Program Curriculum

The content is organized into highly cohesive modules, each of which helps the participant earn a distinct badge of qualification.

WeekTopicDuration (hours)
Week1Overview of Analytics, Analytics Methodology and Problem Definition3
Week2Exploratory Data Analysis - Principles and Output3
Week2Querying with SQL3
Week2Data Manipulation with SQL3
Week3Recap & Assignment discussion + Industry Guest Lecture3
Week3Practice Session + Instructor Guided Tutorials on Assignments3
 Badge: Descriptive Analytics with SQL 
Week4Introduction to Python3
Week4Data Manipulation with Python3
Week5Data Manipulation with Python3
Week5Data Manipulation with Python3
Week6Data Visualization with Python3
Week6Descriptive Analytics with Python3
Week6Recap + Assignment discussion + Industry Guest Lecture3
Week7Practice Session + Instructor Guided Tutorials on Assignments3
 Badge: Python Programming for Data Science and ML 
Week8Introduction to Probability and Statistics3
Week8Hypothesis Testing - Part 13
Week9Hypothesis Testing - Part 23
Week9Hypothesis Testing Examples and Case Studies3
Week10Recap & Assignment discussion + Industry Guest Lecture3
Week10Practice Session + Instructor Guided Tutorials on Assignments3
 Badge: Statistics and Applications in Data Science 
Week11Introduction to Predictive Modeling - Linear Regression3
Week11Linear Regression - Case Study3
Week12Logistic Regression - Part 13
Week12Logistic Regression - Part 23
Week13Logistic Regression - Case Study3
Week13Time Series3
Week14Time Series3
Week14Time Series3
Week15Recap + Assignment discussion + Industry Guest Lecture3
Week15Practice Session + Instructor Guided Tutorials on Assignments3
 Badge: Statistical Modeling with Python 
Week16Introduction to R3
Week16Data Manipulation with R3
Week17Data Manipulation with R3
Week17Data Visualization with R3
Week18Statistical Modeling with R3
Week18Recap & Assignment discussion + Industry Guest Lecture3
Week19Practice Session + Instructor Guided Tutorials on Assignments3
 Badge: R for Data Science 
Week19Introduction to Machine Learning - Tree models and Data Wrangling3
Week20Clustering Models3
Week20PCA3
Week21Machine Learning - Random Forest3
Week21Segmentation and Tree Models Recap and Caselets3
Week22Recap + Assignment discussion + Industry Guest Lecture3
Week22Practice Session + Instructor Guided Tutorials on Assignments3
 Badge: Machine Learning Models with Python 
Week23Qualititative Data Analysis and Web Scraping3
Week23Introduction to Text Mining Concepts3
Week24Introduction to Text Mining Concepts3
Week25Introduction to Neural Networks3
Week25Introduction to Neural Networks3
Week26Understanding Cloud Computing and Cyber Security3
Week27Recap & Assignment discussion + Industry Guest Lecture3
 Badge: Advanced ML techinques 
Week27Storytelling with PowerBI3
Week28Storytelling with PowerBI3
Week28Storytelling with PowerBI3
Week29Recap & Assignment discussion + Industry Guest Lecture3
Week29Practice Session + Instructor Guided Tutorials on Assignments3
 Badge: Storytelling with PowerBI 
Week30Project Submissions and Viva16
Week33Final Certificate 

Note: A learner who successfully completes the Certificate Program on Data Science and Analytics for Business (DSAB) will be eligible to receive 15 academic credit points. The learner can apply these 15 credit points that they've earned against a proposed Master’s program in Data Science/Business Analytics in future with Shiv Nadar University (SNU).

Please Note: For those of you who are interested, we also give access to self-paced learning content around Big Data concepts.

Introduction to Big dataThe world of big data, parallel computing, history of Hadoop, The use cases of Hadoop in real-time
Introduction to Hadoop, HDFS and it's architectureHadoop distributed file system, Master slave architecture, Hadoop components

 

 
 
 
 
 
 
 
 
 
 
 
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