Module 1: Introduction to Data Science
• Introduction To Data Science
• Life Cycle of Data Science
• Skills required for Data Science
• Careers Path in Data Science
• Applications of Data Science
Module 2: Python for Data Science Python programming:
• Environment Setup
• Jupiter Notebook Overview
• Data types: Numbers, Strings, Printing, Lists, Dictionaries, Booleans, Tuples, Sets
• Comparison Operators
• if, elif, else Statements
• Loops: for Loops, while Loops
• list comprehension
• functions
• lambda expressions
• map and filter
• methods
• Programming Exercises
Python for Exploratory Data Analysis:
Module 3: NumPy
• Installing NumPy
• Using NumPy
• NumPy arrays
• Creating NumPy arrays from python list
• Creating arrays using built in methods (arrange (), zeros (), ones (), linspace(), eye(),rand(),etc.
• Array attributes: shape, type
• Array methods: Reshape (), min (), max (), argmax (), argmin (), etc.
Module 4: Pandas
• Introduction to Pandas
• Series
• Data Frames
• Missing Data
• Group By
• Merging, Joining and Concatenating
• Operations
• Data Input and Output
Python for Data Visualization:
Module 5: Matplotlib
• Installing Matplotlib, Basic Matplotlib commands
• Creating Multiplot on same canvas
• Object Oriented Method: figure (), plot (), add_axes (), subplots (), etc.
• Matplotlib Exercise
Module 6: Seaborn
• Categorical plot
• Distribution plot
• Regression plot
• Seaborn Exercise
Module 7: Pandas built in visualization
• Scatter plot
• Histograms
• Box plot
