Post-Graduate Diploma in Data Science

Fin Tech Overview
Program Overview

It is evident that Data Science is an upcoming domain which has been recognized as an important and obligatory practice to be adopted by almost all business organizations irrespective of their size and line of activities. A significant capability enhancement has taken place, particularly in the last decade regarding data or information acquisition. This has led to the need of analyzing the same and for taking out meaningful insights, then plugging those with decision-making and updating policies for better organizational functions.

Post-Graduate Diploma in Data Science will help professionals to succeed in any industry and teach them newer trends & improvise basic processes. There is practically no sector which has remained untouched from the reach of Data Science. Data Scientists, with the right set of skills and expertise, are in demand in a wide range of industries where businesses are realizing the potential of enabling analytics in their organization.

Though the demand for Data Scientists in our country and abroad is significantly high, there is a huge dearth of skilled professionals in the respective field. The Post-Graduate Diploma in Data Science has been conceptualized to prepare professionals in DataScience, who are keen to make a career in this exciting field.

Eligibility

Fresh Graduates and Experienced Working Professional interested in learning the different tools and techniques of data science and successful implementation of data science in the organization.

Duration

Two Semesters (One Year) with 16 weeks per Semester & 40 lectures per Semester.

Curriculum
Semester 1
  • Big Data & Hadoop
    • Introduction to Big Data Tools and Technologies
    • Introduction to Unix & PYTHON
    • Introduction to HDFS
    • Map-Reduce and its assignment
    • Introduction to Sqoop
    • HIVE
    • PIG
    • SPARK
    • SPARK + Python & Case Study (LOG ANALYSIS)
    • Oozie
    • HBASE/ MongoDb
    • Project I [Retail DOMAIN
    • Using all above tools
    • Project II: Banking sector
    • Project III Sentiment
    • Analytics
    • Introduction to R software
  • Business Analytics
    • Data and Basic Statistics
    • Linear Regression
    • Logistic Regression
    • Time Series Modelling - ARIMA
    • Market Mix Modelling
    • Decision Trees
    • Email Marketing Optimization
  • R Integration with Hadoop
Semester 2
  • Data Visualization
    • Getting data to data mart for faster visuals
    • Metabase open source, Apache superset open source visualizers
    • Plotting data based on classifications/ buckets, time, trending
    • Drill downs to provide more details
    • Building dashboards using individual charts
    • Implementing security on visual data visibility
    • Automatic alerting based on data dashboards
  • Machine Learning
    • Introduction: Statistical Decision Theory - Regression, Statistical Decision Theory -Classi cation, Bias Variance”
    • Linear Regression, Multivariate Regression, Subset Selection, Shrinkage Methods, Principal Component Regression, Partial Least squares
    • Linear Classi cation, Logistic Regression, LDA
    • Perceptron, SVM
    • Neural Networks - Introduction, Early Models, Perceptron Learning, Neural Networks - Backpropagation, Neural
    • Networks - Initialization, Training & Validation, Parameter Estimation
    • Decision Trees, Regression Tree, Decision Trees - Stopping Criterion & Pruning, Loss functions, Decision Trees - Categorical Attributes, Multiway Splits, Missing Values, Decision Trees - Instability, Example, Evaluation Measures-1”
    • Bootstrapping & Cross Validation, Class Evaluation Measures, ROC curve, MDL, Ensemble Methods - Bagging, Committee Machines and Stacking, Ensemble Methods - Boosting”
    • Gradient Boosting, Random Forests, Multi-class Classi cation, Naive Bayes, Bayesian Networks”
    • Undirected Graphical Models, HMM, Variable elimination, belief propagation
    • Partitional Clustering, Hierarchical Clustering, Birch Algorithm, CURE Algorithm, Density-based Clustering”
    • Gaussian Mixture Models, Expectation Maximization”
    • Learning Theory, Introduction to Reinforcement Learning + Optional videos (RL framewor and TD Learning, Solution Methods and Applications)
  • Case Study
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