Big Data Analytics

22,000.00 18,000.00

Big data analytics is the process of examining large and varieddata sets — i.e., big data — to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions.

SKU: CFL-111 Category:

Program Objectives

Impart an understanding of Big Data Predictive Analytics concepts, terms and basic and advanced techniques​​ Equip with skills to analyze and interpret data, recognize patterns, derive insights and make conclusion

Who should attend?

Candidates desirous of acquiring Big data Predictive Analytics competencies
Graduates or Diploma holders with or without work experience

The certificates (Participation and Course Completion) shall be issued by “APPIT Software Solutions Private Limited”


Once you are successfully through the project (Reviewed by a Just7to9 expert), you will be awarded with just7to9’s Big Data Analytics Expert certificate.


Big Data Analytics

Course Outline
Module 1 :Introduction

Big Data Predictive Analytics Overview
Types of Methods
Business Value of Analytics
Application Areas

Module 2 : Preparing and Collecting Data

  1. Objectives
  2. Types of Data
  3. Samples Vs Population
  4. Sampling Methodology
  5. Types of Sampling
  6. Sample Size calculation (Variable)
  7. Sample Size Calculation (Discrete)

Module 3 : Descriptive Statistics

  1. Methods
  2. Graphical representation
  3. Application Areas
    1. Understanding central and variation estimate
    2. Central Limit Theorem
    3. Analyzing Using SPSS
  4. Probablitic Distributions (Normal and non-normal)
    1. Bell curve
    2. Skewed
    3. Bi-modal / probabilistic
    4. Hypergeometric
  5. Probability Estimates

Module 4 : Analytics and Inferential Statistics

  1. Hypothesis Formulation
  2. Point and Interval Estimates
  3. Confidence levels and Intervals
  4. Analytics
    1. Univariate Analysis
    2. Bivariate Analysis
  • Multivariate Analysis
  1. Statistical Treatment and identifying suitable statistical Test
  2. Parametric Hypothesis (Means – One and Two Samples)
  3. Parametric Hypothesis (Median – One and Two samples)
  4. Non-Parametric Hypothesis (Proportions – One and Two Samples)
  5. Non-Parametric Hypothesis (Median – One and Two samples)
  6. Testing Hypothesis (Means – Multiple Samples)
  7. Testing Hypothesis (Variance – Multiple Samples)
  8. Non-Parametric Hypothesis (Proportion – Multiple Samples)
  9. Non-Parametric Hypothesis (Median – Multiple Samples)

Module 5 : Predictive Analytics

  1. Time Series Forecasting – Naive, Moving Average, Exponential Smoothing, ARIMA, Seasonality and Box-Jenkins Models
  2. Regression – Linear
  3. Regression – Non-Linear (Polynomial, Lograthmic, Exponential, Power)
  4. Regression – Logistic
  5. Regression Hypothesis testing
  6. Classification – Decision Trees
  7. Classification, Naive Bayes
  8. Classification – k-Nearest Neighbours (k-NN)
  9. Classification – Neural Networks (ANN)
  10. Cluster Analysis (k-Means)
  11. Association Rule Mining
  12. Model Identification, Estimation and Assessment