Week 1: Introduction to data analytics – data types, lifecycle, and key concepts.

Week 2: Basic statistics and probability for analytics.

Week 3: SQL fundamentals – querying, joins, and aggregations.

Week 4: Python for analytics – using Pandas and NumPy.

Week 5: Data visualization basics – Matplotlib, Seaborn, and best practices.

Week 6: Data cleaning and preprocessing techniques.

Week 7: Exploratory Data Analysis (EDA) – uncovering patterns and insights.

Week 8: Advanced Excel techniques – pivot tables, charts, and data functions.

Week 9: Introduction to BI tools – Tableau or Power BI basics.

Week 10: Big data concepts – an overview of Hadoop and Spark.

Week 11: Cloud analytics tools – introduction to Google BigQuery, AWS Redshift.

Week 12: Project: Complete an end‑to‑end analytics project (data cleaning to visualization).

Week 13: Advanced SQL – complex queries and performance tuning.

Week 14: Data warehousing and ETL processes.

Week 15: Advanced data visualization – interactive dashboards in Tableau/Power BI.

Week 16: Statistical modeling – regression analysis and hypothesis testing.

Week 17: Introduction to machine learning techniques for analytics.

Week 18: Advanced Python – data manipulation with SciPy and Scikit‑Learn.

Week 19: Big data analytics with Spark.

Week 20: Time series analysis and forecasting.

Week 21: Data ethics and governance.

Week 22: Industry case studies – real‑world data analytics applications.

Week 23: Mid‑project: Launch an analytics challenge on a real dataset.

Week 24: Review and iterate on the mid‑project findings.

• Define a business problem, source data, and plan a capstone analytics project.

• Develop predictive models, interactive dashboards, and business insights.

• Finalize the project, deploy dashboards, prepare detailed reports, and present findings to industry experts.