Industry-Ready Data Science Master Program

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Python for Data Roles

Python syntax, data types, functions, OOP basics, exception and file handling, virtual environments, Jupyter, debugging.

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Learning objectives Write Python with correct syntax and data types Use functions and OOP basics Handle exceptions and files Use virtual environments and Jupyter Debug effectively

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NumPy Deep Dive

Arrays, broadcasting, vectorization, matrix operations, performance optimization.

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Learning objectives Work with NumPy arrays and broadcasting Apply vectorization and matrix operations Optimize performance

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Pandas Advanced

DataFrames, GroupBy, merging & joining, pivot tables, multi-indexing, window operations, cleaning pipelines, large datasets.

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Learning objectives Master DataFrames, GroupBy, merging, joining Use pivot tables and multi-indexing Apply window operations and cleaning pipelines Handle large datasets

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SQL Foundations

SELECT, WHERE, GROUP BY, HAVING, ORDER BY.

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Learning objectives Write SELECT, WHERE, GROUP BY, HAVING, ORDER BY

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SQL Intermediate

Joins (all types), subqueries, CASE, aggregate functions, indexing.

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Learning objectives Use all join types, subqueries, CASE Apply aggregate functions and indexing

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SQL Advanced

Window functions, CTE, recursive queries, query optimization, performance tuning, database design basics.

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Learning objectives Use window functions, CTE, recursive queries Optimize queries and tune performance Apply database design basics

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Advanced Excel

Pivot tables, lookup functions, Power Query, data cleaning, financial modeling basics.

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Learning objectives Use pivot tables and lookup functions Apply Power Query and data cleaning Use financial modeling basics

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Power BI / Tableau

Dashboard creation, data modeling, DAX basics, KPI dashboards, interactive reports, storytelling.

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Learning objectives Create dashboards and data models Use DAX basics and KPI dashboards Build interactive reports and storytelling

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Descriptive Statistics

Mean, median, variance, standard deviation, skewness, correlation.

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Learning objectives Calculate mean, median, variance, standard deviation Use skewness and correlation

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Probability

Random variables, probability distributions, conditional probability, Bayes theorem.

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Learning objectives Work with random variables and distributions Apply conditional probability and Bayes theorem

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Inferential Statistics

Sampling, confidence intervals, hypothesis testing, p-values, A/B testing, ANOVA, chi-square.

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Learning objectives Apply sampling and confidence intervals Use hypothesis testing, p-values Design A/B tests and use ANOVA, chi-square

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Exploratory Data Analysis

Data profiling, missing value treatment, outlier detection, correlation analysis, feature engineering.

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Learning objectives Profile data and treat missing values Detect outliers and analyze correlation Perform feature engineering

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Business Analytics

KPI identification, funnel analysis, cohort analysis, customer segmentation, revenue analytics, retention analysis.

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Learning objectives Identify KPIs and perform funnel analysis Use cohort analysis and customer segmentation Apply revenue and retention analytics

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Experimentation & Product Analytics

A/B testing design, metrics definition, experiment evaluation, causal inference basics.

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Learning objectives Design A/B tests and define metrics Evaluate experiments and apply causal inference basics

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ML Foundations

ML workflow, train/test split, cross validation, bias-variance tradeoff.

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Learning objectives Follow ML workflow and use train/test split Apply cross validation and understand bias-variance

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Supervised Learning

Linear regression, logistic regression, decision trees, random forest, gradient boosting, evaluation metrics.

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Learning objectives Apply linear/logistic regression, trees, RF, gradient boosting Use evaluation metrics

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Unsupervised Learning

K-Means, hierarchical clustering, PCA, anomaly detection.

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Learning objectives Apply K-Means, hierarchical clustering, PCA Detect anomalies

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Model Tuning & Feature Engineering

Hyperparameter tuning, feature selection, regularization, imbalanced data handling.

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Learning objectives Tune hyperparameters and select features Apply regularization and handle imbalanced data

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Time Series Analysis

Trend & seasonality, ARIMA, SARIMA, forecasting, Prophet.

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Learning objectives Identify trend and seasonality Apply ARIMA, SARIMA, Prophet for forecasting

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NLP Basics

Text cleaning, TF-IDF, sentiment analysis, text classification.

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Learning objectives Clean text and use TF-IDF Perform sentiment analysis and text classification

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Recommender Systems

Collaborative filtering, content-based filtering, matrix factorization.

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Learning objectives Apply collaborative and content-based filtering Use matrix factorization

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ETL & Data Pipelines

ETL concepts, Airflow basics, data transformation, batch vs streaming.

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Learning objectives Apply ETL concepts and Airflow basics Transform data and understand batch vs streaming

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Big Data Fundamentals

Hadoop overview, Spark basics, PySpark, distributed data.

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Learning objectives Understand Hadoop and Spark basics Use PySpark and distributed data

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Data Warehousing

Star schema, snowflake schema, data lakes, data modeling.

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Learning objectives Design star and snowflake schemas Work with data lakes and data modeling

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Cloud Basics

AWS, GCP, Azure basics, cloud storage, cloud databases.

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Learning objectives Use AWS, GCP, Azure basics Work with cloud storage and databases

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Model Deployment

Flask/FastAPI, Docker basics, REST APIs, CI/CD basics.

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Learning objectives Deploy with Flask/FastAPI and Docker Expose REST APIs and use CI/CD basics

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Data Governance

Data quality, data privacy, GDPR basics, data lineage.

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Learning objectives Ensure data quality and privacy Apply GDPR basics and data lineage

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Responsible AI

Bias detection, explainability, fairness metrics.

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Learning objectives Detect bias and apply explainability Use fairness metrics

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Portfolio Development

GitHub projects, Kaggle projects, case studies, end-to-end projects.

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Learning objectives Build GitHub and Kaggle projects Complete case studies and end-to-end projects

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Interview Preparation

SQL interview questions, case study interviews, product thinking, ML and statistics interview questions.

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Learning objectives Prepare for SQL, case study, product thinking Answer ML and statistics interview questions