Certificate Program in Data Science

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Why this course ?

  • If you're interested in the exciting world of data science, but don't know where to start, then this course is the beginning for you.
  • This course designed to introduce participant’s to this rapidly growing field and equip them with some of its basic principles and frequently used tools.
  • A data scientist requires skill sets spanning mathematics, statistics, machine learning and knowledge of data analytics software like Python, R and SAS.
  • To make the learning contextual, case studies from a variety of disciplines used in this course.

Program Duration
and Fees

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Duration

250 Hours

Price

69990

Features

  • Advantages of attending the training
    The classroom environment provides the important “human touch".
    Real-time answers
    Ask questions and get answers in real-time.Discuss, share, exchange ideas.
    Led by an expert instructor
    Expert instructor successfully mentor students to achieve growth and success.

A. PYTHON PROGRAMMING

Description

Introduction to python
Installation of Anaconda
Python Variable
Understanding more about Variable
User input variable
Calculate discounted price
Conditional Statements
Python String Functions
Python Numeric Function
Introduction to Loop
FOR loop for Sum
FOR loop for multiplication
While loop
Conditional Statement(if elif else)
More of if else elif
Introduction to python list
Indexing of Python List
slicing of list
modifying a list
List methods
Python Tuple
Functions on Tuple
Python Dictionary
Functions on dictionary
Methods of Dictionary
Python Set
Create your own functions
Classes in python
Inheritance in Python
Time management function
Introduction to Numpy
Array in Numpy
matrices using Numpy
mathematical fuctions using Numpy
Introduction to pandas
data frame in pandas
groupby in pandas

B. PYTHON MACHINE LEARNING

Description

Installation and Basic syntaxes of Python
Programming construct in Python: if-else statement, looping, functions in python
Basic object oriented programming in python
Python Packages: Numpy
Python Packages: Matplotlib
Python Packages: Pandas
Theory
Code from Scratch and using Python Library
Theory
Code from scratch and using python library
Nearest Neighbour Regression-Theory and Code along
Nearest Neighbour Classification-Theory and Code Along
SVM theory: Linear, Dual SVM and Kernel Trick
SVM code along
Theory
Code Along
Curse of dimensionality
Principal Component Analysis
Singular Value Decomposition
Independent Component Analysis
Fisher Linear Discriminant Analysis
k-Means Clustering: Theory and Code-along
Hierarchical Clustering: Theory and Code-along
Gaussian Mixture Model: Theory and Code-along

C. R PROGRAMMING

Description

Basic Operations in R
Different Data Types and Data Structures in R
Subsetting in R
Vectors
Logical Operator
If ELSE (Conditional Processing)
Loops
While loop
Functions
Create own function
Create matrices
Colnames() Rownames()
Matrix Operations
Subset matrices
Import Data
Operations on data frame
Application on data frame
Filter your data
Array and data manipulation
Plots and Charts in R
Merging and Sorting Functions in R
Summarising Data

D. R ANALYTICS

Description

What is R
What is S
History of R
Features of R
SAS versus R
Installing R
Packages
Input/output
R interfaces
R Library
Vector
Lists & Matrix
Array
Data Frame
Concept of Hypothesis
Null Hypothesis
Alternative Hypothesis
Type-I error
Type-II error
Level of Significance
Confidence Interval
One Sample T-Test
Two independent sample T-Test
Paired Sample T-Test
Chi square Test for Independence of Attributes
One Way ANOVA
Two Way ANOVA
Concept of Regression and features of Linear Line.
Assumptions of Classical Linear Model
Method of Least Squares
Understanding the Goodness of Fit
Test of Significance of the Estimated Parameters
Multiple linear Regression with their Assumptions
Concept of Multicollinearity
Signs of Multicollinearity
The Idea of Autocorrelation
Concept and Applications of Logistic Regression
Principles behind Logistic Regression
Comparison between Linear Probability Model and Logistic Regression
Semi-Partial R-Square and R-Square
Diagrammatic Representation of clusters
Problems of Cluster Analysis
Types of clustering
Hierarchical Clustering
Principal Component Analysis
Eigen Values and Eigen Vectors
Correlation Matrix check and KMO-MSA check
Factor loading Matrix
Diagrammatic Representation of Factors
Problems of Factor Loading and Solutions
Concept of Time Series and its Applications
Assumptions of Time Series Analysis
Components of Time Series
Smoothening techniques
Stationarity
ARIMA Forecasting
Box Jenkins Technology
Text Mining Analysis
Decision Trees
Market Basket Analysis

E. BASE SAS PROGRAMMING

Description

Overview of SAS Foundation
Course logistics
Course data files
Introducing the Structured Query Language
Overview of the SQL procedure
Specifying columns
Specifying rows
Presenting Data
Summarizing Data
Introduction to SQL joins
Inner joins
Outer joins
Complex SQL joins
Non-Correlated Subqueries
In-Line views
Introduction to set operators
The UNION operator
The OUTER UNION operator
The EXCEPT operator
The INTERSECT operator
Creating tables with the SQL procedure
Creating views with the SQL procedure
Dictionary tables and views
Using SQL procedure options
Interfacing PROC SQL with the Macro Language
SAS resources
Beyond this course
Course Logistics
Purpose of The Macro Facility
Program Flow
Introduction to Macro Variables
Automatic Macro Variables
Macro Variable References
User-Defined Macro Variables
Delimiting Macro Variable References
Macro Functions
Defining and Calling a Macro
Macro Parameters
Creating Macro Variables in The Data Step
Indirect References to Macro Variables
Creating Macro Variables in SQL
Conditional Processing
Parameter Validation
Iterative Processing
Global and Local Symbol Tables
SAS Resources
Beyond This Course

F. ADVANCE SAS

Description

An Overview of the SAS System
Introduction to SAS Programs
Running SAS Programs
Mastering Fundamental Concepts
Diagnosing and Correcting Syntax Errors
Exploring Your SAS Environment
Getting Started with the PRINT Procedure
Sequencing and Grouping Observations
Identifying Observations
Special WHERE Statement Operators
Customizing Report Appearance
Formatting Data Values
Creating HTML Reports
Reading Raw Data Files: Column Input
Reading Raw Data Files: Formatted Input
Examining Data Errors
Assigning Variable Attributes
Changing Variable Attributes
Reading Excel Spreadsheets
Reading SAS Data Sets and Creating Variables
Conditional Processing
Dropping and Keeping Variables
Reading Excel Spreadsheets Containing Date Fields
Concatenating SAS Data Sets
Merging SAS Data Sets
Combining SAS Data Sets: Additional Features
Introduction of Summary Reports.
Basic Summary Reports
The Report Procedure
The Tabulate Procedure
Producing Bar and pie Chart
Enhancing output
Producing Plots
Overview
Review of SAS basics
Review of DATA Step Processing
Review of Displaying SAS Data Sets
Working with Existing SAS Data Sets
Outputting Multiple Observations
Writing to Multiple SAS Data Sets
Selecting Variables and Observations
Writing to an External File
Creating an Accumulating Total variable
Accumulating Totals for a Group of Data
Reading Delimited Raw Data Files
Controlling When a Record Loads
Reading Hierarchical Raw data Files
Introduction
Manipulating Character values
Manipulating Numeric values
Manipulating Numeric values based on Dates
Converting variable Type
Using the PUT Statement
Using the DEBUG Option
Do Loop Processing
SAS Array Processing
Using SAS Arrays
Match-merging Two or more SAS Data Sets
Simple Joins Using the SQL Procedure

G. SAS PREDICTIVE MODELING

Description

Types of Analytics
Properties of Measurements
Scales of Measurement
Types of Data
Measures of Central Tendency
Measures of Dispersion
Measures of Location
Presentation of Data
Skewness and Kurtosis
Three Approaches towards Probability
Concept of a Random Variable
Probability Mass Function
Probability Density Function
Expectation of a Random Variable
Probability Distributions
Concept of population and sample
Techniques of Sampling
Sampling Distributions
Concept of hypothesis
Null hypothesis
Alternative hypothesis
Type-I error
Type-II error
Level of Significance
Confidence Interval
Parametric Tests and Non Parametric Tests
One Sample T test
Two independent sample T test
Paired Sample T test
Chi square Test for Independence of Attributes
Concept and Applications of Logistic Regression
Principles behind Logistic Regression
Comparison between Linear Probability Model and Logistic Regression
Mathematical Concepts related to Logistic Regression
Concordant Pairs, Discordant Pairs and Tied Pairs
Concept of Time Series and its Applications
Assumptions of Time Series Analysis
Components of Time Series
Smoothening Techniques
Stationarity
Random Walk
ARIMA Forecasting
One Way ANOVA
Two Way ANOVA
Principal Component Analysis
Estimating the Initial Communalities
Eigen Values and Eigen Vectors
Correlation Matrix check and KMO-MSA check
Factor loading Matrix
Diagrammatic Representation of Factors
Problems of Factor Loading and Solutions
Types of Clusters
Metric and linkage
Ward’s Minimum Variance Criteria
Semi-Partial R-Square and R-Square
Diagrammatic Representation of clusters
Problems of Cluster Analysis
Concept of Regression and features of Linear Line.
Assumptions of Classical Linear Model
Method of Least Squares
Understanding the Goodness of Fit
Multiple linear Regression with their Assumptions
Concept of Multicollinearity
Signs of Multicollinearity
The Idea of Autocorrelation

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Course Name: Certificate Program in Data Science

69990