1. Introduction to Data Analytics

• What is Data Analytics?
• The importance of data in decision-making
• Types of Data Analytics (Descriptive, Diagnostic, Predictive, Prescriptive)
• Key concepts: Data, Information, Insight

2. Data Collection and Preparation

• Data sources (structured and unstructured data)
• Methods of data collection (surveys, web scraping, APIs, databases)
• Data cleaning and preprocessing
  • Handling missing data
  • Outliers, duplicates, and inconsistencies
  • Data transformation techniques
• Introduction to ETL (Extract, Transform, Load) processes

3. Data Analysis with Excel

• Excel basics for data analysis
• Using formulas and functions (VLOOKUP, SUMIF, etc.)
• Pivot tables and charts for data summarization
• Data visualization with Excel

4. SQL for Data Analytics

• Introduction to databases and SQL
• Writing basic SQL queries
• Filtering, grouping, and aggregating data
• Joining multiple tables
• Using SQL for data reporting and insights

5. Data Analytics with Python

• Introduction to Python for data analysis
• Key Python libraries: pandas, NumPy
• Data manipulation and transformation using pandas
• Exploratory Data Analysis (EDA) in Python
• Descriptive statistics with Python

6. Data Visualization

• Importance of data visualization
• Data visualization principles and best practices
• Visualization tools: Matplotlib, Seaborn (Python), Tableau, Power BI
• Creating effective visualizations (bar charts, line graphs, scatter plots, heatmaps)
• Dashboards and storytelling with data

7. Advanced Excel Techniques

• Advanced pivot tables
• Using Power Query for data transformation
• Excel Macros for automating tasks
• Data analysis using Excel’s Analysis Toolpak

8. Business Intelligence Tools

• Introduction to Business Intelligence (BI)
• Power BI: Building interactive dashboards and reports
• Tableau: Visual analytics and creating compelling dashboards
• Comparing Power BI and Tableau for business insights

9. Statistics for Data Analytics

• Introduction to statistics and probability
• Descriptive statistics (mean, median, mode, variance, standard deviation)
• Inferential statistics (hypothesis testing, confidence intervals)
• Correlation and regression analysis
• Statistical tests for significance

10. Introduction to Machine Learning

• What is machine learning?
• Types of machine learning: Supervised, unsupervised, reinforcement
• Introduction to regression and classification models
• Introduction to clustering and segmentation techniques
• Basic machine learning models using Python (scikit-learn)

11. Big Data and Cloud Platforms

• Introduction to Big Data
• Hadoop and Spark overview
• Introduction to cloud platforms (AWS, Google Cloud, Azure) for data analytics
• Using cloud-based tools for large-scale data analysis