MTech Student in Data Science and Machine Learning|Ex-Ericsson|

Data Visualization

Interactive Tableau visualizations for a stolen bike dataset in the United Kingdom for the years 2014 to 2017.

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Photo by Markus Spiske on Unsplash

In this case study, we will see some interesting visual solutions for the questions we could make out from the “Stolen Bikes” data set.

Here, I have used “Tableau” for working out my visualizations and let us see how easy and visually appealing these charts and graphs be. The figures used in this blog are static as they are the snapshots taken from the tableau. At the end of this article, I have given the link to my tableau public where I have published this visualization where it is interactive to use.

A brief summary of the dataset used:

The dataset used here is for the stolen bikes in the United Kingdom from 2014 and 2017. It contains the column names such as “Crime ID”, “Crime Type”, “Falls within”(i.e the police station jurisdiction) , “Last outcome category”(i.e the status of the case), and “Location”. …

Data Analysis

An analysis of the Uber request dataset with relevant illustrations using seaborn and matplotlib visualization libraries.

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Photo by Austin Distel on Unsplash

Uber is a cab service provider for people wanting to travel from one place to another. Here, I have taken an Uber request dataset from Kaggle to try and perform analysis using the visualization libraries such as seaborn and matplotlib. At the end of this article, I have given a link to my Kaggle notebook where I have performed a detailed analysis of this Uber dataset.

The steps taken to perform this analysis are:

  1. Understanding the dataset
  2. Cleaning the data
  3. Draw meaningful conclusions using the visualization libraries.

Let us jump right into the analysis and see what can be understood to make relevant conclusions. …


Fantastic four aspects of python programming(Built-In/User-defined functions, Lambda functions, Recursive functions, and List Comprehension)

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Fantastic four aspects of python programming

After having seen loops and conditional statements in the previous article, we now move forward with the set of topics called “Fantastic Four”. This includes functions(built-in, lambda, recursive) and list comprehension which constitutes the core of Python programming. Let us see each of the concepts in detail with relevant examples.


Functions are broadly divided into built-in, user-defined, and lambda(anonymous). Each of the categories is self-explanatory except the lambda functions which can be used on the go as opposed to the conventional user-defined functions. We will deal with it later in this article.

Built-In functions:

The Python interpreter has functions instantly available for use, called built-in functions. Let us see some common built-in functions. …


Conditional statements and loops in python programming with 60+ practice exercises.

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Conditional statements and loops

After a very detailed study on data structures in Python, let us learn the conditional statements and loops which allows us to do a certain task over and over again(loops) for conditions governed by the conditional statements. We will be seeing each with a few examples to understand how the flow of logic, conditions, iterations are implemented.

NOTE: At the end of this article, I have provided a link to my GitHub repository where you can find more than 60 practice questions and solutions in addition to the examples shown here.

Conditional Statements

if statement:

The if statement decides whether given statements need to be executed or not. It checks for a given condition, if the condition is true, then the code present inside the if block will be executed. …


All you need to know about the very interesting “Data Structures” in python programming.

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Python Data Structures

“Python Collection” also called “Python Data Structure” constitutes the core functionality of how data is stored and being assigned to a variable. All the examples we have seen until now in this series “Understanding Python” had one element assigned to a variable and the data type of the element was either an int, float, or a bool. Moving forward in this article, we will be focussing more on the “advanced data types”(list, tuple, set, and dictionary) wherein a variable can hold more than one element.

Python Collections

  1. List
  2. Tuple
  3. Dictionary
  4. Set

We will be learning about each item in the contents for their accessing technique, properties, operations, and the methods they offer. …


A quick read into the different operators available in python programming.

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We learned the structure and the basic know-how on using the variables, print(), built-in functions, help(), input() in the introductory part of “Understanding Python”. Now, let us see a very basic yet powerful aspect of any programming language which is the “Operators”.

Note: All the programming examples covered in this blog are performed on “Jupyter” platform.

What do you mean by “Operator”?

Suppose, when you would want to compute the addition of 2 numbers “x” and “y”, you just place a sign “+”(x+y) and this will do the trick. …


A quick go through into the fundamentals of python programming.

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Photo by Hitesh Choudhary on Unsplash

This is the first part of “Understanding Python” wherein we would be learning the foremost 5 fundamentals in python. They are,

  1. Variables and their assignment
  2. Print function
  3. User input() function
  4. How to find information using help()
  5. Built-in functions

Let us jump right into understanding each of them so that at the end of your reading, you will have basic know-how on working with python.

Note: All the coding examples provided in this blog are done in the Jupyter notebook which is a web-based platform to work on python. …

Do you want to start with “Python Programming” simply and easily? Then, this is the right place for you. Let us get down to it!!!

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Photo by Adam Whitlock on Unsplash

If you are stuck on how to get started with Python programming where the only thing you know with its name is that “It is a Snake”. …

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