Programming for Analytics - Python
Objective:
The course will begin with an introductory course in Python language and go on to cover the principles of programming and software development for analytics with the important attributes like scalability, reliability, accuracy and efficiency. Different essential libraries will be covered that will help participants to integrate multiple technologies, use and build models for analytics, and adapts to changing requirements.
Learning Outcomes:
1. Learn the basic as well as advanced features and applications of Python
2. Get a deeper understanding of systematic and stepwise process of complex problem solving
3. Develop an ability to write effective and efficient algorithms and heuristics for problem solving
4. Learn to solve complex analytical and business problems using Python
Day-wise Content (5 days):
Day 1:
- Introduction to Computer Science and algorithms. Problem definition, determination, analysis, diagnosis, repair and de-duplication
- Introduction to Jupyter Notebook and IDLE
- Data and Expressions using Python
- Control Structures using Python
- Lists using Python
- Functions using Python
- Modular design and introduction to Python modules
Day 2:
- Object oriented programming using Python
- Working with files using Python
- Introduction to Numpy
- Functions in Numpy
- Working with Numpy Arrays
Day 3:
- Introduction to Pandas
- Functions in Pandas
- Working with DataFrames
- Introduction to Matplotlib
- Graphics using Matplotlib
Day 4:
- Introduction to business analytics and its applications
- Introduction to scikit learn
- Regression using scikit learn
- Classification using scikit learn
- Clustering using scikit learn
Day 5:
- Hands-on mini project/ case study to apply all the knowledge and skills gained in the program