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:
- Data Analysis
- Data Science
- Artificial Intelligence
- Deep Learning
- Filtering & selecting
- Treating missing values
- Removing duplicates
- Concatenating and transforming
- Grouping and aggregation
- The three types of Data Visualization
- Selecting optimal data graphics
- Communicating with color and context
- Creating standard data graphics
- Defining elements of a plot
- Plot formatting
- Creating labels and annotations
- Visualizing time series
- Creating statistical data graphics
- 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
- BeautifulSoup object
- NavigableString objects
- Data parsing
- Web scraping in practice
- Introduction to NLP
- Cleaning and stemming textual data
- Lemmatizing and analyzing text data
- Introduction to Plotly
- Create statistical charts
- Line charts in Plotly
- Bar charts and pie charts in Plotly
- Create statistical charts