Post Graduate Program in Data Engineering (Machine learning)

with Placement Assurance---Duration : 6 Months

Data Engineering (Machine Learning)

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Data Science with IIKMBA

Post graduate program in Data Engineering (Machine Learning) with Placement Assurance

Evolved and designed by veterans in the Analytics industry, this program prepares students and working professionals to start or improve upon a career in the growing Data and Analytics domain. A perfect blend of technology, Data science and business cases and insights, the program stands out as among the best in the world. This is a career oriented program that covers all the key aspects of Data Engineering. A great feature is the flexibility in the program to assimilate and incorporate technology updates into the modules, on the fly. This program also comes with the benefit of Placement support from IBAX Minds though you would not have the need since opportunities galore when you do this program.


Duration : 6 Months

Class : Weekend Classroom

Eligibility : B.E / B.Tech (CSE, IT, EEE, ECE, E&I) from a recognized university

Course Fee: INR 200000/-

Modules : Big Data 101, Statistics 101, R Programming, Hadoop, Access Methods, Big Data with Spark and Python,Python, Data Mining 1 - Machine Learning with R, Data Mining 2 - Advanced Machine Learning with R, RDBMS with SQL and DWH


Course Outline

Big Data 101

Big Data Characteristics, Big Data and Business, Big Data Case Studies, Data Relationships and Data Model, Data Grouping, Clustering Algorithms, UPGMA Clustering Algorithm, Single Link Clustering Algorithm, KPIs and Businesses, KPIs and Data Elements, Mapping for business outcomes, Basic and Advanced Query.

Hadoop

Introduction to Big data and Hadoop, Hadoop Architecture, Hadoop Deployment, Hive - Introduction, Metastore, Hive data types, Partitioning and Bucketing, Mapreduce Framework , Hbase Architecture - Run models & Configuration, Hbase Cluster Deployment, Data Model, HBase Shell, Data Loading Techniques

R Programming

Introduction to R , Common Data Structures in R , Conditional Operation and Loops, Looping in R using Apply Family Functions , Creating User Definrd Functions in R , Graphics with R , Advanced Graphics with R

Data mining 1 / Machine learning with R

R,Loops in R, Concept of data structure in R, Creating Boolean index in R, Understanding Loops, R graphics, data clustering (k-means,hirerachial), Decision tree (C4.5 and CART), Concepts of Association Rule Mining, Building association rules and interpretation.

Data Mining 2 / Advanced machine learning with R

Clustering concept (DBSCAN, EM Clustering), Measures of cluster validity, Classification techniques( KNN, Naïve Bayesian, ANN,Ensembles,Random Forest) Sequential Pattern mining, Case Studies.

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 ,Measuring Association.

Access Methods

Sqoop,Flume,Pig,Zookeeper, Oozie and Apache Spark.

Big Data with Spark and Python

Python: Data Structure, Twitter Analysis and Analytics, Text Analytics, Hadoop. Spark: Machine Learning with Spark Case Studies: Python + Spark Project: Spark.

Python

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

RDBMS with SQL and DWH

Introduction to DBMS / RDBMS, Data Modelling - Entity Relationships Data Modelling - Normalization, Physical Data Model, Getting started with SQL lite, DDL (Creating Tables, Loading Data, Insert, Delete,Update) & DML, Data warehousing, Dimensional modeling.


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