Module 1: Introduction to Data Analytics

• Overview of Data Analytics: Understanding the importance and applications of data analytics in various industries.
• Data Analytics Process: Introduction to the data analytics lifecycle, including data collection, cleaning, analysis, and visualization.
• Tools and Technologies: Overview of key tools and technologies used in data analytics, such as Excel, SQL, Python, and R.

Module 2: Data Collection and Cleaning

• Data Collection Methods: Learning different methods for data collection, including surveys, web scraping, and APIs.
• Data Quality: Understanding the importance of data quality and techniques for ensuring data accuracy and completeness.
• Data Cleaning Techniques: Techniques for handling missing data, outliers, and data inconsistencies. Using tools like Excel and Python libraries (Pandas) for data cleaning.

Module 3: Exploratory Data Analysis (EDA)

• Descriptive Statistics: Understanding basic statistical measures such as mean, median, mode, variance, and standard deviation.
• Data Visualization: Learning to visualize data using charts, graphs, and plots. Tools include Excel, Matplotlib, and Seaborn.
• EDA Techniques: Techniques for uncovering patterns, trends, and insights in data through exploratory analysis.

Module 4: Data Analysis with Excel

• Excel Basics: Introduction to Excel functions, formulas, and data manipulation techniques.
• Pivot Tables and Charts: Using pivot tables and charts to summarize and visualize data.
• Advanced Excel: Advanced functions such as VLOOKUP, INDEX-MATCH, and data analysis add-ins.

Module 5: SQL for Data Analysis

• Introduction to SQL: Understanding the basics of SQL and relational databases.
• Data Retrieval: Learning to write SQL queries to retrieve and manipulate data from databases.
• Advanced SQL: Advanced SQL techniques such as joins, subqueries, and window functions for complex data analysis.

Module 6: Data Analysis with Python

• Python Basics: Introduction to Python programming and essential libraries for data analysis.
• Pandas for Data Analysis: Using Pandas for data manipulation, cleaning, and analysis.
• Data Visualization with Python: Visualizing data using Python libraries such as Matplotlib and Seaborn.

Module 7: Statistical Analysis

• Inferential Statistics: Understanding concepts such as hypothesis testing, confidence intervals, and p-values.
• Regression Analysis: Learning linear and logistic regression techniques for predictive analysis.
• Advanced Statistical Methods: Exploring advanced statistical methods such as ANOVA, chi-square tests, and time series analysis.

Module 8: Machine Learning Basics

• Introduction to Machine Learning: Understanding the basics of machine learning and its applications in data analytics.
• Supervised Learning: Techniques such as regression and classification.
• Unsupervised Learning: Techniques such as clustering and association.

Module 9: Data Visualization and Reporting

• Advanced Data Visualization: Creating interactive dashboards and reports using tools like Tableau and Power BI.
• Storytelling with Data: Techniques for effective data storytelling and presentation.
• Reporting Tools: Using reporting tools to create and automate reports.

Module 10: Capstone Project

• Project Selection: Choosing a real-world problem to solve using data analytics techniques learned during the course.
• Implementation: Developing a comprehensive data analysis project, from data collection to final presentation.
• Presentation: Preparing a detailed report and presentation of the project, showcasing the results and insights.