Introduction to the Data Analytics Lifecycle, Python for Data Analysis: OOP & Data Structures, Managing Environments with Conda, Pip, and Jupyter Notebooks.

Descriptive Statistics: Mean, Median, Mode, and Variance, Inferential Statistics: Hypothesis Testing, p-values, and Confidence Intervals, Probability Theory: Bayes’ Theorem, Distributions, and Sampling Methods.

Data Cleaning & Feature Engineering with NumPy and Pandas, Exploratory Data Analysis (EDA): Distributions, Outliers, and Correlations, Data Visualization with Matplotlib, Seaborn, and Plotly.

Relational Databases: MySQL and PostgreSQL Fundamentals, Advanced SQL: Joins, Subqueries, CTEs, and Window Functions, Writing Analytical Queries for Business Reporting and Dashboards.

PivotTables, Power Pivot, and VLOOKUP for Business Reporting, Dynamic Dashboards and Interactive Charts in Excel, Power Query for Data Transformation and Automated Reporting.

Connecting Data Sources, Data Modelling, and Power Query Transformations, DAX Measures, Calculated Columns, and KPI Cards, Publishing Interactive Reports to Power BI Service and Mobile.

Building Interactive Dashboards with Filters, Actions, and Parameters, Calculated Fields, Level of Detail (LOD) Expressions, and Blended Data, Publishing Workbooks to Tableau Server and Tableau Public.

Introduction to MongoDB: Documents, Collections, and CRUD Operations, Aggregation Pipelines for Analytical Queries, Handling JSON, XML, and Semi-Structured Data for Analytics.

Linear & Logistic Regression for Business Predictions, Decision Trees, Random Forests, and Gradient Boosting, Model Evaluation: Accuracy, Precision, Recall, and AUC-ROC.

K-Means, Hierarchical, and DBSCAN Clustering for Customer Segmentation, Dimensionality Reduction: PCA and t-SNE for Feature Analysis, Market Basket Analysis using Association Rules (Apriori).

Time Series Components: Trend, Seasonality, and Cyclical Patterns, ARIMA and SARIMA Models for Sales and Demand Forecasting, Facebook Prophet for Business Forecasting with Holiday Effects.

Building ETL Pipelines: Extract, Transform, and Load Workflows, Apache Spark for Distributed Data Processing and Big Data Analytics, Workflow Orchestration with Apache Airflow and dbt.

AWS for Analytics: S3, Redshift, QuickSight, and Glue, Azure Synapse Analytics, Azure Data Factory, and Power BI Integration, Google BigQuery, Looker Studio, and Databricks on GCP.

Connecting Google Analytics, GA4, and Sheets to Looker Studio, Building Multi-Page Reports with Drill-Down Filters and Scorecards, Embedding Reports in Stakeholder Portals and Automating Refreshes.

Data Quality Frameworks: Accuracy, Completeness, and Consistency, GDPR and Data Privacy Regulations for Analytics Professionals, Data Cataloguing, Metadata Management, and Access Control.

Translating Data Insights into Business Recommendations and ROI, A/B Testing, Funnel Analysis, and Cohort Analysis for Product Analytics, Stakeholder Storytelling: Communicating Insights with Clarity.

Advanced Git and GitHub for Data Analytics Collaborative Projects, Organizing Jupyter Notebooks and Analytical Reports for Professional Portfolios, Deploying Dashboards on Streamlit and Sharing with Employers.

End-to-End Analytics Project: From Raw Data to an Actionable BI Dashboard, Data Analyst Interview Prep: SQL Tests, Case Studies, and Technical Rounds, Lifetime Placement Support and Exclusive Hiring Drives.