Python - Data Science Essentials (Part 1)

Date: 13th October, 2020 - 25th October, 2020

This repository focuses on my learning path of Python for Data Science.

Here, I haven't included the files of Chapter 1, since there was no practical implementation of it. It focussed more on the introductory part and the professional careers in this field. However, I am listing out few points regarding the same.

Note that Chapter-3 files are not added. Chapter 7 files have been deprecated, so adding the original files for information. However, the topics added are enlisted below:

Chapter 1 - Introduction to the Data Professions

The Four Flavours of Data Science

• Data Analysis
• Data Science
• Artificial Intelligence
• Deep Learning

Chapter 2 - Data Preparation Basics

• Filtering & selecting
• Treating missing values
• Removing duplicates
• Concatenating and transforming
• Grouping and aggregation

Chapter 3 - Data Visualization 101

• The three types of Data Visualization
• Selecting optimal data graphics
• Communicating with color and context

Chapter 4 - Practical Data Visualization

• Creating standard data graphics
• Defining elements of a plot
• Plot formatting
• Creating labels and annotations
• Visualizing time series
• Creating statistical data graphics

Chapter 5 - Basic Math & Statistics

• Simple arithmetic
• Basic linear algebra
• Generating summary statistics
• Summarizing categorical data
• Parametric correlation statistics
• Non-parametric correlation statistics
• Transforming dataset distributions
• Extreme value analysis for outlines
• Multivariate analysis for outliners

Chapter 6 - Data Sourcing via Web Scraping

• BeautifulSoup object
• NavigableString objects
• Data parsing
• Web scraping in practice
• Introduction to NLP
• Cleaning and stemming textual data
• Lemmatizing and analyzing text data

Chapter 7 - Collaborative Analytics with Plotly

• Introduction to Plotly
• Create statistical charts
• Line charts in Plotly
• Bar charts and pie charts in Plotly
• Create statistical charts