Note: The Program requires basic knowledge of Python
Data Analytics with Python
With the advent of big data over the last decade and storage becoming cheaper, organizations are collecting a lot more data than before, making it imperative to derive insights from data and unlock the business value hidden in the data. Python is one of the most popular programming languages for data analysis. This course is designed for students who are familiar with a high level programming language like C, C++ or Java and would like to use Python for data analysis. The course provides a fast introduction to Python and then delves into Python data analysis
libraries – numpy, pandas, matplotlib and scikit-learn – and shows how to apply these libraries to practical dataanalytics problems
Chapter 1 – Introduction to Python
● Python interpreter
● Variables/Data Type
● Loops/Conditionals
● Functions
● Data Structures – Lists and Maps
Chapter 2- Advanced Python
● Decorators
● Object Oriented Programming
● Functional Programming
● HTTP Protocol/Requests
Chapter 3 – numpy/pandas
● ndarray
● Vectorization
● Linear Algebra – Matrix operations
● Random number generation and sampling
● Series, DataFrame
● Summary Statistics
Chapter 4: Pandas/matplotlib
● Loading data – csv, sql
● Cleansing and Shaping data
● Grouping, filtering and joining
● Matplotlib – Figures and Subplots
● Matplotlib – Colors, Markers, Legends, Ticks, Lables
● Matplotlib – Area, Pie, Bar, Line, Density, Scatter plots
Chapter 5: Scikit-learn
● Supervised vs Unsupervised Learning
● Model fitting – cost function
● Classification
● Linear and Logistic Regression