Pandas for Data Science

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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

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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

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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

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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

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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

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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

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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

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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

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1. Plotly
2. Bokeh
3. Geoplotlib

Module 9: Project & Resources

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