression model. Document and visualise the outcomes of your model’s performance.

Resources for USA Housing

Dataset: USA Housing
Scikit-Learn Documentation

Intermediate Portfolio Projects for Data Scientists

Machine learning classification:

Methodology: Train a machine learning model to categorise data into distinct groups. For example, you may train a model using Scikit-Learn to determine if emails are spam or not, or whether photographs contain cats or dogs.

Resources for Image Classification

Machine learning regression:

Methodology: To forecast a continuous value, train a machine learning model. For example, you might train a model to predict secondary school student performance using Regression or the number of people who would visit a business on a particular day.

Resources for Student Performance Prediction

Machine Learning Clustering:

Methodology: Machine learning is often used to find groupings of similar data points. Customers, for example, may be clustered based on their purchasing history, or items could be clustered based on their attributes.

Resources for Retail Purchasing

Advanced Portfolio Projects for Data Scientists

Natural language processing (NLP):

Methodology: Create an NLP model to handle tasks like text summarization, machine translation, and question answering. You could, for example, create a model to summarise news stories or transcribe text from one particular language to another.

Resources for the NLP Model

Deep learning:

Methodology: Create a deep learning model to handle tasks like image identification, object detection, and speech recognition. For example, you might create a model to recognise things in photos or to convert voice to text.

Resources for Converting Voice to Text

Recommender systems:

Methodology: Build a recommender system to help consumers find products, films, music, and other objects. You might, for example, create a recommender system like a streaming site or an e-commerce website.

Resources for Movie Recommendation

Final thoughts

When choosing a project, it is important to consider your skills and experience level. You should also choose a project that you are interested in and that you are motivated to complete. Therefore, you are free to find some other datasets for your desired field or interest.

Here are some additional tips for creating a strong data science portfolio:

Focus on the impact of your work. What problem did you solve? How did your work benefit the users or the business?
Document your process. Include a brief description of your project, the data you used, the methods you used, and the results you obtained.
Publish your code. This will allow potential employers to see your coding skills and how you approach data science problems.
Present your work clearly and concisely. Use data visualizations and other storytelling techniques to communicate your findings to a non-technical audience.

By following these tips, you can create a data science portfolio that will showcase your skills and experience, and help you land your dream job.

Hurrah, You read it all.

You deserve a gift of Data Science Mastery Course Outline. Feel Free to get this PDF at No Cost Here.

Happy Learning!

Think twice before becoming Data Scientist

Imad — Mon, 18 Sep 2023 11:50:14 +0000

“All that glitters is not gold; often have you heard that told. Many a man his life hath sold, but my outside to behold.” — William Shakespeare

You know how they say data science is like a universe of infinite possibilities, with every problem simply waiting for a data-driven superhero to swoop in and rescue the day? That is somewhat correct, but here’s the deal: it isn’t always as glamorous as it seems.

But check out the reality: it's not all glitzy models and miraculous algorithms. In our reality, data isn’t always perfect, models don’t always behave as intended, and not every project ends triumphantly.

Fear not, It is not a gloom and doom. We are humans who have coded algorithms to go on Mars, the Generative AI’s, and certainly, there is a solution for our problems even though we have created them for ourselves: Evolution ‘huh’.

what is discussed here:

Data Quality Issues
Overhyped Expectations
Continuous Learning
Data Privacy and Ethics
Documentation
Project Failures
Imposter Syndrome
Model Deployment and Maintenance

Harsh Reality of Data Science

So, without going anywhere else, let’s get to the point and get you the reality checks and solutions for them.

1. Data Quality Issues

Garbage in, Garbage out. Due to insufficient, incorrect or inconsistent data sources, data scientists frequently spend a substantial amount of effort cleaning and preparing data. Most of the time data is duplicated, biased, and outdated. Tackling data quality concerns is critical in data science since the precision and dependability of modelling and conclusions created from that data are directly affected by the level of quality of the input data.

Solution: Spend time preparing and cleaning data to guarantee correctness and completeness. To optimise data pipelines, establish data quality standards and engage with data engineers.

2. Overhyped Expectations

Data science is frequently overhyped, leading to excessive expectations. Data cannot solve every problem, and not every data-driven endeavour will provide spectacular outcomes as there is no guarantee of definite accuracy and monetization from data-driven Predictions.

Solution: Communicate with stakeholders on what data science can and cannot do. Project schedules and deliverables should be reasonable. Concentrate on small steps forward.

3. Continuous Learning:

Data science is an ever-changing discipline. Data scientists must commit to lifelong learning and staying current with new tools, methodologies, and technologies in order to remain relevant. Today, Some Algorithms are hyped like LLM(Large Language Models) may or may not be used as they are often upgraded, refined and improved.

Solution: Make time for continuous learning and professional growth. Attend seminars and keep up with industry developments. Choose a group of peers that make you learn new things.

4. Data Privacy and Ethics:

A double-edged sword without any doubt. Data privacy and ethical considerations grow increasingly important as data gets more lucrative, data is the currency of businesses. Navigating these challenges may be difficult. Sometimes data is extremely sensitive as it may hold people’s bank records, personal thoughts etc.

Solution: Stay up to date on data privacy rules and ethical norms. Use strong data confidentiality and encryption techniques. Keep stakeholders up-to-date on ethical issues.

5. Documentation:

Keeping up with code, models, and research and ensuring they are replicable and well-documented takes work, but it is vital for sustaining transparency and cooperation. What dependencies, experiments, results and Ethical Considerations have been used before and during the project?

Solution: Utilise version control systems and tools for documentation as a solution. Keep detailed records of all code, demonstrations, and model versions. To maintain openness, work closely with team members.

6. Project Failures:

Not every single data science project is a success. Certain might fail to deliver substantial outcomes, while some may be abandoned for a variety of reasons, including shifting corporate goals, Inaccurate Models, Data Quality Issues, Failure to meet deadlines and Insufficient resources.

Solution: Adopt an attitude of exploration and failure learning. Before committing significantly, do extensive project feasibility studies. Constantly assess the project’s growth and make necessary adjustments. Furthermore, Invest in proper project planning, stakeholder engagement, data quality assurance, and project management practices to reduce project failure risk.

7. Impostor Syndrome:

A lot of data scientists, including experienced ones, suffer from imposter syndrome, which makes them feel as though they don’t fully belong or aren’t as good as they should be. They downplay their achievements, overwork and seek validation constantly because of the feeling of not knowing everything like in web or software development fields.

**Solution: **Recognise that imposter syndrome is prevalent in data science, even in qualified data science individuals. Therefore, Seek out mentoring and support from your peers, if that can make you feel confident. Individuals suffering from imposter syndrome might benefit from developing self-confidence and adopting a growth mindset in order to prosper in their employment. Celebrate your accomplishments and recognise the skills you possess.

8. Model Deployment and Maintenance:

Building a model is only the first step. Model deployment and maintenance in a production setting can be complicated and difficult as it involves Hyperparameter tuning, data drifting, model updation, compliance changes and security concerns. The lifecycle management of models can be intricate and often overlooked due to its cumbersome work.

Solution: Create strong deployment pipelines and evaluation mechanisms. DevOps practices should be used to streamline model deployment and upgrades. Prioritise documentation for upkeep. Make data more refined for future endeavours, take enhanced security measures, regularly collaborate with stakeholders and use risk mitigation.

Final Thoughts:

Regardless of these obstacles, data science is a lucrative and prominent discipline. Confronting these hard truths, according to several data scientists, is part of what makes the field both vital and intriguing. Data scientists may continue to contribute meaningful contributions to their organisations and society as a whole by recognising and overcoming these issues.

Don’t forget to follow. If You are still persistent about the data science field. Congratulations, I have got some presents for you. Here is the Ultimate guide for data science books for book wizards, otherwise, for comprehensive Roadmap and resources: 7-Stage Roadmap for Data Science. To know about soft skills in Data Science.

Happy Learning!

The Secret Sauce of Success: Soft Skills Every data Scientist needs

Imad — Fri, 15 Sep 2023 06:47:50 +0000

The Secret Sauce of Success: Soft Skills Every data Scientist needs

Soft skills are like the operating system, while hard skills are the apps.

Hello, future data wizard! Data Scientists often get caught in the hypnotic maze of numbers, equations, and algorithms, there’s something you should know. It’s not only about crunching numbers; it’s also about artistry, which takes the shape of soft skills.

Soft skills take a competent data scientist to a rockstar in the area. So, grab your favourite data-themed beverage, take a seat, and let’s take a look at what it takes to excel in the field of data science.

But first thing first, why do we need it?

STAKEHOLDERS! “Woah”

Who are the Stakeholders?

Individuals, groups, or institutions with a vested interest in the results, insights, or choices that arise from data analysis and data-driven initiatives are referred to as stakeholders in data science. These stakeholders have a variety of roles and responsibilities inside an organization, and their participation is essential for the success of data science activities.

Here are some common types of stakeholders in data science:

Business Executives and Leaders: Senior executives such as CEOs, CTOs, and department heads are frequently the major stakeholders. They establish the strategic goals and priorities for data-driven initiatives and rely on data scientists to give decision-making insights.
Data Science Teams: Stakeholders include data scientists, machine learning engineers, data engineers, and other data specialists inside the organisation. They work together to complete projects, exchange ideas, and achieve project objectives.
Domain Experts: Domain experts are subject matter experts who have an extensive understanding of the industry or a specific topic important to the project. Their knowledge aids in the definition of project objectives and the interpretation of findings in a relevant context.
Business Analysts: Data Analysts play a critical role in transforming data-driven insights into meaningful suggestions. They collaborate closely with data scientists to bridge the technical analysis and business choices divide.
IT and data management teams: These are in charge of data infrastructure, governance, and security. They are responsible for guaranteeing data availability, quality, and regulatory compliance, making them critical stakeholders.
End Users: The ultimate benefactors of data science initiatives are frequently the end users of data-driven solutions or reports, such as marketing teams, sales teams, or customer service professionals. Their input and requirements are important to the project’s success.
Regulatory and Compliance Authorities: Regulatory bodies may be stakeholders in regulated businesses (e.g., healthcare, finance), as data science initiatives must conform to legal and ethical norms.
Customers and Clients: In certain circumstances, an organization’s customers or clients may profit indirectly from data science activities, particularly if they result in enhanced goods or services.
Investors and Shareholders: Investors and shareholders in publicly listed firms may be interested in data-driven choices that affect the company’s financial performance.
External Consultants or Data Science Partners: Organisations may work with external consultants, suppliers, or partners who bring data science knowledge and become project stakeholders.

Beyond Numbers and Algorithms: Soft Skills in Data Science

The Unsung Heroes of data science are soft skills. These Intangible qualities are the secret sauce that transforms a good data scientist into a great one. Here are the top-notch soft skills that are essential for data scientists to thrive in their careers.

Problem-Solving: The Ultimate Data Puzzle

Data science is the ultimate puzzle-solving game. Data scientists are the digital equivalent of Sherlock Holmes. They like analysing complicated problems, connecting the connections, and uncovering hidden insights in data.

Assume you’re a detective with a Business Executive as your primary stakeholder. They are similar to a police chief, and they want you to solve a complicated case (data problem) for the corporation. As you acquire clues (data), connect the dots, and give a clear answer, your problem-solving abilities come into play.

Critical Thinking: Data Detective

Data isn’t always cooperative. It’s like a devious mystery, and you must be the detective who solves it. Critical thinking serves as a magnifying glass, allowing you to see biases, limits, and flaws in your analysis.

Consider the following scenario: you are working with a Domain Expert who is well-versed in the business. They serve as your trusted guide through a vast jungle of data. Your critical thinking abilities assist you in navigating the thicket of knowledge and avoiding becoming lost in the woods.

3. Curiosity: The Spark of Discovery

Curiosity not only killed the cat, but it also transformed ordinary facts into astonishing discoveries. Data scientists are inherently inquisitive individuals who are constantly eager to investigate, examine, and find the hidden jewels hiding inside the data mines.

Imagine your Data Science Team colleagues. They are your data adventure companions. Curiosity serves as your collective compass, fueling your collective drive to explore new data regions, unearth hidden insights, and continually improve your approaches.

4. Communication Skills: Data Storytelling

You’ve solved the riddles of data, and now it’s time to share your findings with the rest of the world. Data scientists are storytellers; they spin engaging yarns with data as ink and insights as plot twists.

For Example, You’re in a meeting with Business Analysts, who serve as the link between your technological and business worlds. Your diplomatic passport is your ability to communicate effectively. You interpret sophisticated data jargon into their common language, ensuring that everyone is on the same page and that is a big win for everyone especially for you.

5. Teamwork: Data Collaboration

Data science is a collaborative endeavour. You’re not alone on your voyage; you’re accompanied by engineers, domain experts, and business gurus. Your hidden weapons are being a team member, sharing your thoughts, and speaking the language of teamwork.

Your IT and Data Management Teams are your technological guardians, ensuring that data flows smoothly. Consider them your tech sidekicks. The dream is made possible via collaboration. You work together to keep the data fortress safe.

6. Adaptability: The Shape-Shifter

Things move quicker in the digital world than you can say “algorithm.” New tools, methods, and fashions sprout like mushrooms after a rain. To remain ahead of the game, you must be as versatile as a chameleon(Not in a bad way).

Just as a chameleon, seamlessly blending in with your End User team. They are the true heroes on the front lines, utilising your data insights to solve business problems. Being flexible guarantees that your solutions fit perfectly and solve their specific needs.

7. Time Management: The Juggler

Data scientists excel at multitasking. You must juggle many tasks and deadlines like a circus act.

This seems like a heinous act but sometimes we have to push and manage beyond our measures. It all comes down to prioritising things, meeting deadlines, and making every second count.

When you multitask like a champ, juggling various tasks while your Regulatory and Compliance Authorities keep an eye on you(How dare they do that!). Time management is your superpower, allowing you to fulfil deadlines and keep everything in order.

8. Ethical Considerations: The Data Guardian

Data is about people as well as statistics. Data scientists are the digital realm’s stewards of ethics. You negotiate the perilous seas of privacy and bias to ensure that data is treated with dignity and justice.

As an advocate of ethics, you guarantee that your data solutions are spotless, satisfying even the most demanding regulators. When working with sensitive data(Like analyzing domestic violence), your ethical radar ensures that you obey the regulations.

9. Business Acumen: Beyond the Numbers

Data is about more than simply data; it is also about business. Understanding the larger company environment and goals is critical. Your recommendations are not just data-driven, but also business-savvy.

Business Acumen is your reliable partner that assists you in understanding the larger business context and speaking the language of your Business Executives. It’s similar to having a secret code to open a treasure box of business chances.

10. Resilience: The Data Warrior

Data science is an adventure, and voyages are not always easy. You’ll face difficulties, storms, and undiscovered territory. Resilience serves as your armour, allowing you to weather setbacks, learn from mistakes, and emerge stronger.

Final Thoughts:

So, in the realm of data science, soft skills aren’t simply extras; they’re your trusted companions, guiding you through the diverse combination of stakeholders and ensuring your data quests are fruitful! These Soft skills are the secret source that will make you a true data sorcerer.

Having said that! Here is the Complete Roadmap for your data science journey.7-Stage Roadmap for Data Science (With Courses), If you are a Book Wizard: Ultimate Guide: Books for Data Science.
*Don't forget to Follow and react! *

Now, go and conquer the data universe!

7-Stage Roadmap for Data Science

Imad — Wed, 13 Sep 2023 11:40:15 +0000

7-Stage Roadmap for Data Science

A comprehensive map with Complete Resouces

One only needs a Road and will to move on it. (Unknown)

Are you eager to start a transformational journey and unleash the wonder of data? If yes, buckle up because we’re about to start the Full Stack Data Science Roadmap, where each project is a problem that has to be overcome and every stage serves as a stepping stone.

But If you are a book wizard, **Here** is my guide to Data Science Books for You! I have covered all the books (With Individual Ratings on different metrics) needed for Data Science from Beginner to advanced levels.

Here are the Topics I will cover in this Post:

What is Data Science?
Data Science vs. ML Engineer vs. Data Engineer
What does a Data Scientist Do?
The Data Science Project Lifecycle
7 Stage Roadmap for Data Scientist with courses and books

Having said that! Let’s deep dive into our Data Science Roadmap.

What is a Data Science?

Data science is a superpower for comprehending information. It all revolves around the use of computers and specialised knowledge to make sense of data, which is just a tonne of information. Consider data as a huge puzzle with parts all over the place. Data scientists are similar to puzzle solvers. To view the broader picture, they take the bits (of data), clean them up, and merge them. To uncover hidden patterns and solutions, they employ mathematical and computational methods.

Simply, data science is the art of finding valuable insights using Statistics, manipulation, visualization and deep learning model creation on the given or extracted data.

Supercool!

Data Science vs. ML Engineer vs. Data Engineer

Three unique professions within the data and analytics industry are data science, machine learning (ML) engineer, and data engineering, each with its emphasis and duties.

A Data Engineer focuses on maintaining data pipelines, data warehouses and data lakes, ensuring data quality and reliability. ML Engineer builds and optimizes machine learning models, integrates them into applications and ensures their production efficiency. Data Scientist performs exploratory data analysis, develop and apply machine learning algorithms and predict decisions based on their findings.

What does a Data Scientist Do?

Data Scientists should have a clear idea of what their responsibilities are.

So, Let’s take an Example project which will explain all of these roles:

Project: Customer Churn Prediction for a Telecommunication Company

The data Engineer sets up the data infrastructure and Extract-Transform-Load (ETL) data from different sources, the ML Engineer builds and deploys the predictive model to make real-time predictions and apply feature engineering to enhance model performance, and the Data Scientist leverages the model’s output to provides actionable recommendations and strategies for retaining customers.

These roles collaborate to create a comprehensive solution that addresses the business problem of reducing customer churn for the telecommunication company.

The Data Science Project Lifecycle

The data science project lifecycle is an organised procedure that data scientists use to develop, generate, and deploy data-driven solutions. It consists of several steps and tasks that assist organisations in extracting insights from data to make educated decisions. The specific processes vary based on the project and organisation, however below is a broad outline of the data science project lifecycle:

Data Preparation

Most of the time the data we extract or supply for our problem or project is not clean. Therefore, data cleaning and preprocessing are important before exploratory data analysis(EDA). EDA helps in understanding the data’s characteristics and identifying potential relationships between variables.

Model building

Data scientists build algorithmic models using clean data. While building a model, start with simple algorithms or models like Regression then try complex models such as Neural Networks. Assess the model performance using evaluation metrics specific to the problem such as F1, RMSE etc.

Data Insights

To acquire insights into the situation, interpret the model’s predictions and feature relevance. Data visualisation and clear explanations should be used to communicate findings to stakeholders.
The whole project should be documented, including data sources, preprocessing methods, model information, and findings. Make detailed reports or presentations for stakeholders.

Model Monitoring

The practice of continually following and analysing the performance of machine learning models deployed in a production setting is known as model monitoring in data science. It entails tracking how effectively the model performs over time, recognising any flaws or deviations from predicted behaviour, and taking appropriate remedial steps. Model monitoring is critical for ensuring that machine learning models retain their accuracy and dependability when they meet fresh data in real-world settings.

7-Stage Roadmap for Data Science

Data Science is a rigorous field but rewards are also amazing!

A person should choose hard ways to test his conscious and unconscious limits.

The stages in this roadmap are organised in logical succession to help newbies become skilled data scientists while taking into account the complexity and interconnection of the skills and knowledge areas involved.

Stage 1: The Foundation

This level focuses on creating a firm foundation by understanding core mathematical principles and obtaining programming expertise, both of which are required for data science.

Mathematics Fundamentals:

Resource: “Linear Algebra” by Gilbert Strang (Book) and Khan Academy (Online Course).
Resource: “Calculus” by James Stewart (Book) and MIT OpenCourseWare (Online Course).
Resource: “Introduction to Probability” by Joseph K. Blitzstein and Jessica Hwang (Book) and it is also available as course on edx.

2. *Programming Proficiency:*

Resource: “Python for Data Analysis” by Wes McKinney (Book).
Resource: “Python Programming for Beginners” on Coursera (Online Course).
Resource: Corey Schafer’s Python YouTube channel for tutorials.

3. *Data Handling and Exploration:*

Resource: “Data Science for Business” by Foster Provost and Tom Fawcett (Book).
Resource: Kaggle’s “Intro to Data Analysis” (Online Course).
Resource: Data School’s YouTube channel for pandas tutorials.

Stage 2: Data Wrangling

After Foundation there is data wrangling since it is critical to clean, preprocess, and manage data correctly before using machine learning algorithms. SQL and database abilities are covered in this section since they are widely utilised in data retrieval and storage.

Data Cleaning:

Resource: “Python for Data Cleaning” by Kevin Markham (YouTube Playlist).
Resource: “Data Wrangling with pandas” on DataCamp (Online Course).

2. *SQL and Databases:*

Resource: “SQL for Data Science” on Coursera (Online Course).
Resource: Mode Analytics SQL Tutorial (Online Resource).
Resource: Codecademy’s SQL course (Online Course).

Stage 3: Machine Learning Foundations

After establishing a solid understanding of data processing, students dig into the fundamental concepts of machine learning. Starting with the fundamentals of supervised and unsupervised learning, this level provides the foundation for more sophisticated machine-learning approaches.

Resource: “Introduction to Machine Learning with Python” by Andreas C. Müller & Sarah Guido (Book).
Resource: Andrew Ng’s Machine Learning Course on Coursera (Online Course).
Model Evaluation and Metrics:

Resource: “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron (Book).
Resource: “Machine Learning” by Stanford University on Coursera (Online Course).

Stage 4: Advanced Machine Learning(Deep Learning)

This level immerses students in machine learning, especially deep learning. It comes after the foundational machine learning stage to ensure that learners have a firm grasp on the fundamentals before moving on to more advanced topics.

Resource: “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (Book).
Resource: Fast.ai’s Deep Learning for Coders course (Online Course).
Resource: Stanford University’s CS231n course on Convolutional Neural Networks (Online Course).
Model Tuning and Optimization:

Resource: “Practical Machine Learning for Computer Vision” on Coursera (Online Course).
Resource: Sebastian Raschka’s YouTube channel for machine learning tips.

Stage 5: Data Visualization and Communication

To bridge the gap between data analysis and communicating insights to stakeholders, effective data visualisation and communication skills are provided here. This stage improves the capacity to effectively convey findings.

Resource: “Storytelling with Data” by Cole Nussbaumer Knaflic (Book).
Resource: Datasaurus Rex’s YouTube channel for data visualization.
Communication Skills:

Resource: “Data Points” by Nathan Yau (Book).
Resource: DataCamp’s “Data Science Communication with Python” (Online Course).

Stage 6: Real-World Projects

It is critical to apply information in practical contexts after attaining a strong skill set. Real-world projects give a hands-on experience that reinforces and solidifies previously gained abilities.

Build Projects:

Apply your skills to real-world data science projects. Start with small projects and gradually work on more complex ones.
Resource: Kaggle (for datasets and competitions).
Resource: GitHub (for hosting and showcasing your projects).

2. Continuous Learning:

Stay updated with the latest trends and research in data science.
Resource: Blogs and forums like Towards Data Science (Medium), Data Science Stack Exchange, and Reddit’s r/datascience.
Resource: Subscribe to academic journals and publications in the field.

Stage 7: Networking and Career Development

Learners in this stage concentrate on professional development and career advancement. As people advance into data science professions, networking, job hunting, and specialisation become increasingly important.

Networking:

Attend data science meetups, conferences, and webinars both in-person and online.
Resource: Meetup.com (for finding local data science meetups).
Resource: LinkedIn (for connecting with professionals and joining data science groups).

2. Job Search:

Create a strong resume and LinkedIn profile highlighting your skills and projects.
Prepare for interviews by practising technical questions and behavioural interviews.
Resource: “Cracking the Data Science Interview” by Jake VanderPlas (Book).
Resource: Glassdoor and LinkedIn Jobs (for job search).

3. Advanced Specialization:

Consider specializing in areas like Natural Language Processing (NLP), Computer Vision, or Data Engineering based on your interests.
Resource: Specialized courses and books in your chosen domain.
Resource: Online forums and communities dedicated to your specialization.(AnalyticsVidhya)

4. Certifications:

Consider pursuing relevant certifications such as the Google Data Analytics Professional Certificate or Microsoft Certified: Azure Data Scientist Associate.
Resource: Coursera, edX, and Microsoft Learn offer certification programs.

Additional Skills

Final Thoughts

The stages are ordered sequentially, however, it is crucial to remember that learning is an iterative process. As they handle more sophisticated topics and tasks, learners may return to previous stages. Furthermore, continual learning, networking, and remaining motivated are continuing activities that operate concurrently with the other stages of a data scientist’s career.

That’s all, Thank you for reading. Hope you enjoyed learning, Don’t forget to Subscribe to my Newsletter **Here, and get the **DATA SCIENCE MASTERY COURSE OUTLINE.

Happy Learning!

Ultimate Guide: Best Books for Data Science with Ratings for All Levels

Imad — Fri, 08 Sep 2023 04:43:07 +0000

Ultimate Guide: Best Books for Data Science with Ratings for All Levels

The Data Science domain is dynamic and staying current is mandatory, Some indispensable books can make you up-to-date and expert whether you are just starting out or Pro as each book is assessed with its individual attributes with ratings(1 to 5 stars).

As you know, Data Science is a discipline that thrives on continuous learning and there is a vast sea of Book recommendations for Data science that encompasses the basics of Python to deep learning, but the books here are chosen due to their practicality, clarity of concepts and experience level (Beginner to Advance).

Understanding Subjects like Statistics, Machine Learning, Deep Learning and Neural Networks make a data scientist validated in the data science field, Learning them from proficient sources can make things/concepts/skills very easy. Therefore better to learn from the people who have mastered the domain with practical knowledge.

Having said that, if you still know some books that are useful for Data scientists, I encourage you to mention them in the comments, as many people love to learn from different sources to enhance their knowledge and skills. Also, if you have any other kind of recommendation regarding data science, feel free to share that as well.

So, whether you are new to the realm of Data Science or looking to polish your skills for advancement in your career, Below are the books that could be the perfect match for your Journey. Let’s Dive in:

Beginner Level:

This Level Includes all the basics that you need to get started with data science or advanced data analytics.

**“Python for Data Analysis” by Wes Mckinney**

Ratings: 4.5/5 (★★★★½)

“Python for Data Analysis” is a highly valued book for people getting started in data science or data analysis. Mckinney’s clear and concise explanations make it accessible for people with minimal programming expertise.

The book begins by introducing fundamental tools and libraries for data analysis in Python. One of the standout features is its emphasis on real-world applications, which makes some complex topics more digestible and approachable for readers.

Python for Data Analysis covers topics and tools like Numpy, Pandas and Matplotlib for visualization. In short, this book makes readers proficient in tackling real-world data by harnessing some insights from that data.

It is a gem for people getting started in data analytics, data science and even data engineering as it covers all the basics needed for these domains.

Attributes:

Clarity (5/5): This book excels in explaining complex concepts in a simple and clear manner.
Practicality (4/5): Offers hands-on examples and real-world use cases.
Coverage (4/5): Covers essential data manipulation and analysis techniques using Python.
Applicability (5/5): Ideal for beginners who want to start their journey with Python in data science.

2. “Python Data Science Handbook” by Jake VanderPlas

Ratings: 5/5 (★★★★★)

A trusty data science companion for beginners in the world of Python.

This book covers all the concepts of data analysis plus some basics of machine learning using Scikit-Learn. Jake VanderPlas make readers write the code with practical examples using Pandas for data wrangling, Numpy for efficient manipulation of ndarrays, Matplotlib for stunning visualizations and when you are ready then gives the reader the basic insights for machine learning and statistics using Scikit-Learn.

It truly gives the proper roadmap for moving forward in the Data Science world. VanderPlas not only teach you to code but also “why” you should code this, so it does not feel like copying from him.

So, even if you are a newbie or a pro, the Python Data Science Handbook will benefit you either way.

Attributes:

Clarity (5/5): Offers a clear and concise explanation of data science concepts using Python.
Practicality (5/5): Contains numerous practical examples and code snippets.
Coverage (5/5): Covers various data science topics, including data manipulation, visualization, and machine learning.
Applicability (5/5): Ideal for those who want to learn data science using Python.

3. “**Data Science for Business” by Foster Provost and Tom Fawcett**

Ratings: 4.5/5 (★★★★½)

A must-read for anyone who wants to get insights into businesses with a data-driven approach. Since Data Scientists often predict outcomes using Machine learning, Deep learning and Neural Networks, this book is a gold mine for learning Business problems with data science Solutions.

Data Science for Business is not about complex algorithms and technical jargon, it’s about understanding how data is manipulated/used to make smart business decisions. Foster and Tom break down, how to ask the right questions and use data to solve business queries.

The best part is that everything is explained in simple plain English and no complex formulas. It’s like having an interesting conversation with a data-savvy friend or colleague.

So whether you are a data scientist, business intelligence analyst or just curious about the businesses that drive themselves using the data-centric approach.

Attributes:

Relevance (5/5): Focused on the business aspect of data science.
Clarity (4/5): Provides a clear understanding of the fundamental concepts.
Real-world Examples (5/5): Features case studies and practical scenarios.
Beginner-friendly (4/5): Great for those new to the field.

Intermediate Level

At the intermediate level, You need to cover most of the Statistics and Machine Learning.

4. “Essential Math for Data Science” by Thomas Nield

Ratings: 5/5 (★★★★★)

Math is to data scientists as Kryptonite is to Superman.

But worry not, you already have the solution from Thomas Nield in the form of “Essential Math for Data Science”. This book is like a math tutor for data scientists which breaks those headache-inducing concepts into a bite-sized, digestible food that you can chew easily.

Nield makes your math skills proficient by diving into Linear Algebra, calculus, probability and statistics with plain English, and practical real-world examples that make the math come alive.

So conquer the power of data by learning the wilderness of Essential Math Skills for Data Science.

Attributes:

Clarity (5/5): Thomas Nield’s “Essential Math for Data Science” is a beacon of clarity, making complex mathematical concepts crystal clear.
Practicality (5/5): It focuses on the math that’s directly applicable to data science, ensuring you learn what truly matters.
Engaging (5/5): Nield’s engaging writing style turns math into an enjoyable journey, not a daunting task.
Hands-On (5/5): Packed with practical examples and exercises, it ensures you can apply what you’ve learned.

5. “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron

Ratings: 5/5 (★★★★★)

Consider it a friendly mentor, who walks you through the garden of Machine Learning from start to end. You will start with foundations like Linear regression and decision trees, and then you will venture into deep learning with TensorFlow and Keras.

The good thing is, it is not all theory as there are hands-on coding examples to get your hands dirty. This book is clear, concise and loaded with real-world insights as if someone is guiding you like he has been there, done that and more.

Therefore, If you want to dive into Machine Learning first time or the 10th time to test your capabilities, this book will certainly be helpful to you every time as it puts your knowledge to work bit by bit.

Hands-on (5/5): Provides practical exercises and coding examples.
Up-to-date (5/5): Covers the latest machine learning libraries.
Conceptual Clarity (5/5): Explains complex topics in an approachable way.
Comprehensive (4/5): Suitable for those looking to deepen their machine-learning skills.

6. “Machine Learning Yearning” by Andrew NG

Ratings: 5/5 (★★★★★)

Andrew NG is the Mastermind behind Coursera’s machine learning course. So, you can have an idea, this book is going to be your personal trainer for Machine Learning.

Andrew’s book is a treasure trove for machine learning enthusiasts who want practical advice and insights from one of the brightest minds in the field. Ng dives deep into building machine learning algorithms that are related to real-world applications by whispering the secrets of Machine learning in the ear.

You will learn from how to set goals for ML projects to debugging and fine-tuning, just like a GPS that guides you through a Journey to make sure you are on the right path.

To take your machine learning skills to the next level, Machine Learning Yearning is a must-have in your knowledge.

Attributes:

Expert Insights (5/5): Written by one of the pioneers of machine learning.
Practical Guidance (5/5): Offers practical advice for building and deploying machine learning systems.
Project Focus (5/5): Emphasizes project management and decision-making in machine learning.
Up-to-date (5/5): Covers the latest real-world applications of machine learning.

Advance level

Books at this level need to be Bibles of Data Science so let’s get started:

7. “Deep Learning” by Yoshua Bengio, Ian Goodfellow, and Aaron Courville

Ratings: 5/5 (★★★★★)

This book is authored by Titans of Deep Learning — Bengio, Goodfellow and Courville. They take you onto the mysteries of Deep Neural Networks by teaching you the basics of neural networks to the most cutting-edge techniques of deep learning.

They cover it all: convolution, recurrent, feedforward deep networks and more. But the amazing thing about this book is, that all of the concepts are explained in a very warm and effective manner that makes everything clear as a crystal ball.

Without any doubt “Deep Learning” is your neural enlightenment to unravel the power of neural networks and deep learning techniques.

Attributes:

Expert-level Content (5/5): People looking to master deep learning.
Comprehensive (5/5): Covers the entire spectrum of deep learning.
Theory and Practice (5/5): Balances mathematical hardness with practical implementation.
Challenging (5/5): Ideal for already experienced data scientists seeking cutting-edge knowledge.

8. “Pattern Recognition and Machine Learning” by Christopher M. Bishop

Ratings: 5/5 (★★★★★)

Beware, This is not for the faint-hearted.

This is the book that can make you a data detective as it makes you a pioneer in pattern recognition and advanced machine learning.

Bishop takes you into the multiverse of algorithms and models that make machines smart.

It is the wilderness of being a human that make us smarter than machines.

You will explore topics like Bayesian networks, Support vector machines and neural networks. Still, it feels like chatting with a brilliant friend who is making it all understandable.

So, this book could be a secret weapon of yours in your journey of data science which will help you understand what works behind the scenes to uncover the patterns of data and predictions.

Attributes:

Advanced Topics (5/5): Delves into advanced machine learning and pattern recognition.
Mathematical Rigor (5/5): Requires a strong mathematical background.
In-depth (5/5): A reference for those wanting to understand the intricacies of ML and pattern recognition.
Challenging Exercises (5/5): Not for the faint-hearted, but immensely rewarding.

These books are considered most profound at their levels for all aspiring and pro data scientists. The books mentioned above not only the theoretical aspects of data science, machine learning, neural networks and deep learning but also the practical aspects.

Moreover, if you need a complete course outline for your data science journey, it will also be published soon.

More is coming for you so follow Data Scian and clap if you have made it this far.

Forem: Imad

Ultimate Guide: Statistics for Data Science(Beginners to Advanced)

What is Statistics?

Role of Statistics in Data Science

Why Should You Ace Statistics?

Statistics in Data Science Project Lifecycle

Defining the Problem

Exploring the Data

Data Cleaning and Preprocessing

Model Selection and Evaluation

Building and Fine-Tuning Model

Complete the Statistics course outline for All levels (Beginners to Advanced)

Beginner Level

Intermediate Level

Advanced Level

Practical Learning Tips for Statistics

Top-Down Method

Bottom-Up Method

Learning Resouces

Books

Courses

Final Thoughts

You need to know the DATA in Data Science

Qualitative Data

Types of Qualitative Data

Nominal Data

Ordinal Data

Quantitative Data

Types of Quantitative Data

Discrete Numerical Data

Continuous Numerical Data

Benefits of Classifying the Data Types in Data Science

Real-World Examples of Data Types Usage

Final thoughts

Generative AI and its Economy Impact (Realistic Answers)

Positive Side of Generative AI with real-world examples

Accelerating Innovation

Improving Efficiency

Sea of Amazing Products

Negative Side of Generative AI

Job Displacement

Economic Inequality

Horrors of War and Misinformation

Steps to Improve the Negative Impact of Generative AI

Factors That Limit Generative AI Economic Impact

Final Thoughts

Why You Should Not Become a Data Scientist?

The answer is divided into 3 Parts

Reasons not to Become a Data Scientist

Technical skills

Math and Statistics:

Coding:

Constant learning:

Soft skills

Communication:

Team Work:

Wizard:

Naturalistic Preferences

Work-life Balancer:

Not Coolheaded:

Competitive:

Final thoughts

Evolution of Applied Data Scientists in Data Science

Table of Contents

Who is an Applied Scientist?

Skills Needed for Applied Scientist

Tools and technologies

What does an Applied Scientist do?

Difference Between Applied Scientist and Data Scientist

Examples of Work Differences

Final thoughts

Opportunities in AI by Andrew Ng

Andrew Ng’s Talk on Opportunities in AI

Foundational Concepts Behind Opportunities in AI

Trends in AI technologies and tools

Supervised learning and generative AI:

The adoption of AI

Opportunities in AI

AI for productivity

AI for new products and services