Data Science Course Program

Data Science Course prepares you for the Data Science Certification exam and for the role of Data Scientist by making you an expert in Statistics, Analytics, Data Science, Big Data, AI, Machine Learning and Deep Learning. The Data science master program has been designed by the industry expert to help you master data mining, management, exploration, and carry out several industry-relevant projects. Enroll & Get Certified now!

  • ✔ Course Duration : 56 hrs
  • ✔ Training Options : Live Online / Self-Paced / Classroom
  • ✔ Certification Pass : Guaranteed

Why Learn Data Science?

Data science is a “concept to unify statistics, data analysis, machine learning & their related methods” in order to “understand & analyze actual phenomena” with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning, data mining, databases, and visualization. Data Science Turing award winner Jim Gray imagined data science as a ” fourth paradigm” of science (empirical, theoretical, computational and now data-driven) and asserted that ” everything about science is changing because of the impact of information technology” and the data deluge.

What you will Learn

Data science course program will help you master skills and tools like Statistics, Hypothesis testing, Clustering, Decision trees, Linear and Logistic regression, R Studio, Data Visualization, Regression models, Hadoop, Spark, PROC SQL, SAS Macros, Statistical procedures,Advanced analytics, Matplotlib, Excel analytics functions, Hypothesis testing, Zookeeper, Kafka interfaces. You will master TensorFlow, Machine Learning, and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems. These skills will help you prepare for the role of a Data Scientist.


Following are some prerequisites to learn Anaplan:

1. Fundamentals of Python programming
2. Basic knowledge of statistics

If you don’t know about python, statistics, machine learning etc. don’t worry we will provide training for that to you so, that you can analyze and statistical techniques, it will take hardly 15-20 hours.


Learning Objectives: Analytics Overview Introduction
Introduction to Business Analytics
Types of Analytics
Areas of Analytics
Analytical Tools
Analytical Techniques Introduction to SAS Introduction
What is SAS
Navigating in the SAS Console
SAS Language Input Files
PROC Step and DATA Step
DATA Step Processing
SAS Libraries
Importing Data
Exporting Data Combining and Modifying Datasets Introduction
Why Combine or Modify Data
Concatenating Datasets
Interleaving Method
One – to – one Reading
Data Manipulation
Modifying Variable Attributes PROC SQL Introduction
What is PROC SQL
Retrieving Data from a Table
Selecting Columns in a Table
Retrieving Data from Multiple Tables
Selecting Data from Multiple Tables
Concatenating Query Results
Activity SAS Macros Introduction
Need for SAS Macros
Macro Functions
Macro Functions Examples
SQL Clauses for Macros
The % Macro Statement
The Conditional Statement Basics of Statistics Introduction to Statistics
Statistical Terms
Procedures in SAS for Descriptive Statistics
Descriptive Statistics
Hypothesis Testing
Variable Types
Hypothesis Testing
Parametric and Non – parametric Tests
Parametric Tests
Non – parametric Tests
Parametric Tests – Advantages and Disadvantages Statistical Procedures Introduction o Statistical Procedures
PROC Means
PROC REG Options
PROC ANOVA Data Exploration Introduction
Data Preparation
General Comments and Observations on Data Cleaning
Data Type Conversion
Character Functions
SCAN Function
Date/Time Functions
Missing Value Treatment
Various Functions to Handle Missing Value
Data Summarization Advanced Statistics Introduction
Introduction to Cluster
Clustering Methodologies
K Means Clustering
Decision Tree
Logistic Regression Working with Time Series Data Introduction
Need for Time Series Analysis
Time Series Analysis — Options
Reading Date and DDateTimeValues
White Noise Process
Stationarity of a Time Series
Stages of ARIMA Modelling
Transform Transpose and Interpolating Time Series Data Designing Optimization Models Introduction
Need for Optimization
Optimization Problems

Learning Objectives: Introduction to Business Analytics Introduction
Need of Business Analytics
Business Decisions
Introduction to Business Analytics
Features of Business Analytics
Types of Business Analytics
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
Supply Chain Analytics
Health Care Analytics
Marketing Analytics
Human Resource Analytics
Web Analytics
Application of Business Analytics
Business Decisions
Business Intelligence (BI)
Data Science
Importance of Data Science
Data Science as a Strategic Asset
Big Data
Analytical Tools Introduction to R Introduction
An Introduction to R
Comprehensive R Archive Network (CRAN)
Cons of R
Companies Using R
Understanding R
Installing R on Various Operating Systems
Installing R on Windows from CRAN Website
Install R
IDEs for R
Installing RStudio on Various Operating Systems
Install R-Studio
Steps in R Initiation
Benefits of R Workspace
Setting the Workplace
Functions and Help in R
Access the Help Document
R Packages o Installing an R Package
Install and Load a Package R Data Structure Introduction
Types of Data Structures in R
Create a Vector
Colon Operator
Accessing Vector Elements
Accessing Matrix Elements
Create a Matrix
Accessing Array Elements
Create an Array
Data Frames
Elements of Data Frames
Create a Data Frame
Create a Factor
Create a List
Importing Files in R
Importing an Excel File
Importing a Minitab File
Importing a Table File
Importing a CSV File
Read Data from a File
Read Data from a File
Exporting Files from R Apply Functions Introduction
Types of Apply Functions
Apply() Function
Lapply() Function
Sapply() Function
Tapply() Function
Vapply() Function
Mapply() Function
Dplyr Package
Installing the Dplyr Package
Functions of the Dplyr Package
Functions of the Dplyr Package – Select()
Use the Select() Function
Functions of Dplyr-Package – Filter()
Use the Filter() Function
Use Select Function
Functions of Dplyr Package – Arrange()
Use Arrange Function
Functions of Dplyr Package – Mutate()
Functions of Dply Package – Summarise()
Use Summarise Function Data Visualization Introduction
Graphics in R
Types of Graphics
Bar Charts
Creating Simple Bar Charts
Editing a Simple Bar Chart
Create a Stacked Bar Plot and Grouped Bar Plot
Pie Charts
Editing a Pie Chart
Create a Pie Chart
Creating a Histogram
Kernel Density Plots
Creating a Kernel Density Plot
Create Histograms and a Density Plot
Line Charts
Creating a Line Chart
Box Plots
Creating a Box Plot
Create Line Graphs and a Box Plot
Heat Maps o Creating a Heat Map
Create a Heatmap
Word Clouds
Creating a Word Cloud
File Formats for Graphics Outputs
Saving a Graphic Output as a File
Save Graphics to a File
Exporting Graphs in RStudio
Exporting Graphs as PDFs in RStudio
Save Graphics Using RStudio Introduction to Statistics Introduction
Basics of Statistics
Types of Data
Qualitative vs. Quantitative Analysis
Types of Measurements in Order
Nominal Measurement
Ordinal Measurement
Interval Measurement
Ratio Measurement
Statistical Investigation
Normal Distribution
Example of Normal Distribution
Importance of Normal Distribution in Statistics
Use of the Symmetry Property of Normal Distribution
Standard Normal Distribution
Use Probability Distribution Functions
Distance Measures
Distance Measures – A Comparison
Euclidean Distance
Example of Euclidean Distance
Manhattan Distance
Minkowski Distance
Mahalanobis Distance
Cosine Similarity
Correlation Measures Explained
Pearson Product Moment Correlation (PPMC)
Pearson Correlation
Dist() Function in R
Perform the Distance Matrix Computations Hypothesis Testing I Introduction
Need of Hypothesis Testing in Businesses
Null Hypothesis
Alternate Hypothesis
Null vs. Alternate Hypothesis
Chances of Errors in Sampling
Types of Errors
Contingency Table
Decision Making
Critical Region
Level of Significance
Confidence Coefficient
Bita Risk
Power of Test
Factors Affecting the Power of Test
Types of Statistical Hypothesis Tests
Upper Tail Test
Test Statistic
Factors Affecting Test Statistic
Critical Value Using Normal Probability Table Hypothesis Testing II Introduction
Parametric Tests
Z-Test in R
T-Test in R
Use Normal and Student Probability Distribution Functions
Testing Null Hypothesis
Objectives of Null Hypothesis Test
Three Types of Hypothesis Tests
Hypothesis Tests About Population Means
Decision Rules
Hypothesis Tests About Population Means
Hypothesis Tests About Population Proportions
Chi-Square Test
Steps of Chi-Square Test
Degree of Freedom
Chi-Square Test for Independence
Chi-Square Test for Goodness of Fit
Chi-Square Test for Independence
Chi-Square Test in R
Use Chi-Squared Test Statistics
Introduction to ANOVA Test
One-Way ANOVA Test
The F-Distribution and F-Ratio
F-Ratio Test
F-Ratio Test in R
One-Way ANOVA Test
One-Way ANOVA Test in R
Perform ANOVA Regression Analysis Introduction
Introduction to Regression Analysis
Use of Regression Analysis
Types Regression Analysis
Simple Regression Analysis
Multiple Regression Models
Simple Linear Regression Model
Perform Simple Linear Regression
Correlation Between X and Y
Find Correlation
Method of Least Squares Regression Model
Coefficient of Multiple Determination Regression Model
Standard Error of the Estimate Regression Model
Dummy Variable Regression Model
Interaction Regression Model
Non-Linear Regression
Non-Linear Regression Models
Perform Regression Analysis with Multiple Variables
Non-Linear Models to Linear Models
Algorithms for Complex Non-Linear Models Classification Objectives
Introduction to Classification
Examples of Classification
Classification vs. Prediction
Classification System
Classification Process
Classification Process – Model Construction
Classification Process – Model Usage in Prediction
Issues Regarding Classification and Prediction
Data Preparation Issues
Evaluating Classification Methods Issues
Decision Tree
Decision Tree – Dataset
Classification Rules of Trees
Overfitting in Classification
Tips to Find the Final Tree Size
Basic Algorithm for a Decision Tree
Statistical Measure – Information Gain
Calculating Information Gain for Continuous-Value Attributes
Enhancing a Basic Tree
Decision Trees in Data Mining
Model a Decision Tree
Naive Bayes Classifier Model
Features of Naive Bayes Classifier Model
Bayesian Theorem
Naive Bayes Classifier
Applying Naive Bayes Classifier
Naive Bayes Classifier – Advantages and Disadvantages
Perform Classification Using the Naive Bayes Method
Nearest Neighbor Classifiers
Computing Distance and Determining Class
Choosing the Value of K
Scaling Issues in Nearest Neighbor Classification
Support Vector Machines
Advantages of Support Vector Machines
Geometric Margin in SVMs
Linear SVMs
Non-Linear SVMs
Support a Vector Machine Clustering Introduction
Introduction to Clustering
Clustering vs. Classification
Use Cases of Clustering
Clustering Models
K-means Clustering
K-means Clustering Algorithm
Pseudo Code of K-means
K-means Clustering Using R
K-means Clustering
Perform Clustering Using K-means
Hierarchical Clustering
Hierarchical Clustering Algorithms
Requirements of Hierarchical Clustering Algorithms
Agglomerative Clustering Process
Perform Hierarchical Clustering
DBSCAN Clustering
Concepts of DBSCAN
DBSCAN Clustering Algorithm
DBSCAN Clustering Association Introduction
Association Rule Mining
Application Areas of Association Rule Mining
Parameters of Interesting Relationships
Association Rules
Association Rule Strength Measures
Limitations of Support and Confidence
Apriori Algorithm
Applying Apriori Algorithm
Step 1 – Mine All Frequent Item Sets
Algorithm to Find Frequent Item Set
Ordering Items
Candidate Generation
Step 2 – Generate Rules from Frequent Item Sets
Perform Association Using the Apriori Algorithm
Perform Visualization on Associated Rules
Problems with Association Mining

Learning Objectives: Introduction to Big data and Hadoop Ecosystem Introduction
Overview to Big Data and Hadoop
Hadoop Ecosystem HDFS and YARN Introduction
HDFS Architecture and Components
Block Replication Architecture
YARN Introduction MapReduce and Sqoop Introduction
Why Mapreduce
Small Data and Big Data
Data Types in Hadoop
Joins in MapReduce
What is Sqoop Basics of Hive and Impala Introduction
Interacting with Hive and Impala
Working with Hive and Impala
Data Types in Hive
Validation of Data
What is Catalog and Its Uses Types of Data Formats Introduction
Types of File Format
Data Serialization
Importing MySql and Creating hive to
Parquet With Sqoop Advanced Hive Concept and Data File Partitioning Introduction
Overview of the Hive Query Language Apache Flume and HBase Introduction
Introduction to HBase Pig Introduction
Getting Datasets for Pig Development Basics of Apache Spark Introduction
Spark – Architecture, Execution, and Related Concepts
RDD Operations
Functional Programming in Spark RDDs in Spark Introduction
RDD Data Types and RDD Creation
Operations in RDDs
Implementation of Spark Applications Introduction
Running Spark on YARN
Running a Spark Application
Dynamic Resource Allocation
Configuring Your Spark Application Spark Parallel Processing Introduction
Parallel Operations on Partitions Spark RDD Optimization Techniques Introduction
RDD Persistence Spark RDD Optimization Techniques Spark Algorithm
Spark: An Iterative Algorithm
Introduction To Graph Parallel System
Introduction To Machine Learning
Introduction To Three C’s Spark SQL Introduction
Interoperating with RDDs Apache Kafka Core Java

Learning Objectives: Data Science Introduction to Data Science
Different Sectors Using Data Science
Purpose and Components of Python Data Analytics Data Analytics Process
Exploratory Data Analysis(EDA)
EDA-Quantitative Technique
EDA – Graphical Technique
Data Analytics Conclusion or Predictions
Data Analytics Communication
Data Types for Plotting
Data Types and Plotting Statistical Analysis and Business Applications Introduction to Statistics
Statistical and Non-statistical Analysis
Major Categories of Statistics
Statistical Analysis Considerations
Population and Sample
Statistical Analysis Process
Data Distribution
Dispersion o Histogram
Testing Python Environment Setup and Essentials Anaconda
Installation of Anaconda Python Distribution (contd.)
Data Types with Python
Basic Operators and Functions Mathematical Computing with Python (NumPy) Introduction to Numpy
Activity-Sequence it Right
Creating and Printing an array
Class and Attributes of array
Basic Operations
Activity-Slice It
Copy and Views
Mathematical Functions of Numpy Scientific computing with Python (Scipy) Introduction to SciPy
SciPy Sub Package – Integration and Optimization
SciPy sub package
Calculate Eigenvalues and Eigenvector
SciPy Sub Package – Statistics, Weave and IO Data Manipulation with Pandas Introduction to Pandas
Understanding DataFrame
View and Select Data
Missing Values
Data Operations
File Read and Write Support
Pandas Sql Operation Machine Learning with Scikit–Learn Machine Learning Approach
How it Works
Supervised Learning Model Considerations
Supervised Learning Models – Linear Regression
Supervised Learning Models – Logistic Regression
Unsupervised Learning Models
Model Persistence and Evaluation Natural Language Processing with Scikit Learn NLP Overview
NLP Applications
NLP Libraries-Scikit
Extraction Considerations
Scikit Learn-Model Training and Grid Search Data Visualization in Python using matplotlib Introduction to Data Visualization
Line Properties
(x,y) Plot and Subplots
Types of Plots Web Scraping with BeautifulSoup Web Scraping and Parsing
Understanding and Searching the Tree
Navigating options
Navigating a Tree
Modifying the Tree
Parsing and Printing the Document Python integration with Hadoop MapReduce and Spark Why Big Data Solutions are Provided for Python
Hadoop Core Components
Python Integration with HDFS using Hadoop Streaming
Using Hadoop Streaming for Calculating Word Count
Python Integration with Spark using PySpark
Using PySpark to Determine Word Count
Python Basics

Learning Objectives: Introduction to Business Analytics Introduction
What Is in It for Me
Types of Analytics
Areas of Analytics Formatting Conditional Formatting and Important Functions Introduction
What Is in It for Me
Custom Formatting Introduction
Conditional Formatting Introduction
Logical Functions
Lookup and Reference Functions
VLOOKUP Function
HLOOKUP Function
MATCH Function
INDEX and OFFSET Function
Statistical Function
SUMIFS Function
STDEV, MEDIAN and RANK Function Analyzing Data with Pivot Tables Introduction
What Is in It for Me
Pivot Table Introduction
Concept Video of Creating a Pivot Table
Grouping in Pivot Table Introduction
Custom Calculation
Calculated Field and Calculated Item
Slicer Intro
Creating a Slice
What Is in It for Me
What is a Dashboard
Principles of Great Dashboard Design
How to Create Chart in Excel
Chart Formatting
Thermometer Chart
Pareto Chart
Form Controls in Excel
Interactive Dashboard with Form Controls
Chart with Checkbox
Interactive Chart Business Analytics With Excel Introduction
What Is in It for Me
Concept Video Histogram
Concept Video Solver Addin
Concept Video Goal Seek
Concept Video Scenario Manager
Concept Video Data Table
Concept Video Descriptive Statistics Data Analysis Using Statistics Introduction
Moving Average
Hypothesis Testing
Normal Distribution Power BI Introduction
Power Pivot
Power View
Power Query
Power Map Microsoft Power BI Desktop Microsoft Power BI Recipes

Learning Objectives: Machine Learning Introduction Techniques of Machine Learning
Supervised Learning
Unsupervised Learning
Semi-supervised Learning and Reinforcement Learning
Some Important Considerations in Machine Learning
Data Preprocessing Data Preparation
Feature engineering
Feature scaling
Dimensionality reduction
Math Refresher Eigenvalues, Eigenvectors, and Eigendecomposition
Concepts of Linear Algebra
Introduction to Calculus
Probability and Statistics
Regression Regression and Its Types
Linear Regression: Equations and Algorithms
Classification Logistic regression
K-nearest neighbours
Support Vector Machines
Kernel SVM
Naive Bayes
Decision tree classifier
Random forest classifier
Unsupervised learning – Clustering K-Means Clustering
Clustering Algorithms
Introduction to Deep Learning Meaning and importance of deep learning
Artificial Neural networks
Introduction to Artificial Intelligence and Machine Learning Artificial Intelligence
Machine Learning o Machine Learning algorithms o Applications of Machine Learning
Python Programming for Beginners Python Django From Scratch

Learning Objectives: Introduction to Deep Learning with TensorFlow Introduction to TensorFlow
Intro to TensorFlow
Computational Graph
Key highlights
Creating a Graph
Regression example
Gradient Descent
Sharing Variables
Perceptrons What is a Perceptron
XOR Gate
Activation Functions Sigmoid
Hyperbolic Fns
Artificial Neural Networks Introduction
Perceptron Training Rule
Gradient Descent Rule
Gradient Descent and Backpropagation Gradient Descent
Stochastic Gradient Descent
Some problems in ANN
Optimization and Regularization Overfitting and Capacity
Feature Selection
Intro to Convolutional Neural Networks Intro to CNNs
Kernel filter
Principles behind CNNs
Multiple Filters
CNN applications
Intro to Recurrent Neural Networks Intro to RNNs
Unfolded RNNs
Seq2Seq RNNs
Deep Learning applications Image Processing
Natural Language Processing
Speech Recognition
Video Analytics


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