Forem: Gagan

Hello How are you?

Gagan — Wed, 12 Oct 2022 19:29:08 +0000

POV I am writing

Why Python is Best🤔 - 5 Reasons

Gagan — Sat, 06 Mar 2021 22:47:24 +0000

Nowadays there does exist many programming languages and Python is one of these which often comes under limelight. As per surveys conducted by SlashData, there are over 8.2 million developers who use Python programming language for their work. That's a lot, so there must be some strong reasons Why they use Python? If you come to me and ask why I would prefer using Python over other programming languages then my answer would be Code Quality, Developer Producticity, Portability, Support for libraries and Easily integrateability.

Code Quality

From experience of working at multiple startups and some other big companies what I've realised is that Most of Developers spend their time on trying to figure out what code is required for implementing certain functionality. But Python's feature of Easily readable, coherence makes it unique and quite easier to implement functionality.

Python is designed to be easily readable
Easy to implement code in some specific techniques like Object-Oriented and Functional Programming

Developer Productivity

Owing to simpler syntax, easily readable Python reduces time to implement functionality way lower as compared to other programming languages like C. Also in Developer community people jokes about Python saying "Python code is one-third to one-fifth of equivalent C++ code".

Lesser time to write Python code
Lesser time to find bugs and do debugging
Less time to maintain code over time

Portability

As Python is interpretable that's why it could be easily ported from Windows to Mac or to Linux, it's just matter copying a script over. But for so many other programming languages so many things need to be kept in mind before porting over. For example - For running same C++ code using gcc on Mac and then transferring over it to Windows, Developer need to keep in mind that there does exist some differences between gcc on Mac versus on Windows. So before porting over some things need to be fixed.
But that's not the case with Python.

Python can be easily ported from Windows to Mac or some other operating system.

Support for Libraries/Modules

Python comes with a number of modules beforehand, so you don't need to explicitly write code doing some stuff. For example - Doing a search through a text file of 100000 lines is so easy that it can just be done in three lines of code using re python module.
In addition to a number of internally defined modules like array, calendar, email and many more. There does exist many libraries externally specifically designed for Data Science Purpose. Some of python libraries designed for doing Data Science are -

Numpy
Scipy
PyTorch

import re
txt = "Computer Science Hub"
x = re.search("Hub$", txt)

if x:
    print("Yes, txt have Hub word")
else:
    print("No, txt doesn't have Hub word")

Easily Integrateability

Nowadays most of code need to run across multiple platforms either it in hand of users as SmartPhone or on lap of users Laptops. That's why most of Software need to work across platforms, but I believe that it would be meaningless if a company's engineering team spends 6 months on developing a piece of software and then spends another 6 months for making it to work across platforms.
But that's not the case with Python Programming Language you can easily integrate it with other programming languages to make it work across platforms.
Not only that you can easily integrate C++ code with Python, providing access to low level of memory if you need to explicitly do Memory Management while coding in Python.

Conclusion

From experience of learning Python at university to working as a Python Developer, I've realised that most people prefer python because of this one single reason - Easy to read and write. Also most of software engineers have started their programming journey by first learning Python.
Personally I also recommend to learn Python first and then move onto learn some low level language like C++, because understanding under the hood stuff is really crucial to develop reliable, cross-platform and scaleable software.
Do comment down below why like Python or prefer it over other programming langauges.

Happy Coding 🥳

PandasGUI: GUI interface for Pandas Library

Gagan — Tue, 03 Nov 2020 02:13:33 +0000

Over the recent few years owing to boom of Data Science Industry, Pandas have become quite important tool specifically used for Exploratory Data Analysis.

Exploratory Data Analysis refers to the critical process of performing initial investigations on data so as to discover patterns,to spot anomalies,to test hypothesis and to check assumptions with the help of summary statistics and graphical representations.

Earlier Data Scientists were expected to have great Python programming skills. But now due to the development GUI based easy to use interfaces this anomaly is changing.

Recently PandasGUI have been released which provides a GUI Interface for accessing in built functions in Pandas. Developers of PandasGUI have wrapped Pandas into a clean GUI interface, which can be easily used for Data Analysis.

Here is a brief guide how to use it ->

Installation

PandasGUI can be easily installed using pip

pip install pandasgui

*If you've not got pip already installed then watch following tutorial to see how you could do that -> *

Features

Let's see what features does PandasGUI have by using an example.

Importing library into workspace

Common way to import libraries into Python workspace is by using import statement.

import pandasgui

Opening GUI interface

For opening GUI interface just pass dataset as a parameter to show function which can be imported by using from pandasgui import show. This will open up GUI interface showing dataset in tabular form. Let's see how this work by taking titanic(inbuilt dataset in pandasgui) as an example.

import pandas as pd
from pandasgui import show
from pandasgui.datasets import show
gui = show(titanic)

This will open up following GUI ->

As is clear from above picture there is a vertical column on left hand side.This contains information about dataset.
Along with this there're five tabs - DataFrame, Filters, Statistics, Grapher and Reshaper.

DataFrame Tab

This tab shows dataset which is read using pandas in tabular form.

Filters Tab

Before using filters we need to drag DataFrame tab and leave at top. This will make dataset to show in filters tab.

Different filters here can be applied like == > < to column names.

Statistics Tab

Here you can see brief information Mean, Standard Deviation, Min or Max for different columns in dataset.

Grapher Tab

Here you can use columns in dataframe to make plots. There is option for many types of graphs like Histogram, Scatter and Bar etc.

Any plot can easily be made by following these steps ->

Click on graph name you want to make, this will involve some function which will show default variables required for making that graph under Name.
Just drag and drop column names on to default variables(as per you want to make graph). Then click on Finish. This will run and graph would pop up.

Reshaper Tab

So in this tab there are two functions which are being offered -> Pivot and Melt. These can be used in same way as grapher but this is for reshaping dataframe.

Conclusion

In this article, we looked at a GUI-based tool for analyzing pandas dataframes. This tool has a number of interesting features like filtering, sorting, visualizing, and even aggregating, which we saw in detail with an example dataset. As this library is just released and still is in developing phase so there will be more and more features which would be added to this in future.

If you liked this article and find it useful hit like button. Also check out my blog Computer Science Hub.
If want to reach out to me then just DM me on Linkedin

Why you should learn Python in 2021?

Gagan — Sun, 01 Nov 2020 15:46:53 +0000

It's November 2020, year is ending and world is waiting for dawn of 2021. With that comes the question for Tech Enthusiasts/Software Developers "which new language they should learn in 2021". Python is a great choice for learning as it can used in different kind of applications development across software industry.

Easy to learn

Python have quite simple syntax and cherry on the top of cake is that external libraries can be easily imported, thus making writing code more easier. Like for doing a task C in most cases require more code as compared to Python.If you’re keen enough to start learning Python then watch following tutorials which are taught by experts from freecodecamp ->

Beginner Tutorial

Intermediate Tutorial

Number of Application

Python is such a powerful and wide programming language that it can be used in so many domains of technology.

Web Development

Python can be used for developing websites by using frameworks like Django or Flask. Also with increasing access of internet to the developing parts of world, there will more and more content creators who does not have website developing skills. So by learning Web Development using Python you can offer this as a service on fiverr and make some money.For learning Web Development using Python watch following tutorial ->

Machine Learning

Famously most of highly intellect people is saying that Machine Learning would be next big thing. Owing to this reason a lot of people are trying to break into it. One of most important aspect of Machine Learning is Python either it be building models or doing data visualisations. It can be clearly said that Python is back bone for Machine Learning. Moreover Python have libraries like Scikit-Learn which abstracts out complexity of implementing functions and enables ML Developer to build meaningful models.

Data Science

Well Data Scientist is sexiest job of the century as per Forbes, moreover there a lot of new job posting coming up every month related to Data Science.

Watch following tutorial in order to learn how to use Python as a tool for doing Data Science.

There are many more applications of Python programming language which makes it quite lucrative to learn.

Good luck for your Python Journey!!

Check out my blog for such more useful articles ->
https://computersciencehub.io/

Interview with Machine learning Consultant

Gagan — Fri, 30 Oct 2020 00:18:23 +0000

Vin Vashishta is an Applied Machine Learning Consultant who’s an industry veteran. I thought it would be cool to talk to him about his day to day, how being a consultant (and running his own company) is different to working as an employee, and what advice he has to those looking to break into the field.

You have a background in engineering. Can you tell me a bit about your early days? Were you always interested in Data Science?

The first piece of my education was in Computer Science. Microsoft gave my university a grant to do Machine Vision research and that’s what I worked on whilst I was in college. I expected to graduate, go straight into Microsoft and work on AI, but the wave of AI at the time, in the 90s, petered out and so that wasn’t possible.

And what’s interesting is that models back then were different to now. The definition of model was more a scientific definition – you looked at examples in environmental sciences, climatology, etc. Those were models that had solid features which were proven – you could do mathematical proofs that showed that these models did what they were supposed to do.

We didn’t get that at the beginning of machine learning a lot and a lot of students don’t get that concept. That you don’t just train and test, but then you go out and validate. You have to prove your model works, you can’t just rely on predefined metrics.

So anyways, I spent 15 years in technology. I did everything from installing PCs and building websites to leading teams. I’ve been involved at every stage of the software development cycle.

How did the transition to Data Science look like?

In 2010, I was working at a casino gaming company. One of the projects I got to work on started gathering data across different casinos and we were able to follow players from casino to casino. This is similar to marketing where you’re able to follow the same user from one site to the other. The team I was working with had experience in the video game sector, where this type of attribution is common, so we were all able to connect the dots.

And then I got laid off. So I had this chaotic 6 week period where I was looking for a job but then I just decided to start a business. I realized that I could do much of the analysis I had been doing at my job for other clients.

Starting a business though meant that I had to learn so many new things. There was no blueprint, I had to learn through experience. The machine learning research had been there for 8-10 years but the hard part was applying it within the context of industry.

Then, on the business side, I had to learn other things like sales and marketing. I hate selling but enjoy marketing.

What does working with clients look like?

I say no to clients more than they say no to me. I want to make sure clients are set up for success right from the beginning and a lot of the time that’s hard to do. Sometimes, they think they want to do machine learning but they really don’t – they want to do something else.

And sometimes I just have to tell them that what they’re doing is not machine learning. It’s just advanced analytics. In those cases, I have to teach the team how to do it. I have to help them build the models.

There’s a bit more scientific rigor needed to create something that works. There’s a level of maintenance and monitoring post deployment that needs to be done which is often overlooked and I need to often educate clients on this part.

In a lot of cases, I enjoy my work because I have to go into a company and teach senior leadership what monetizing machine learning products means, build a path to production, and bring teams together. For instance, dev teams often have to get involved – there’s quality and assurance to be done at the feature level, there’s data wrangling and transformation to worry about, there’s the data pipelines that need to be considered, etc. So there’s a lot of moving parts that need to be outlined at the strategy level.

What company sizes are you normally working with?

I’ve worked with startups all the way up to Fortune 100 clients. And it may sound like they’re completely different processes, but a big company is willing to do something very similar to what a startup does but it just has more teams. So in bigger companies, it is basically just a rapid deployment of this process and adoption cross teams.

The startup has few teams. So the depth of adoption is fairly simple – I can sit down with everyone who’s going to be involved within the process and just have one on ones.

In a larger company, I am initially working with one set of teams for a particular product line. And we are creating a very detailed and consistent process. Once you’ve done it once, then those teams can go out and teach other teams how to do it. Now you’re able to create this institutional knowledge on how to do machine learning from your research phase all the way down to maintenance and continuous improvement.

Another important thing to mention based on this: it may only take the startup about 6 months to be fully educated on this process since there’s less people than at a larger company, where it may take 2 years. But once a larger company has multiple teams educated on the process, they can then themselves do the education to other teams, so the rate of iteration grows and the actual ROI is similar in terms of timeline to what the startup sees.

What does an average day look like?

I usually wake up around 5am and start doing social media. That’s a big part of what I do marketing wise. So I’m writing blogs, doing interviews like this, and responding to emails.

I then will have some standing meetings with clients. I’ll also spend 2-3 hours a day building a model or reviewing it. I’ll spend a few hours a day on strategy calls sitting in on senior leadership and helping them understand product performance.

Every two weeks, I do an executive seminar where I explain concepts like monetization or product planning. I answer lots of questions and understand what my customers’ needs are.

So I wear a lot of hats everyday but do enjoy most of it.

How important is storytelling within Data Science? What are some ways we can get better at it?

Well, you need communication skills and that starts with having communication objectives. And then understanding how to use language to achieve those objectives.

The main case of that involves data. The more complex the concept behind the communication objective, the worse words do. Words break down very quickly as a mechanism to exchange complex ideas. So you have to use visuals instead.

In many cases, you have to put a whole lot of information into a very simple format. And again the visualizations carry with them the communication objectives. And a lot of the time, you’re not there to do the translation (because your visualization may go out in a dashboard). So you have to build visualizations that stand on their own and are not able to be over extended. They need to have that asterix in important places.

The hard part about this is that the story always evolves. The dataset isn’’t static. So your story is evolving and you have to explain to users that even though there is more data to consume, it still has an end. There is still some assessment that is unsupported even though it may feel like it is.

Source -> Machine Learning Interview

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