KITS Online Training Institute provide best Data Science online training by our highly professional and certified trainers. Data science is an interdisciplinary field about processes and systems to extract knowledge or insights from detain various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as statistics, machine learning, data mining, and predictive analytics, similar to Knowledge Discovery in Databases (KDD). We  also do corporate training’s and help the companies to train their employees. We also resolve many queries for the clients by providing real time support.

HighlightsData Science Online training:-

*  Very in depth course material with Real Time Scenarios for each topic with its Solutions for Data Science Online Trainings.

*  We Also provide Case studies  for Data Science Online Training.

*  We do Schedule the sessions based upon your comfort by our Highly Qualified Trainers and Real time Experts.

*  We provide you with your recorded session for further Reference.

* We also provide Normal Track, Fast Track and Weekend Batches also for Data Science Online Training.

* We also provide Cost Effective and Flexible Payment Schemes.


We are also provide related Course of Data Science with R Programming Online Training.

Data Science Online Training Course Content

Module:1 – Descriptive & Inferential Statistics

1. Turning Data into Information

• Data Visualization
• Measures of Central Tendency
• Measures of Variability
• Measures of Shape
• Covariance, Correlation
• Using Software-Real Time Problems

2.Probability Distributions

• Probability Distributions: Discrete Random Variables
• Mean, Expected Value
• Binomial Random Variable
• Poisson Random Variable
• Continuous Random Variable
• Normal distribution
• Using Software-Real Time Problems

3.Sampling Distributions

• Central Limit Theorem
• Sampling Distributions for Sample Proportion, p-hat
• Sampling Distribution of the Sample Mean, x-bar
• Using Software-Real Time Problems

4.Confidence Intervals

• Statistical Inference
• Constructing confidence intervals to estimate a population Mean, Variance, Proportion
• Using Software-Real Time Problems

5.Hypothesis Testing

• Hypothesis Testing
• Type I and Type II Errors
• Decision Making in Hypothesis Testing
• Hypothesis Testing for a Mean, Variance, Proportion
• Power in Hypothesis Testing
• Using Software-Real Time Problems

6.Comparing Two Groups

• Comparing Two Groups
• Comparing Two Independent Means, Proportions
• Pairs wise testing for Means
• Two Variances Test(F-Test)
• Using Software-Real Time Problems

7. Analysis of Variance (ANOVA)

• One-Way and Two-way ANOVA
• ANOVA Assumptions
• Multiple Comparisons (Tukey, Dunnett)
• Using Software-Real Time Problems

8.Association Between Categorical Variables

• Two Categorical Variables Relation
• Statistical Significance of Observed Relationship / Chi-Square Test
• Calculating the Chi-Square Test Statistic
• Contingency Table
• Using Software-Real Time Problems

Module:2 – Applied Regression Methods

  1. Simple Linear Regression(SLR)
  • Prerequisite Mathematics
  • The Simple Linear Regression Model
  • What is The Common Error Variance?
  • The Coefficient of Determination
  • Hypothesis Test for the Population Correlation Coefficient
  • Using Software-Real Time Problems
  1. SLR Model Evaluation
  • Inference for the Population Intercept and Slope
  • The Analysis of Variance (ANOVA) table and the F-test
  • Equivalent linear relationship tests
  • Decomposing the Error
  • The Lack of Fit F-test
  • Using Software-Real Time Problems
  1. SLR Estimation & Prediction
  • Confidence Interval for the Mean Response
  • Prediction Interval for a New Response
  • Using Software-Real Time Problems
  1. SLR Model Assumptions
  • Model Assumptions Diagnostics
  • Using Software-Real Time Problems
  1. Multiple Linear


  • The Multiple Linear Regression Model
  • Using Software-Real Time Problems
  1. MLR Model Evaluation
  • The General Linear Test
  • Sequential (or Extra) Sums of Squares
  • The Hypothesis Tests for the Slopes
  • Partial R-squared
  • Lack of Fit Testing in the Multiple Regression Setting
  • Using Software-Real Time Problems
  1. MLR Estimation, Prediction & Model Assumptions
  • Confidence Interval for the Mean Response
  • Prediction Interval for a New Response
  • Model Assumptions Diagnostics
  • Using Software-Real Time Problems
  1. Categorical Predictors
  • Coding Qualitative Variables
  • Additive Effects
  • Interaction Effects
  • Using Software-Real Time Problems
  1. Data Transformations
  • Using Software-Real Time Problems
  1. Model Building
  • Forward Selection/Backward Elimination
  • Stepwise Regression
  • Adjusted R-Sq, Mallows Cp, PRESS, AIC, BIC, SBC, AICC
  • Outliers and Influential Data Points
  • Cooks Distance/DIFBETAS/DFFITS
  • Using Software-Real Time Problems

Module:3 – Applied Time Series Analysis

1. Time Series Basics

• Overview
• ACF and AR(1) Model

2. MA Models, PACF

• Moving Average Models (MA models)
• Using Software-Real Time Problems

3. ARIMA models

• Non-seasonal ARIMA
• Diagnostics
• Forecasting
• Using Software-Real Time Problem

4. Seasonal Models

• Seasonal ARIMA
• Identifying Seasonal Models
• Using Software-Real Time Problems

5. Smoothing and Decomposition Methods

• Decomposition Models
• Smoothing Time Series
• Using Software-Real Time Problems

6. Periodogram

• Periodogram
• Using Software-Real Time Problems

7. Regression with ARIMA errors; CCF; 2 Time Series

• Linear Regression Models with Autoregressive Errors
• CCF and Lagged Regressions
• Using Software-Real Time Problems

Module:4 – Machine Learning


• Application Examples
• Supervised Learning
• Unsupervised Learning

2.Regression Shrinkage Methods

• Ridge RegressionüLasso
• Using Software-Real Time Problems


• Logistic Regression
• Discriminant Analysis
• Nearest-Neighbor Methods
• Using Software-Real Time Problems

4. Tree-based Methods

• The Basics of Decision Trees
• Regression Trees
• Classification Trees
• Ensemble Methods
• Bagging, Boosting, Bootstrap, Random Forests
• Using Software-Real Time Problems

5. Neural Networks

• Introduction
• Single Layer Perceptron
• Multi-layer Perceptron
• Forward Feed and Backward Propagation
• Using Software-Real Time Problems

6.Support Vector Machine

• Support Vector Classifier
• Support Vector Machine
• SVMs with More than Two Classes
• Using Software-Real Time Problems

7.Dimension Reduction Methods

• Principal Components Regression (PCR)
• Partial Least Squares (PLS)
• Using Software-Real Time Problems

8.Association rules

• Market Basket Analysis
• Using Software-Real Time Problems

Module:5 – SAS/R Programming

1.Base SAS

• Working with SAS program syntax
• Examining SAS data sets
• Accessing SAS libraries
• Producing Detail Reports
• Sorting and grouping report data
• Enhancing reports
• Formatting Data Values
• Creating user-defined formats
• Reading SAS Data Sets
• Customizing a SAS data set
• Handling missing data
• Manipulating Data
• Combining SAS Data Sets
• Creating Summary Reports
• Controlling Input and Output
• Summarizing Data
• Reading Raw Data Files
• Data Transformations
• Debugging Techniques
• Using the PUTLOG statement
• Processing Data Iteratively
• Restructuring a Data Set
• Creating and Maintaining Permanent Formats


• Working with SAS program syntax
• Basic Queries
• Examining SAS data sets
• Sub-Queries
• Accessing SAS libraries
• Joins (SQL)
• Producing Detail Reports
• Operators
• Sorting and grouping report data
• Creating Tables and Views
• Enhancing reports
• Managing Tables
• Formatting Data Values

3. SAS Macros

• Creating user-defined formats
• Macro Variables
• Reading SAS Data Sets
• Definitions
• Customizing a SAS data set
• Data Step and SQL Interfaces
• Handling missing data

4. R Programming

• Manipulating Data
• RCMDR Package
• Combining SAS Data Sets
• Rattle Package
• Creating Summary Reports

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