Session 1: Introduction to Data Analytics

• Overview of data analytics and its applications
• Importance of data analytics in decision-making
• Basic concepts and terminology

Session 2: Data Collection and Cleaning*

• Techniques for collecting and accessing data
• Data cleaning and preprocessing
• Handling missing data and outliers

Session 3: Exploratory Data Analysis (EDA)*

• Understanding the importance of EDA
• Basic statistical analysis and visualizations
• Identifying patterns and trends in data

Session 4: Data Visualization*

• Introduction to data visualization tools
• Creating effective charts and graphs
• Communicating insights through visualizations

Session 5: Basic Statistical Analysis*

• Descriptive statistics
• Inferential statistics and hypothesis testing
• Correlation and regression analysis

Session 6: Introduction to SQL for Data Analytics*

• Basics of SQL for querying databases
• Retrieving and manipulating data using SQL
• Joining tables and aggregating data

Session 7: Introduction to Python for Data Analytics*

• Overview of Python libraries (Pandas, NumPy)
• Data manipulation and analysis with Python
• Basic scripting for data analytics

Session 8: Project: Applying Level 1 Concepts*

• Participants work on a small data analytics project applying learned concepts
• Presenting project findings and evaluation