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The Ultimate Guide to Data Analytics

Muthoni, Rogers — Sun, 25 Aug 2024 15:39:01 +0000

Intro’

Did you know that 90% of the world’s data was generated in the last two years alone? That is to say, approximately 402 million terabytes of data are created every day. The world is literally drowning in data. In the context of data, “created” means data that has been generated, copied, consumed, or captured. Individuals or organizations who will be able to make sense of this data will (without a doubt) hold the key to the future.

As a result of the growing need to make sense of data, Data Analytics has emerged as a key field enabling individuals and/or organizations to convert their raw data into insights. By leveraging Data Analytics, organizations such as businesses get a deeper understanding of their products, processes, or services. When insights obtained from Data Analytics are connected to action, it becomes easier for businesses to craft personalized experiences for their customers, optimize their operations, and increase efficiency.

Data Analytics refers to a broad term consisting of data collection, validation, visualization, analysis, and communication. Of importance in this article is Data Analysis which is a field and a profession by itself within the data space. While Data Analytics is a more expansive term including the above processes, Data Analysis is just a subset limited to the actual extraction of meaning from data. A detailed discussion on data analysis and a guide on how to become a data analyst has been given below.

What is Data Analysis?

Data Analysis is the process of extracting meaningful information from data. As the data available to organizations continues to grow, the need for effective and efficient processes and ways for harnessing value from it becomes essential. As a result, businesses need to master the process and key steps in data analysis in their quest to support their data-driven decisions. This process is outlined below;

Stage 1: Identifying Business Questions
At this stage, the business should craft business questions and understand how data can be used to answer the questions.

Stage 2: Collecting Data
This stage involves collecting raw data or putting together datasets that will be used to answer business questions. In a business context, data may come from different sources. Examples include; conducting surveys, Customer Relationship Management (CRM) applications, software, social media applications, or other secondary sources such as government records or publicly available datasets.

Stage 3: Cleaning Data
Raw data is not always clean. It is often characterized by imperfections such as spelling or punctuation mistakes, anomalies, incorrect formats, missing field values, or duplicated records. It is believed that the outcome of any data analysis project is as good as the data used, that is, garbage in garbage out. Common activities involved in this stage include;

Removing duplicates.
Handling missing data.
Handling outliers, i.e., data points significantly far away from the rest of the data.
Fixing structural errors, e.g., typos, poor naming conventions, or incorrect punctuation.

To avoid false conclusions that may result from working with unclean data, businesses need to create a culture of quality data. This may be achieved by defining what quality data business to the business and thoroughly documenting some of the tools that can instill a culture of quality data.

Stage 4: Data Analysis
At this stage, (clean) data is manipulated using different tools and techniques to discover trends, patterns, variations, and correlations. Common techniques used in data analysis include; descriptive, predictive, diagnostic, inferential, quantitative, qualitative, and exploratory data analysis. Common tools used during this stage include; Microsoft Excel and Power BI, Tableau, Python, and R programming languages, and SAS. The tool to choose for your data analysis needs may largely be determined by factors such as capabilities.

Stage 5: Interpretation and Communication
This is the stage where businesses establish whether data can answer the formulated business questions. Depending on the questions developed, a business can make recommendations based on its data.

Data Analysis in Action

Here is an example where data analysis (or analytics at large) has been applied to answer business questions;

Microsoft on Improving Collaboration and Productivity
In 2015, Microsoft needed to improve the collaboration and productivity of its engineering teams. The company understood the need for face-to-face collaborations with its staff and how the same could be used to improve performance and boost collaborations. The Workplace Analytics Team in the company found that moving teams closer reduced the distance they had to cover for meetings. Through relocations of teams, the company was able to save more than $500,000 per year on employee time.

Understanding Feature Engineering

Muthoni, Rogers — Tue, 20 Aug 2024 15:29:29 +0000

Introduction
Feature engineering refers to the selection, manipulation, and transformation of data (mainly raw) into features that can be used for effective data analysis or the development of machine learning models. Data scientists use feature engineering to create new features that can more precisely represent the problem at hand. This is achieved using a set of techniques that aid in highlighting the most important patterns, trends, or relationships, making it easier for models to learn from the data effectively.

In this article, we will look into;
What is feature engineering?
Feature engineering process.
The importance of feature engineering.
Feature engineering techniques.
Tools for feature engineering.

What is Feature Engineering?
When working with Machine Learning models, feature engineering is essential in building reliable machine learning pipelines. It involves selecting, manipulating, and transforming raw data into features that can be used with machine learning models. The new features (or variables) are usually not included in the original dataset. The main aim of feature engineering is to make transformations on datasets to increase the efficiency or accuracy of the models. Feature engineering is considered one of the most crucial tasks that significantly determines the outcome of models. To ensure that a certain machine learning algorithm performs optimally, the features of the input data should be engineered effectively.

Feature Engineering Process
Several processes are involved in feature engineering. These are discussed below;

- Feature creation: This involves creating new features that will be helpful during model development. Typically, this may involve removing or adding features in the dataset.
- Transformation: This refers to the function of transforming features in the dataset from one representation to another.
- Feature extraction: This involves the extraction of features from the dataset without necessarily distorting significant information or relationships existing in the original data.
- Exploratory data analysis: Exploratory Data Analysis (EDA) is the approach used to understand the general patterns, trends, or relationships existing in the data. Normally, EDA involves the use of graphs, charts, or summary statistics to perform initial investigations mainly to identify patterns and trends, and sometimes to spot anomalies in the data.

- The Importance of Feature Engineering
Feature engineering is crucial in any machine learning project. As mentioned, features produced during feature engineering are used by machine learning algorithms to improve performance and accuracy, in other words, to improve the results of the models. Since data scientists spend most of their time with data, making models accurate and reliable is therefore essential.

When feature engineering is done correctly
Feature Engineering Techniques
Tools for Feature Engineering

Understanding Your Data: The Essentials of Exploratory Data Analysis (EDA)

Muthoni, Rogers — Sun, 11 Aug 2024 12:48:00 +0000

Introduction

Exploratory Data Analysis (EDA) is the art of investigating a dataset to discover its main characteristics. It is an important step in every data analysis project. Data professionals often employ EDA techniques to see what the data can reveal while understanding the relationship between different variables in the dataset. Depending on size interpreting and comprehending datasets can be a challenging task. It would not be feasible to make inferences by looking at the first hundred records from a thousand or even a million of them. Through EDA techniques, it becomes easier to extract summaries and find critical and relevant data points that can guide further steps in data analysis projects. In this article, I will discuss various EDA techniques used by data professionals including different methods of summarizing and visualizing data, detecting outliers, and finding correlations.

EDA Techniques

There are several techniques that form the backbone of EDA. The essential techniques include; summary statistics, analysis of missing data, detection of outliers, correlation analysis, data visualization, time series analysis, exploration of categorical data, and dimensionality reduction. An in-depth discussion of these techniques has been made below.
- Summary Statistics
Summary statistics are useful when you want to get a quick overview of your data. They provide information such as the measures of location and spread. Measures of location (also measures of central tendency) provide information on where the data points are located. The particular measures of location include the mean, median, and mode of the data. Measures of spread tell how data points in the datasets are varied or spread out. The particular measures of spread include quartiles, range, interquartile range, variance, and standard deviation.

_- Analysis of Missing Data _
Missing data refers to values or data points that may not be present for some columns (or features or variables) in a given dataset. This may be caused by different factors such as human error or faulty sensors used during data collection. The need to address missing data in data analysis is critical for the success of the project and the reliability of the resulting models.
Missing data may be represented in different forms. Common representations of missing data include; NaN, NULL, or blank spaces. Understanding how missing data is represented in a dataset is important as it will determine the best approach for data cleaning.

_- Detection of Outliers _
During EDA, data professionals often encounter outliers in the data. Outliers are data points that significantly deviate from the general behavior of the dataset. In practice, these data points tend to lie far away from the rest of the data. Outliers may be contributed by errors or data corruption. Although this is the case, outliers could also represent genuine extreme values in the dataset.
Outliers in a dataset can affect statistical analysis, machine learning models, and results of data visualization techniques. Therefore, these values must be addressed properly to avoid producing biased results and conclusions.
Different techniques can be employed to identify and remove outliers. Common techniques include the use of visualizations (such as boxplots) and statistical methods (such as percentiles and z-scores)

- Correlation Analysis
In EDA, correlational analysis is used to show the degree of association between different features in a dataset. Such information can be used during subsequent steps in data analysis especially when there is a need to understand how different variables in the dataset are related. Correlation analysis can also indicate different factors confounding the relationship aspect under study.

_- Data Visualization _
Data Visualization is an important technique in EDA. It allows data professionals to “look at” the data and have a glimpse of how variables are related to each other. This involves the use of graphs and charts that make it easier to spot patterns, trends, anomalies, and relationships. Histograms, bar plots, line plots, and scatter plots are common visualizations that are used in visualizing how variables are related.

Time Series Analysis
Categorical Data Analysis
Dimensionality Reduction

The Ultimate Guide to Data Analytics: Tools and Techniques

Muthoni, Rogers — Sun, 04 Aug 2024 10:00:27 +0000

Introduction

Data Analytics is the process of collecting, transforming, and organizing data to draw conclusions, make predictions, and drive decision-making. In some cases, data analytics is often confused with data analysis. Although the two may be related, they refer to different things. At its most basic level, Data Analytics is a field by itself and focuses on the overall management of data along with the tools and methods used. On the other hand, data analysis focuses on dissecting data sets into their different components and analyzing them to understand how they (components) are related to each other. That is to say, Data Analysis is a subset of Data Analytics.

In this article, we shall delve into the subject of Data Analytics and get to know what it entails, its key concepts, and how it is used. Moreover, we will get to explore Data Analytics skills providing a guide on how to break into a Data Analytics career.

What is Data Analytics

Data Analytics is a broader term used to describe the entire process of data management, methods, and techniques used. It is a multidisciplinary subject that leverages several analytical skills and techniques such as mathematics, computer science, and statistics to get insights from data. Data Analytics includes everything from analysis of data to developing frameworks that can be used to collect and store data.

Understanding Data Analytics

Today, almost every kind of information can be subjected to techniques used in Data Analytics to obtain insights that can be used to improve processes or decision-making. This is the reason why Data Analytics is pivotal in many businesses as it helps in optimizing performance and/or profits while at the same time reducing costs. This increases the overall efficiency of the organizations.

Manufacturing firms, for example, record factory runtimes, workload queues, and downtimes and leverage data analytics to better plan machine operations and achieve optimal operations.

Marketing firms use Data Analytics to understand their customers better. Through data analytics, marketers easily analyze the behavior, trends, tastes, and preferences of their customers helping them to create personalized marketing campaigns. As a result, marketers not only improve engagement with their customers but also increase the effectiveness of their marketing strategies.

Customer historical data forms the basis for banks to leverage data analytics to understand the individual financial habits of their customers. This way, banks can accurately predict and recommend things such as savings and investment decisions helping their customers reach their financial goals. To mitigate credit risk, banks and other lenders also leverage historical data to forecast the likelihood of their customers to default.

Steps in Data Analytics

When starting a new Data Analytic project, data professionals often follow a five-stage process as discussed below;

Stage 1: Determine Business Requirements or Questions
Typically, the quality of data collected for Data Analytics is determined by the business questions asked. An organization can collect as much data as it can but fail at obtaining value from it. That is to say, data will only be beneficial to the organization when the right business questions have been developed. Developing the right business questions requires data professionals to;

Know what the company needs.
Know the problems to be solved.
Know the place of data in answering the business questions.
Know the type of data needed.
Know how results will be measured.
Know what technologies to apply.

- Stage 2: Data Collection
- Stage 3: Data Cleaning and Preparation
- Stage 4: Data Analysis
- Stage 5: Data Visualization and Storytelling

Data Analytics Tools and Techniques

Several tools and techniques are used at different stages of Data Analytics. Data professionals use a wide range of software tools and technologies to acquire, store, process, and report findings. Below are some of the tools used in data analytics.

Python and R
SQL
Google Sheets and Excel
Power BI and Tableau

To extract valuable insights from data, data professionals employ different methods and techniques. Such include;

Time Series Analysis
Machine Learning
Classification
Regression Analysis

Understanding how to use and apply these tools and techniques is what empowers data analysts to uncover insights, make informed decisions, and contribute towards strategic goal-setting in organizations across industries.

How Can One Become a Data Analytics Expert

If you are intrigued by numbers, uncovering patterns, and making data-informed decisions then a Data Analytics career is your perfect field. Below is the brief roadmap you can use to become a data professional.

Step 1: Understand Data Analytics
Before breaking into a Data Analytics career, it is important to have a solid understanding of what the field entails. Typically, data professionals collect, process, and analyze data to uncover insights to help their organizations make better data-driven decisions.

Step 2: Build a Foundation in Data Analytics
To break into a Data Analytics career, you may (or may not) need formal education. However, having formal education can greatly improve your domain knowledge especially if you want to be a data analytics expert in a certain industry. Common formal education degrees that aspiring data professionals may consider include; bachelor's Degrees in Mathematics, Statistics, Computer Science, Economics, Business Administration, and Data Science.
Online courses and boot camps have also become essential in preparing aspiring data professionals for a career in data analytics. This is a perfect option for individuals who prefer self-paced and flexible learning environments. Typically, online courses and bootcamps provide accelerated learning paths providing practical hands-on training to learners. Over the years, this option has empowered many people to become job-ready within a short time. An example of a platform providing data courses is DataCamp.

Step 3: Master Essential Tools and Technologies
Becoming an expert in data analytics requires one to be well-versed in different data tools and technologies. Some of these tools include;

Spreadsheet software such as Google Sheets and Excel.
Programming languages such as Python and R.
Data Visualization tools such as Power BI and Tableau.
Databases skills particularly SQL.

Step 4: Get Practical Experience
You will need to put your skills to work by working on hands-on projects, hackathons, or even getting an internship. Projects are a perfect way to hone your data skills. You can look up data analytics projects online from platforms such as Kaggle which can easily help you build your data portfolio.

Step 5: Earn Certifications
In case you want to validate your data analytics skills, certifications may come in handy. Certifications may come as an added advantage especially if you want to stand out before potential employers.

Step 6: Build a Network
In any career development, networking plays a very critical role. Through networking, people have landed data opportunities such as jobs, collaborations, or even mentorship. Building a professional network may require one to participate in online platforms, attend data conferences, or even join data communities.

The Bottom Line

In a world that is becoming increasingly dependent on data, individuals and organizations can leverage data analytics to be sure of their data. A set of raw and meaningless data points can be transformed using different tools and techniques to obtain informative insights that can support decision-making processes. This makes data analytics the heartbeat of a modern organization.