Last Enrollment Date : 30th September'21

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  • Post Graduate Program in Data Science and Machine Learning
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    INR 59,600

    No Cost EMI of Rs. 4950 per month learn more

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    Program Summary

    • 30 credits
    • Duration 1 Year
    • 212 Hours of online sessions
    • 90 Hours of projects/Assignments

    Post Graduate Program in Data Science and Machine Learning

    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. This uniquely blended Program is brought to you by Praxis, a Top-ranked Analytics B-School in India.

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    Modules

    Big Data 101

    Big Data Characteristics

    • Volume
    • Variety
    • Velocity
    • Veracity
    • Valence
    • Value

    Big Data and Business

    Data Relationships and Data Model

    • One-to-one relationship
    • One-to-many relationship
    • Many-to-many relationship
    • Flat model
    • Hierarchical model
    • Network model
    • Relational model
    • Star schema model
    • Data vault model

    Data Grouping

    Clustering Algorithms

    • partitioning
    • hierarchical
    • grid based
    • density based
    • model based

    Getting ready for Clustering Algorithms

    Clustering Algorithms – UPGMA, single Link Clustering

    KPIs, Businesses & Data Elements

    Mapping for business outcomes

    • Define the pain point
    • Define the goal
    • Identify the actors
    • Identify the impacts
    • Identify the deliverables
    • Creating your impact map

    Basic Query

    Advanced Query – Embedding

    Mathematics Modelling

    Introduction to key mathematical concepts

    • eigenvalues and eigenvectors

    Application of eigenvalues and eigenvectors

    • investigate prototypical problems of ranking big data

    Application of the graph Laplacian

    • investigate prototypical problems of clustering big data

    Application of PCA and SVD

    • investigate prototypical problems of big data compression

    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

    Industry Connect - See what experts say about this program

    • G Infotech

      We are elated by the program methodology, content, people and the platform of 361 DM which instills confidence in the quality of candidates emerging out of this program. As a techprenur, I look forward for such candidates who could partner in our growth


      -Praveen, Director, G Infotech

      Aaum Analytics

      This product is endorsed by Aaum Analytics.A company specialised in analytics with strong focus on research and technology

      CapGemini

      As an Industry person with over 20 years of experience,I have witnessed multiple training programmes and training providers.This program of 361DM stands out from all of them for the expertise of professionals delivering, the quality of the content and the engaging model of the platform.Truly Enriching!


      - VijayKumar, Senior Manager, CapGemini

      Infodrive Analytics

      Very good on trainings

      -G.Karpagavalli, Sr Manager - HR

      Code mantra

      GOOD Keep going...would like to connect with your institute for placements..

      -Saranya, Senior HR Executive

      Mitosis Technologies

      very good on trainings

      -Sathiyan Sivaprakasam, CEO

    About Praxis

    Praxis Business School, Kolkata, is a premier B-School whose courses are rated among India’s top two in Big Data and Analytics domain. It is one of the most trusted and influential management education institutions in India. Praxis Business School is motivated by the desire to generate business professionals who can partake in and add to the economic development of the country.

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    Mentors

    Testimonials

    Course Highlights

    Key Features of the Program

    Outcome of the Program

    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.

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