• Data Analyst vs Business Analyst vs BI Analyst vs Analytics Engineer

• Business problem statements, stakeholder requirements, and user stories

• Understanding KPIs, metrics, OKRs, SLAs, and business objectives

• Descriptive, diagnostic, predictive, and prescriptive analytics

• Revenue, profitability, customer acquisition, retention, and churn metrics

• Translating business questions into analytical requirements

• KPI definitions, metric ownership, and acceptance criteria

Internship Assignment: Prepare a Business Requirements Document (BRD), KPI dictionary, and analysis plan for an e-commerce company.

• Advanced formulas: XLOOKUP, INDEX-MATCH, SUMIFS, COUNTIFS, FILTER

• Dynamic arrays, LET, LAMBDA, and modern Excel functions

• Pivot Tables, Pivot Charts, slicers, and advanced conditional formatting

• Power Query for importing, cleaning, and combining datasets

• Power Pivot, Data Model, and introductory DAX

• Financial analysis, variance analysis, forecasting, and scenario modeling

• Excel automation using Power Query and Office Scripts

Internship Assignment: Develop an automated sales, inventory, and financial performance workbook.

• Introduction to Python
• Why Python, Value, Variable, Function, Library [Roadmap on Python]
• IDE in Python, Different Data Types
• List, Tuple, Set & Dictionary Overview
• Different List Methods
• Different Tuple Methods
• Set & Frozenset
• Dictionary & String Manipulations
• Overview on Loops, If Statements, UDFs, Escape Sequences, Lambda
• Types of Operators, Conditional Statements
• While Loop, List Comprehension, Break, Continue, Arguments
• Functions, Escape Sequences, Lambda Functions
• Hackathon-1
• Iterators, Decorators & Generators
• Modules in Python
• Creating Custom Python Libraries
• Lambda, Map, Filter, Reduce
• Exception Handling
• File Handling
• Regular Expressions (Regex)
• Web Scraping Basics
• Introduction to OOPS
• Instance Variable, Class Variable, Class Method
• Association vs Composition & Aggregation
• Oops Concept
• Encapsulation, Inheritance
• Polymorphism, Method Overloading, Method Overriding
• Introduction to Pandas
• Data Analysis using Pandas
• Introduction to Numpy
• Different Numpy Commands
• Introduction to Data Visualisation
• Data Visualisation using Matplotlib
• Data Visualisation using Seaborn
• Data Visualisation using Plotly
• Why Data Cleaning?
• Data Cleaning with Sklearn & pandas
• Regular Expression Basics
• Working with CSV, Excel, JSON, SQL databases, and APIs
• Data cleaning, merging, reshaping, aggregation, and feature creation
• Exploratory Data Analysis using Matplotlib, Seaborn, and Plotly
• Missing data analysis, outlier detection, and data validation
• Automating repetitive reports and data-processing tasks

Internship Assignment: Develop a Python-based automated data cleaning and business reporting pipeline.

• Introduction to Advanced DSA
• Non-Primitive Data Structures
• Non-Linear Data Structures
• What is an Algorithm
• Theory & Code Implementation of Linked List
• Stacks & Queues Assignment
• Coding Stack Data Structure
• Coding Queue Data Structure
• Tree Data Structures
• Types of Tree Data Structure
• Tree Traversal
• BFS Traversal
• Bubble Sort Theory
• Bubble Sort Code Implementation
• Selection Sort Theory
• Selection Sort Code Implementation
• Insertion Sort Theory
• Insertion Sort Code Implementation
• Merge Sort Theory
• Merge Sort Code Implementation
• Quick, Merge Sort Performance
• Quick Sort Theory
• Linear Search & Bisection Search
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• Pandas DataFrames for Coding Interviews

• Data Cleaning and Imputation Logic

• GroupBy and Aggregation Coding

• Merging, Joining, and Concatenating DataFrames

• Reshaping Data (Melt, Pivot)

• NumPy Array Broadcasting

• Vectorized Operations vs Loops

• Handling DateTime Objects in Python

• String Manipulation with Regex

• What is SQL, RDBMS, and Table Structure
• Understanding Sprint, Scrum and Agile Project Breakdown in SQL
• OLTP vs OLAP
• Data Warehousing Concepts
• Data types (INT, VARCHAR, DATE, BOOLEAN, etc.)
• ER Diagrams
• Data Models like Star Schema and Snowflake Schema
• DDL vs DML vs DCL vs TCL Commands
• Basic CRUD Operations 
• Different DDL Commands
• Different DML Commands
• Upsert Operations
• Different DQL Commands
• Database Constraints
• Aggregate Functions, Date Functions and String Functions
• SQL Joins: Inner, Self, Cross, Left, Right and Outer Join
• SQL Grouping & Aggregations
• SubQueries and Types of SubQueries
• Window Functions in SQL

• Data Integrity & Referential Integrity
• Data Normalisation
• First & Second Normal Form
• Functional Dependency & Transitive Dependency
• Boyce Codd Normal Form
• Denormalization
• Temporary Tables, CTE, Recursive CTE
• When to Use Temporary Table, CTE, Recursive CTE
• SubQuery in MySQL
• Views in MYSQL
• Stored Functions
• Stored Procedures
• Triggers in MySQL
• Create Events
• In-depth DDL Commands
• Different Functions in MySQL
• Running totals, moving averages, and period-over-period growth
• Recursive CTEs, correlated subqueries, and nested analytical logic
• Cohort analysis, retention analysis, and conversion funnels
• Customer lifetime value, RFM analysis, and customer segmentation
• Pivots, dynamic reporting queries, and advanced date calculations
• Query optimization, indexing, execution plans, and performance tuning

Internship Assignment: Solve 30+ business SQL problems involving revenue, retention, churn, inventory, and operational KPIs.

• Building reusable analytical views and reporting tables

• Data profiling, deduplication, validation, and reconciliation

• Handling late-arriving records, inconsistent data, and missing values

• Fact tables, dimension tables, and analytical data marts

• Incremental loading concepts and audit checks

• Building reliable SQL transformations for downstream BI reporting

Internship Assignment: Convert raw transactional data into validated analytics-ready tables.

• What is Data? Various Types of Data
• Levels of Measurement
• Categorical Variables & Visualisation Techniques
• Continuous Variables and Visualisations
• Measures of Central Tendency
• Measuring Skewness
• Measures of Dispersion
• Covariance & Correlation Coefficient
• Box & Whisker Plot
• Outlier Identification
• Treating Outliers
• Sampling Techniques
• Statistical Analysis Consideration
• Types of Analytics
• Sample vs Population
• Introduction to Inferential Statistics
• Law of Large Numbers
• Central Limit Theorem
• Probability Distributions
• Conditional Probability
• Continuous Probability Distribution & Discrete Probability Distribution
• PMF, CDF, PDF, PPF in Probability
• Binomial Distribution
• Poisson Distribution
• Normal Distribution
• Exponential Distribution
• Laplace Distribution
• Log-Normal Distribution
• Standard Error
• Confidence Interval Calculations
• Z-Table for Confidence Interval
• T-Table for Confidence Interval
• Hypothesis Testing
• Null & Alternate Hypothesis
• Type-1 Error & Type-2 Error
• 2-Sample T Test
• ANOVA Test
• p-Value Calculations
• Chi-Square Test
• Project Demonstration using all the Statistical Techniques

• Experiment design, randomization, and control groups

• Primary metrics, guardrail metrics, and success criteria

• Sample-size estimation and statistical power

• Conversion rate, click-through rate, uplift, and retention analysis

• Experiment validity, peeking problems, and multiple comparisons

• Evaluating experiment results and recommending product changes

Internship Assignment: Analyze an A/B experiment and present a launch, iteration, or rollback recommendation.

• Connecting to SQL databases, Excel, APIs, and cloud sources

• Advanced Power Query and M language

• Star schema, snowflake schema, fact tables, and dimensions

• Relationships, cardinality, and filter propagation

• Import, DirectQuery, and Direct Lake concepts

• Designing enterprise semantic models

• Model performance, data granularity, and refresh strategies

Internship Assignment: Design an enterprise sales analytics semantic model using multiple data sources.

• Row context, filter context, and context transition

• CALCULATE, FILTER, ALL, REMOVEFILTERS, and iterator functions

• Time intelligence: MTD, QTD, YTD, YoY, and rolling periods

• Dynamic measures, calculation groups, and KPI comparisons

• Ranking, segmentation, and customer profitability measures

• DAX Studio, Performance Analyzer, and optimization techniques

• Drillthrough, bookmarks, field parameters, and dynamic reporting

Internship Assignment: Develop an executive dashboard with advanced business calculations and dynamic KPIs.

• Workspaces, apps, dashboards, and sharing permissions

• Semantic model refresh, gateways, and incremental refresh

• Row-Level Security (RLS) and report access controls

• Deployment pipelines and development-to-production workflows

• Dashboard performance troubleshooting

• Data alerts, subscriptions, and usage monitoring

• Power BI certification alignment and PL-300 preparation

Internship Assignment: Publish and secure a multi-department BI solution with refresh and access controls.

• Tableau Desktop, data connections, and extracts

• Relationships, joins, calculated fields, and LOD expressions

• Table calculations, parameters, sets, and dashboard actions

• Advanced interactive visualizations and executive reporting

• Visualization design, accessibility, and performance optimization

• Dashboard storytelling and presenting insights to stakeholders

• Power BI vs Tableau: choosing the right BI solution

Tableau Assignment: Build an interactive customer and revenue intelligence dashboard in Tableau.

• Dimensional data modeling and data warehouse fundamentals

• ETL vs ELT and layered analytics architectures

• Introduction to dbt and SQL-based transformation models

• Data lineage, documentation, and reusable metrics

• Data quality tests, reconciliation, and model dependencies

• Git, GitHub, branching, pull requests, and code reviews

• Development, testing, and production environments

Internship Assignment: Build a version-controlled analytics project with documented SQL transformations and automated data tests.

• Cloud analytics fundamentals: Azure, AWS, and Google Cloud

• Introduction to BigQuery, Snowflake, and Microsoft Fabric

• Cloud data warehouses, lakehouses, and storage concepts

• Querying large datasets and understanding compute costs

• Data ingestion, orchestration, and scheduled pipelines

• Connecting cloud datasets to Power BI and other BI tools

• Data access, permissions, privacy, and governance fundamentals

Internship Assignment: Build a cloud-based reporting workflow using SQL, a cloud analytics platform, and Power BI.

• Batch analytics vs streaming and near-real-time analytics

• Event-driven data sources, APIs, and telemetry

• Introduction to Microsoft Fabric Eventstream and Eventhouse

• KQL fundamentals for event and time-series analysis

• Transaction monitoring, delivery tracking, and anomaly detection

• Designing operational dashboards with refresh and alerting

• Handling data latency, duplicate events, and unreliable feeds

Internship Assignment: Build a simulated live digital payments or delivery operations monitoring solution.

• AI-assisted SQL writing, explanation, and debugging

• Python data-analysis assistance and code validation

• Using AI to explore datasets and suggest analytical approaches

• Natural-language interactions with BI and semantic models

• AI-assisted data documentation and dashboard narratives

• Automating repetitive analytical workflows

• Hallucination prevention, data privacy, and human verification

• Using AI responsibly without compromising analytical accuracy

Internship Assignment: Develop an AI-assisted analytics workflow that produces validated SQL queries, reports, and business summaries.

• E-Commerce & Retail: Sales, margins, inventory, RFM, customer retention

• Banking & FinTech: Loan portfolios, digital payments, transaction failures, delinquency

• Marketing: Campaign ROI, CAC, ROAS, funnel conversion, attribution fundamentals

• SaaS & Product: Activation, churn, cohorts, DAU/MAU, feature adoption

• Supply Chain: Vendor SLAs, lead times, inventory turnover, fulfillment

• HR & Finance: Attrition, recruitment, budgeting, variance, profitability

Internship Assignment: Analyze business cases across different industries and explain findings using industry-specific KPIs.

• Receive a stakeholder-style analytics request and clarify requirements

• Understand source-system schemas and data limitations

• Extract raw data and perform quality checks

• Transform data with SQL, Python, and Power Query

• Define, calculate, and validate business KPIs

• Design dashboards based on stakeholder requirements

• Review results, resolve defects, and document changes

• Present findings, answer stakeholder questions, and recommend actions

Internship Assignment: Complete an end-to-end analytics delivery cycle with realistic change requests, deadlines, and review feedback.

• Advanced SQL interview questions and timed assessments

• Excel, Python, Power BI, DAX, and statistics assessments

• Business case interviews and KPI interpretation challenges

• Dashboard review and troubleshooting exercises

• Stakeholder communication and analytical presentation rounds

• Resume and LinkedIn project positioning

• GitHub portfolio review and project storytelling

• Mock technical interviews, case presentations, and feedback

Career Deliverable: A professional analytics portfolio, interview-ready project walkthroughs, and a skills-gap improvement plan.

• Types of ML: Supervised, Unsupervised, Reinforcement
• Pipeline: Data Cleaning → Feature Selection → Modelling → Evaluation
• Bias-Variance Trade-off
• Train/Validation/Test Splits
• Cross-Validation
• Linear Regression (with gradient descent math)
• Math behind OLS Technique
• Assumptions of Linear Regression
• Evaluation Metrics: RMSE, MAE, R² Score
• Polynomial Regression
• Coding Linear Regression Model
• Regularisation Techniques
• Why Logistic Regression
• Math Behind Logistic Regression
• Evaluation Metrics Behind Classification Algorithms
• Evaluation: Confusion Matrix, Accuracy, Precision, Recall, F1, AUC-ROC
• Coding Logistic Regression Model
• Introduction to Decision Tree
• Intuition Behind Decision Tree
• Math Behind Decision Tree
• Math Behind Decision Tree using GINI
• Drawbacks of Decision Tree
• Random Forest, Gradient Boosting, XGBoost, LightGBM, CatBoost
• Coding Decision Tree, Random Forest, GB
• SMOTE Technique to Handle Imbalanced Dataset
• Filter Method, Wrapper Method, Embedded Method – Feature Selection Techniques
• Coding Feature Selection Techniques
• Math Behind KNN
• Math Behind SVM
• Plotting SVM
• Introduction to PCA
• Math Behind PCA
• Coding PCA
• AutoML Using Pycaret
• Clustering: K-Means (Euclidean distance), Hierarchical Clustering
• Dimensionality Reduction: PCA (Eigen decomposition), t-SNE
• Anomaly Detection
• Hyperparameter Tuning: GridSearchCV, RandomSearch
• Pipelines with Scikit-learn