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PCP in Business Analytics

Program Highlights:
Praxis Certification, Placement Support, Tech Update Sessions - 1 Yr, Real Time Internships, Industry Projects

E 3

PCP in Business Analytics

This Professional Certificate Program in Business Analytics – designed by veterans in the Analytics industry; helps to establish a decent career in the growing Data and Analytics domain. This uniquely blended Program is brought to by Praxis, a Top-ranked Analytics B-School in India.

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INR 35,000

Program Summary

  • 20 credits
    Credits

    With this course, you are 5 credits short of an assured placement.

    Learn more
  • Duration 6 months
  • 70 Hours of projects/Assignments
  • 146 Hours of online sessions

Course Topics

  • 1

    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

    • 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
  • 2

    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

  • 3

    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

  • 4

    Text Analytics

    • Basics of text analysis processes

      • Annotators
      • analysis results
      • feature structure
      • type
      • type system
      • annotation
      • common analysis structure
      • Web crawling
    • Web crawling

    • Web Scraping from downloaded html files

    • Text classification

    • Singular Value decomposition concept

    • Latent Semantic Analysis

    • Document clustering

    • Topic Modeling

    • Class Assignments

    • Presentation

  • 5

    Web Analytics

    • Introduction to Digital Media Analytics

    • Introduction to Google Analytics

    • Concept of Account, Property and View

    • Concept of Dimension, Metric and Segment

    • Reading a Google Analytics Report

    • Audience Analytics

    • Acquisition Analytics

    • Behaviour Analytics

    • Real-Time Analytics

    • Setting Up and Analysing Events

    • Intelligent Events

    • Setting Up and Analysing Experiments

    • Setting Up and Measuring Conversion Goals

    • Attribution Modelling

    • Segment Reporting

    • Designing Custom Reports

    • Introduction to Google Adwords

    • Search Marketing

    • Display Marketing

    • Google Adwords Analytics

    • Managing a Google Analytics Account

  • 6

    Data Visualization with Tableau

    • Need for visualizing data

      • Same dataset, different interpretation
      • Read texts well, not numbers
      • Brain processes visuals by short circuiting brain’s pathways
      • Quicker conclusions | Speed
    • Research methodologies

      • Problem Formulation
      • Literature review
      • Methodology
      • Analysis
      • Finding and Interpretation
      • Suggestion
      • Conclusion
      • Bibliography
    • Importance of Big data visualization

      • Traditional Visualization
      • Big Data Visualization
    • Tableau product offerings

    • Installation of Tableau Public

    • Working with Tableau - Live Case study/Discussion

    • Creating interactive dashboards with Tableau Public

    • Case study discussion

      • HR – Case Study with Data
    • Story Boarding with Tableau Public

    • Case study discussion

    • Geomapping in Tableau

      • Create a geographic hierarchy
      • Build a basic map
      • Change from points to polygons
      • Add visual detail
      • Add labels
      • Customize your background map
    • Qlik view – Basics

      • Download QlikView Personal Edition and Install
    • Google charts – Basics

      • Creating a simple Google Chart with in data
      • Image generation, Line, bar, and pie charts.
      • Scatter plot
      • Google-o-meter
      • Map,Radar,Venn diagram
      • Specification of attributes
    • Dynamic charts with Google Docs

      • Using Google Docs as database to store graphical data
      • Specifying the range of data and selecting columns
      • Creating an interactive Google Chart with Google Docs data
    • Supplementary material & Case study discussion

    • Closing session & Queries

  • 7

    Statistics with R

    • Introduction to Data

      • Data Basics
      • Overview of data collection principles
      • Experiments - Principles of experiment design
      • Examining Numerical and Categorical data
      • Comparing numerical data across groups
    • Introduction to Probability

      • Introduction
      • Conditional probability
      • Bayes’ Rule
    • Distributions

      • Discrete Distributions
      • Continuous Distributions
    • Introduction to linear regression

      • Correlation
      • Line fitting
      • Fitted values
      • Residuals
      • Basic introduction to multiple regression
    • Foundations for inference and estimation

      • Variability in estimates
      • Sampling distribution
      • Confidence intervals
      • Margin of error and ascertaining a sample size
    • Foundations for inference and hypothesis testing

      • Nearly normal population with known SD
      • Hypothesis testing framework
      • Two Tailed and One Tailed tests
      • Testing hypothesis using confidence intervals and Critical Z values
      • One-sample means with the t distribution with unknown population SD
      • Inference for a single proportion
      • Decision errors (Type 1 and 2)
      • Hypothesis testing using p-values
      • Choosing a significance level
      • Power and the type 2 error rate
    • Linear Regression and Multiple Regression

      • Introduction to F-statistic
      • Hypothesis Tests
      • Intervals
      • Coefficient of Multiple Determination
      • Interpreting the model output
  • 8

    Python

    • Understanding Basics of Python

    • Control Structures and for loop

    • Playing with while loop | break and continue

    • Strings and files

    • List

    • Dictionary and Tuples

  • 9

    Web Analytics

    • Introduction to Digital Media Analytics

    • Introduction to Google Analytics

    • Concept of Account, Property and View

    • Concept of Dimension, Metric and Segment

    • Reading a Google Analytics Report

    • Audience Analytics

    • Acquisition Analytics

    • Behaviour Analytics

    • Real-Time Analytics

    • Setting Up and Analysing Events

    • Intelligent Events

    • Setting Up and Analysing Experiments

    • Setting Up and Measuring Conversion Goals

    • Attribution Modelling

    • Segment Reporting

    • Designing Custom Reports

    • Introduction to Google Adwords

    • Search Marketing

    • Display Marketing

    • Google Adwords Analytics

    • Managing a Google Analytics Account

  • 10

    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

Industry Connect

  • G Infotech Logo

    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

    Infodrive Analytics

    very good on trainings

    -G.Karpagavalli, Sr Manager - HR

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