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    Post Graduate Program in Business Analytics and Visualization (PGP-BA&V)

    BY
    Amity Future Academy

    Brochure Enquire
    Mode

    Online

    Duration

    12 Months

    Fees

    ₹ 100000

    Inclusive of GST

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study, Virtual Classroom
    Mode of Delivery Video and Text Based
    Learning efforts 8-10 Hours Per Week

    Course and certificate fees

    Fees information
    ₹ 100,000  (Inclusive of GST)

    The fees for the course Post Graduate Program in Business Analytics and Visualization is -

    HeadAmount
    Programme feesRs. 1,00,000
    certificate availability

    Yes

    certificate providing authority

    Amity Future Academy

    The syllabus

    Module 1 – The Science of Data Driven Decision Making

    • Data and its classifications: Continuous & Discrete, Structured vs Unstructured
    • Fundamentals of R
    • Data manipulation
    • Data Visualization

    Module 2 – Fundamental Data Analysis to Real Business Problems

    • Describing and Summarizing Data
    • Sampling and Estimation
    • Hypothesis Testing
    • Single Variable Linear Regression
    • Multiple Regression

    Module 3 – Inferential Statistics

    • Measuring & Modelling uncertainty
    • Bayes Theorem
    • Probability and Inferential Statistics - I
    • Probability and Inferential Statistics - II
    • Discrete Random Variables vs Probability Distributions
    • Sampling and Estimation
    • Inferential Statistics & Examples of Hypothesis Testing
    • Analysis of Variance - ANOVA

    Module 4 – Supervised Learning Algorithms

    • Linear Regression
    • Logistic Regression
    • K-nearest Neighbor
    • Decision Tree
    • Random Forrest 

    Module 5 – Forecasting Techniques

    • Linear optimization
    • Optimal solution using Excel solver
    • Sensitivity Analysis
    • Time series analysis
    • Exponential smoothing
    • ARIMA modelling

    Module 6 – Optimization Analytics Techniques

    • Clustering
    • Prescriptive Analytics
    • Association Rule
    • Variables
    • Constraints
    • Objective function

    Module 7 – Dimension Reduction Techniques

    • Model Diagnosis
    • Outlier Analysis
    • Principal Component Analysis
    • Factor Analysis

    Module 8 – Ensemble Learning Techniques

    • Resampling method
    • K-fold cross validation method
    • Bagging and boosting
    • Gradient boosting
    • Bootstrapping

    Module 9 – Primer on Big Data and Artificial Intelligence

    • Data Mining
    • Data Mining with traditional tools and technologies
    • Data mining with Big Data and Advanced Techniques

    Instructors

    Mr Tushar Kakkaiya
    Instructor
    Freelancer

    Dr Suresh Varadarajan
    Instructor
    Freelancer

    Ph.D

    Dr Karthic Narayanan
    Instructor
    Freelancer

    Ph.D

    Mr Pranav Shastri
    Program Director
    Freelancer

    Ph.D

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