Pandas for Data Science

A brief summary of the topics covered in this course is as below. This is 30 hours course, it is suggested to complete this course in 3 weeks. If you already know python enough, then you can skip the first 3 modules of this course. It will save you one week.

Module 1: Basics of Python

  1. Introduction
  2. Getting Started with Python
  3. Introduction to Jupyter Notebook
  4. Data Types in Python
  5. Arithmetic Operations
  6. String Operations
  7. Data Structures in Python
  8. Introduction
  9. Lists
  10. Tuples
  11. Sets
  12. Dictionaries
  13. Assignment and Practice

Module 2: Control Structures and Functions in Python

  1. Introduction
  2. Decision Making
  3. Loops and Iterations
  4. Comprehensions
  5. Functions in Python
  6. Map, Filter and Reduce Functions
  7. Practice Exercise: Map, Filter and Reduce
  8. OOP in Python
  9. Introduction
  10. Class and Objects
  11. Methods
  12. Class Inheritance and Overriding
  13. Decorator Function
  14. Assignment and Practice

Module 3: Python for Data Science

  1. Introduction to NumPy, Matplotlib and Pandas
  2. NumPy
  3. Introduction to NumPy
  4. Basics of NumPy
  5. Operations Over 1-D Arrays
  6. Practice Exercise I
  7. Multidimensional Arrays
  8. Creating NumPy Arrays
  9. Mathematical Operations on NumPy
  10. Mathematical Operations on NumPy II
  11. Computation Times in NumPy vs Python Lists
  12. Assignment and Practice

Module 4: Pandas

  1. Introduction to Pandas
  2. Basics of Pandas
  3. Pandas – Rows and Columns
  4. Series
  5. Dataframe
  6. Pandas functions
  7. Reading files
  8. Index and Reindexing
  9. Sorting
  10. Slicing Dataset
  11. Groupby and Aggregate Functions
  12. Merging DataFrames
  13. Pivot Tables
  14. Window function
  15. Data function
  16. Time delta function
  17. Categorical data
  18. Visualization
  19. IO Tools
  20. Statistical functions
  21. Working with text data
  22. Iterations
  23. Panel
  24. Assignment and Practice

Module 5: Data Visualization in Python I

  1. Introduction to Data Visualizationwith Matplotlib
  2. Introduction to Matplotlib
  3. The Necessity of Data Visualization
  4. Visualization – Some Examples
  5. Facts and Dimensions
  6. Bar Graph
  7. Scatter Plot
  8. Line Graph and Histogram
  9. Box Plot
  10. Subplots
  11. Choosing Plot Types
  12. Assignment and Practice

Module 6: Data Cleaning I

  1. Introduction
  2. Case Study Overview
  3. Visualization
  4. Data Handling and Cleaning
  5. Data Visualization with Seaborn
  6. Styling Options
  7. Sanity Checks
  8. Histograms
  9. Assignment and Practice

Module 7: Data Cleaning II

  1. Introduction
  2. Distribution Plots
  3. Outliers Analysis with Boxplots
  4. Pie – Chart and Bar Chart
  5. Scatter Plots
  6. Pair Plots
  7. Revisiting Bar Graphs and Box Plots
  8. Heatmaps
  9. Line Charts
  10. Stacked Bar Charts
  11. Assignment and Practice

Module 8: Data Visualization in Python II

  1. Plotly
  2. Bokeh
  3. Geoplotlib

Module 9: Project & Resources

  1. Resources for practice
  2. A Final Data Cleaning and Analysis Project