Complete Python & Data Science Course for Absolute Beginners¶

This so called project, is actually all the learnings that I have managed to accumulate by taking up the course Complete Python & Data Science Course for Absolute Beginners from Udemy no idea if this is an affiliated link!

I might have tossed up a few materials here and there, from other places which I found to be useful related to Python that are not covered as part of the above mentioned course.

Course Index¶

01 - Python Basics

• Variables
• Operators
• Collections
• Conditions
• Loops
• Functions
• Classes and Objects

02 - Data Visualization with MatPlotLib

• Intro to PyPlot
• Installing PyPlot
• Graphing some data
  • Basic Line Plot
  • Customizing a graph
• Showing multiple graphs
• Different kinds of graphs
  • Bar Chart
  • Pie Chart
  • Histogram
• 3D Graphs

03 - Data Analysis with Pandas

• Intro to Pandas
  • What is Pandas?
  • Pandas Data Structures
  • What are some of the applications of Pandas?
  • Why learn Pandas?
• Installing and importing Pandas
• Creating Pandas Series
  • Creating Pandas Series - With list
  • Creating Pandas Series - With dictionary
  • Creating Pandas Series - with NumPy arrays
  • Creating Pandas Series - Date Ranges
• Getting elements from Series
• Getting properties from Series
• Modifying Series
• Series operations
• Series comparisons and iteration
• Creating Pandas DataFrames
  • Creating DataFrames from Python lists
  • Creating DataFrames from Dictionaries
  • Creating DataFrames from Pandas Series
• Getting elements from DataFrames
  • Getting DataFrames elements based on columns
  • Getting DataFrames elements based on rows
  • Getting individual elements from DataFrames
  • Slicing DataFrame columns and rows
  • Getting DataFrame elements based on booleans
• Getting properties from DataFrames
• Modifying DataFrames
• DataFrames operations
• DataFrames comparisons and iteration
• Reading CSVs into DataFrames

04 - Data Mining with Python

• Data Wrangling
  • Cleaning Data
    • Filtering out noise
    • Making data available for analysis
  • Statistics
    • Simple Statistics
  • Practical example of data mining
  • Dataset Examples
• Data Mining Fundamentals
  • Cluster Analysis
  • Classification and Regression
    • LinearRegression and LogisticRegression
    • Support Vector Classifier and Support Vector Regressor
    • KNeighborsClassifier and KNeighborsRegressor
  • Association and Correlation
  • Dimensionality Reduction

05 - Big Data with Apache Spark

• Apache Spark - Framework Overview
• Apache Spark - Key Functions
• Apache Spark - Machine Learning
• Examples - Using Machine Learning Pipelines

06 - Mining and Storing Data

• Text Mining
• Network Mining
• Python Matrix library
• Mining a SQL-database

07 - Natural Language Processing

• NLP Data Cleaning
• Count Vectorizer, TFIDF
• NLP Example with Spam
• Tweak model with Spam data
• Pipeline with Spam data

08 - PySpark - Building DataFrames with Python, Apache Spark and SQL

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