PYTHON FOR DATA ANALYTICS
Day 1: Intro to Data Analytics and Python
 Introduction to Data Analytics and its applications.
 Overview of Python for data analytics.
 Setting up the Python environment (Anaconda, Jupyter Notebooks).
 Basic Python programming concepts.
 Introduction to key libraries: NumPy, Pandas, Matplotlib.
Day 2: Data and Statistics for Analytics
 Understanding different types of data (categorical, numerical, etc.).
 Descriptive statistics: mean, median, mode, variance, standard deviation.
 Correlation and regression analysis.
Day 3: Data Manipulation with Pandas
 In-depth exploration of Pandas library.
 Loading and inspecting datasets using Pandas.
 Data cleaning and handling missing values.
 Data manipulation operations (filtering, sorting, grouping).
 Merging and joining datasets with Pandas.
Day 4: Data Visualization
 Introduction to data visualization principles.
 Basic plotting with Matplotlib.
 Advanced data visualization with Seaborn.
Day 5: Dashboarding with Python, Streamlit
 Introduction to dashboarding concepts.
 Basics of creating dashboards with Matplotlib and Seaborn.
 Hands-on experience with Streamlit for creating interactive dashboards.
 Deploying a simple dashboard.