Understand the basics of Python, its frameworks, and the fundamentals of Python programming for data analysis.
- Overview of Python for Data Science
- Setting up the Environment
- Basic Python Syntax
Work with loops and functions, defined by conditions, to enable automation.
- Conditional Statements & Looping Statements
- Functions
Understand the types of data that can be passed in Python, along with the features and behaviors of different data types.
- Strings, Lists, Tuples, Set, Dictionaries, Arrays
Working with advanced libraries in Python which enable users to apply mathematical and tabular functions for better analytics
- Numpy
- Pandas
Understand the different aspects of Python used for data preprocessing, a crucial step before analyzing and gaining insights.
- Handling Missing Values
- Data Transformation
- Feature Engineering
- Data Inspection
Work with datasets to derive insights that reveal a deeper story beyond what meets the eye.
- Join,Reshaping
- Group Operations
- Data Aggregation
Use advanced visualization libraries to create charts, graphs, and other visuals.
- Matplotlib and Seaborn
- Creating Effective Plots
- Interactive Visualizations with Plotly
This module involves working with processed data, applying different algorithms, and developing a predictive model for exploring and analyzing the dataset.
- Univariate, bi variate and multi variate analysis
- Statistical analysis.
Case study combining all modules to build a mini project
- Documenting EDA Process
