Post Graduate Program In Data Science And Machine Learning - Placement Assured

Can be taken anytime
Course Type
Postgraduate Course
Course Fee
$1,298 $1,104 only

Course Overview

This Post Graduate Program in Data Science and Machine Learning has a perfect blend of Technology, Data Science and Business cases and insights; it stands out to be among the best in the world. It is imperative that career seekers grab this opportunity.This uniquely blended Program is brought to you by Praxis, a Top-ranked Analytics B-School in India. Post Graduate Program in Data Science is one of the key requisites in any large organization. The time is at its best for someone to take up a career in this domain. Enormous opportunities and extreme dearth in getting candidates force large organizations go helter-skelter. It is imperative that career seekers grab this opportunity.

Who should take this course

This course is designed for the following professionals:

  • Entry to senior techical level managers
  • Senior techinical leaders

Course content

Learners are required to complete 12-15 months of faculty led Online mode.

The following are the modules covered:

Big Data 101 - Big Data Characteristics - Big Data and Business - Data Relationships and Data Model - Data Grouping - Clustering Algorithms - Getting ready for Clustering Algorithms - Clustering Algorithms - UPGMA, single Link Clustering - KPIs, Businesses & Data Elements - Mapping for business outcomes - Basic Query - Advanced Query - Embedding - Mathematics Modelling - Introduction to key mathematical concepts - Application of eigenvalues and eigenvectors - Application of the graph Laplacian - Application of PCA and SVD - Coding in DB Environment - Making Data Sets

Statistics 101 - Introduction to Statistics - Introduction to Statistics - II - Measures of Central Tendency, Spread and Shape - I - Measures of Central Tendency, Spread and Shape - II - Measures of Central Tendency, Spread and Shape - III

R Programming - R Programming - Introduction to R - I - Introduction to R - II - Common Data Structures in R - Conditional Operation and Loops - Looping in R using Apply Family Functions - Creating User Defined Functions in R - Graphics with R - Advanced Graphics with R

Hadoop - Introduction to Big Data and Hadoop - Introduction to DBMS systems using MySQL - Big Data and Hadoop EcoSystem - HDFS - Unix & HDFS Hands-on - Map-Reduce basics - Map Reduce Advanced Topics and Hands on - Pig introduction and Hands on - Pig Scripting - Hive Introduction, Metastore, Limitations of Hive - Comparison with Traditional Database and HIVE scripting - Hive Data Types, Partitioning and Bucketing - Hive Tables (Managed and External) - Hive Continued - Scoop Introduction and Hands-on - Introduction to NoSql and HBASE - HBASE architecture and Hands-on - Access Methods - Big Data with Spark and Python

  • Python
  • Understanding Basics of Python
  • Control Structures and for loop
  • Playing with while loop | break and continue
  • Strings and files
  • List
  • Dictionary and Tuples

Data Mining 1 - Machine Learning with R & Python - Introduction to NumPy - Introduction to Pandas - Slicing Data - Exploratory Data Analysis - Exploratory Data Analysis (Continue) - Missing Value Imputation and Outlier Analysis - Linear Regression Motivation - Linear Regression optimization objective - Linear Regression in Python - Introduction to Regression Tree - Introduction to Classification Tree - Measures of Selecting the best Split - Cluster Analysis - Hierarchical Clustering & k-Means Clustering - Customer segmentation in Telecom Industry using Cluster Analysis - k-Means clustering - Association Rules mining - Market Basket Analysis

Data Mining 2 - Advanced Machine Learning with R & Python - Sources of Error (Irreducible error, bias and variance) - Formally defining the 3 Sources of Error - Linear Regression - Multicollinearity (VIF) - Qualitative Predictors - Use of Dummy Variables - Observing overfitting in Polynomial Regression - Regularized Regression (L2 - Regularization) - To avoid overfitting - Regularized Regression (L1 - Regularization) - Feature selection using regularization - Regularized Regression - How does regularized regression handles multicollinearity? - Decision Tree - Pruning - Bagging Models - Designing your own Bagged Model - Random Forest - Boosting (Ada Boost) - K Nearest Neighbour - Concept. kNN algorithm for k=1 and k>1 - Writing a K Nearest Neighbour algorithm from scratch - Comparison of kNN with Linear Regression; Difference between kNN and kMeans. - Revision of basics of Linear Algebra - The Theory of dimension reduction - Practical - Compressing an image file [Practical using R Software] - Practical - Compressing an image file [Practical using R Software] (Continue)

RDBMS with SQL and DWH - Introduction to DBMS / RDBMS - Data Modelling - Physical Data Model - Getting Started with SQL Lite - DDL - DML - Introduction to Data Warehousing - Dimensional Modelling - Advanced SQL - Olap Cubes - Olap Cubes Practicals - Artificial Intelligence & Deep Learning - Industry Practices

About Course Provider

361DM is a learning science and digital education-oriented company run by professionals with over 2 decades of experience in the tech-enabled learning industry. 361DM works with corporate, institutions, universities across the world, and has designed and delivered learning programs to over 100000 students and executives in the last decade. We help higher education institutions and corporate organizations architect a robust digital strategy.