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Science

Industry-Ready Data Science Master Program

Comprehensive Data Science program covering Programming (Python, NumPy, Pandas), SQL (Foundations to Advanced), Excel & BI (Power BI/Tableau), Statistics & Probability, EDA & Business Analytics, ML for Data Science, Time Series, NLP, Recommender Systems, Data Engineering (ETL, Big Data, Warehousing), …

  • 6 Months Duration
  • 30 Modules
  • Paid Access
Course fee ₹5000.00

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Learning path

Course Modules

Work through each module and pass quizzes to unlock the next step in your journey.

Module 1 Locked

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
Module 2 Locked

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
Module 3 Locked

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
Module 4 Locked

SQL Foundations

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

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Learning objectives
  • Write SELECT, WHERE, GROUP BY, HAVING, ORDER BY
Module 5 Locked

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
Module 6 Locked

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
Module 7 Locked

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
Module 8 Locked

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
Module 9 Locked

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
Module 10 Locked

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
Module 11 Locked

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
Module 12 Locked

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
Module 13 Locked

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
Module 14 Locked

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
Module 15 Locked

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
Module 16 Locked

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
Module 17 Locked

Unsupervised Learning

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

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Learning objectives
  • Apply K-Means, hierarchical clustering, PCA
  • Detect anomalies
Module 18 Locked

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
Module 19 Locked

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
Module 20 Locked

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
Module 21 Locked

Recommender Systems

Collaborative filtering, content-based filtering, matrix factorization.

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Learning objectives
  • Apply collaborative and content-based filtering
  • Use matrix factorization
Module 22 Locked

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
Module 23 Locked

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
Module 24 Locked

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
Module 25 Locked

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
Module 26 Locked

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
Module 27 Locked

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
Module 28 Locked

Responsible AI

Bias detection, explainability, fairness metrics.

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Learning objectives
  • Detect bias and apply explainability
  • Use fairness metrics
Module 29 Locked

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
Module 30 Locked

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
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