UpGrad PG Diploma in Data Science

1. Preparatory Course –
Data Analysis in Excel, Analytics Problem Solving, Introduction to Python, Python for Data Science

2. Data Toolkit –
Data Analysis using SQL, Introduction to Python, Programming in Python, Python for Data Science, Visualization in Python, Assignment: IMDb Movie Assignment, Exploratory Data Analysis, Inferential Statistics, Hypothesis Testing, Credit EDA Case Study

3. Machine Learning –
Introduction to Machine Learning and Linear Regression, Assignment: Linear Regression, Logistic Regression, Unsupervised learning: Clustering, Business Problem Solving, Assignment: Unsupervised + Supervised, Lead Scoring Case Study

4. Specialization 1: Deep Learning
Tree Models, Model Selection & General ML Techniques, Bagging and Boosting, Advanced Regression, Advanced Regression Assignment, Principal Component Analysis, Time Series Analysis, Telecom Churn Case Study, Introduction to Neural Networks, Neural Networks Assignment, Convolutional Neural Networks – Introduction and Industry Applications, Recurrent Neural Networks, Gesture Recognition.

5. Specialization 2: Natural Language Processing
Tree Models, Model Selection & General ML Techniques, Bagging and Boosting, Advanced Regression, Advanced Regression Assignment, Principal Component Analysis, Time Series Analysis, Telecom Churn Case Study, Lexical Processing, Syntactic Processing, Syntactic Processing Assignment, Semantic Processing, Chatbot Case Study.

6. Specialization 3: Business Intelligence/ Data Analytics
Introduction to Databases, Advanced SQL and Best Practices, Schema Design and Retrieval Assignment, NoSQL Databases and Best Practices, Introduction to Cloud and Hive, SQL Case Study: Analysing Big Data in Retail, Advanced Excel, Visualisation using Tableau, Interactive Marketing Campaign Analysis, Visualisation using PowerBI, Introduction to R and RShiny, Effective Communication Strategies, Formats, and Templates, Presentations to Technical and Non-Technical Stakeholders, Business Case Study.

7. Specialization 4: Business Analytics
Tree Models, Time Series Analysis, Retail-Giant Sales Forecasting Assignment, Model Selection & General ML Techniques, Telecom Churn Case Study, Advanced SQL and Best Practices, Advanced Excel, Structured Problem Solving using Frameworks, Hypothesis Formulation, Business Problem Assignment, Revenue and Operational Cost Modelling, Introduction to Economics and Financial Concepts, Effective Communication Strategies, Formats, and Templates, Presentations to Technical and Non-Technical Stakeholders, Business Case Study.

8. Specialization 5: Data Engineering
Introduction to Hadoop and MapReduce Programming, Data Management and Relational Database Modelling, NoSQL Databases and Apache HBase, Data Warehousing (Optional), Data Ingestion with Apache Sqoop and Apache Flume, Business Problem Assignment (Optional), Building and Querying Data Warehouse with Apache Hive, Case Study: Ingestion & Warehousing, Data Processing with PySpark, Real-Time Data Streaming with Apache Kafka, Real-Time Data Processing using Spark Streaming, Business Problem Assignment, Building Automated Data Pipelines with Oozie/Airflow, Analytics using PySpark, Case Study: Kafka, Spark Streaming and PySpark.

9. Capstone Project