Certified Data Scientist with R, SAS and Excel

simplilearn
4.5
1 opinion
  • The course faculty conducted the course in an excellent manner and aligned the same with many real life examples.
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Description

  • Type

    Course

  • Methodology

    Online

Simplilearn is the World’s Largest Certification Training Provider, with over 400,000+ professionals trained globally
Trusted by the Fortune 500 companies as their learning provider for career growth and training
2000+ certified and experienced trainers conduct trainings for various courses across the globe
All our Courses are designed and developed under a tried and tested Unique Learning Framework that is proven to deliver 98.6% pass rate in first attempt.

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Reviews

4.5
  • The course faculty conducted the course in an excellent manner and aligned the same with many real life examples.
    ← | →
100%
4.5
excellent

Course rating

Recommended

Centre rating

Kanhaiya Jethani

4.5
19/03/2014
What I would highlight: The course faculty conducted the course in an excellent manner and aligned the same with many real life examples.
Would you recommend this course?: Yes
*All reviews collected by Emagister & iAgora have been verified

This centre's achievements

2017
2016

All courses are up to date

The average rating is higher than 3.7

More than 50 reviews in the last 12 months

This centre has featured on Emagister for 7 years

Subjects

  • Ms Excel
  • Excel MS

Course programme

Course Preview Course Agenda
  • Business Analytics foundation - RTools
    • Lesson 00 - Business Analytics Foundation With R Tools
      • 0.1 Business Analytics Foundation With R Tools
      • 0.2 Objectives
      • 0.3 Analytics
      • 0.4 Places Where Analytics is Applied
      • 0.5 Topics Covered
      • 0.6 Topics Covered (contd.)
      • 0.7 Career Path
      • 0.8 Thank You
    • Lesson 01 - Introduction to Analytics
      • 1.1 Introduction to Analyics
      • 1.2 analytics vs analysis
      • 1.3 What is Analytics
      • 1.4 Popular Tools
      • 1.5 Role of a Data Scientist
      • 1.6 Data Analytics Methodology
      • 1.7 Problem Definition
      • 1.8 Summarizing Data
      • 1.9 Data collection
      • 1.10 Data Dictionary
      • 1.11 Outlier Treatment
      • 1.12 Quiz
    • Lesson 02 - Statistical Concepts And Their Application In Business
      • 2.1 Statistical Concepts And Their Application In Business
      • 2.2 Descriptive Statistics
      • 2.3 Probability Theory
      • 2.4 Tests of Significance
      • 2.5 Non-parametric Testing
      • 2.6 Quiz
    • Lesson 03 - Basic Analytic Techniques - Using R
      • 3.1 Introduction
      • 3.2 Data Exploration
      • 3.3 Data Visualization
      • 3.4 Pie Charts
      • 3.5 Correlation
      • 3.6 Analysis of variance
      • 3.7 Chi-squared test
      • 3.8 T-test
      • 3.9 Summary
      • 3.10 Quiz
    • Lesson 04 - Predictive Modelling Techniques
      • 4.1 Predictive Modelling Techniques
      • 4.2 Regression Analysis and Types of regression models
      • 4.3 Linear Regression
      • 4.4 Coefficient of determination R
      • 4.5 How good is the model
      • 4.6 How to find Liner regression equation
      • 4.7 Commands to perform linear regression
      • 4.8 Linear regression to predict sales
      • 4.9 Case Study - Linear Regression
      • 4.10 Case Study - Classification
      • 4.11 Logistic regression
      • 4.12 Example - Logistic regression in R
      • 4.13 Logistic Regression Predicting recurrent visits to a web site
      • 4.14 Cluster Analysis
      • 4.15 Command to perform clustering in R
      • 4.16 Hierarchical Clustering
      • 4.17 Case Study - Implement K means and Hierarchical Clustering
      • 4.18 Time Series
      • 4.19 Cyclical versus seasonal analysis
      • 4.20 Decomposition of Time Series
      • 4.21 Caes Study- Time Series Analysis
      • 4.22 Decomposing Non-Seasonal Time Series
      • 4.23 Exponential Smoothing
      • 4.25 Exponential smoothing and forecasting in R
      • 4.26 Example - Holt Winters
      • 4.27 White Noise
      • 4.28 Correlogram Analysis
      • 4.29 Box-Jenkins forecasting Models
      • 4.30 Case Study - Time Series Data using ARMA
      • 4.31 Business Case
      • 4.32 Summary
      • 4.33 Thank You
      • 4.24 Advantages and Diadavantages of Exponential Smoothing
  • Business Analytics foundation – SAS tools & Excel
    • Lesson 00 - Business Analytics Foundation With SAS and Excel Tools
      • 0.1 Business Analytics Foundation With SAS and Excel Tools
      • 0.2 Objectives
      • 0.3 Analytics
      • 0.4 Places Where Analytics is Applied
      • 0.5 Topics Covered
      • 0.6 Topics Covered (contd.)
      • 0.7 Career Path
      • 0.8 Thank You
    • Lesson 01 - Introduction to Analytics
      • 1.1 Introduction to Analytics
      • 1.2 Analytics Vs. Analysis
      • 1.3 What is Analytics
      • 1.4 Popular Tools
      • 1.5 Role of a Data Scientist
      • 1.6 Data Analytics Methodology
      • 1.7 Problem Definition
      • 1.8 Summarizing Data
      • 1.9 Data Collection
      • 1.10 Data Dictionary
      • 1.11 Outliers and their treatment
      • 1.12 Quiz
      • 1.13 Summary
    • Lesson 02 - Statistical Concepts And Their Applications In Business
      • 2.1 Statistical Concepts and Their Application In Business
      • 2.2 Descriptive Statistics
      • 2.3 Probability Theory
      • 2.4 Tests of Significance
      • 2.5 Non-parametric Testing
      • 2.6 Quiz
    • Lesson 03 - Basic Analytic Techniques
      • 3.1 Basic Analytic Techniques - Using SAS
      • 3.2 Data Exploration
      • 3.3 Data Visualization
      • 3.4 Diagnostic Analytics
      • 3.5 Case studies
      • 3.6 Quiz
    • Lesson 04 - Predictive Modeling Techniques
      • 4.1 Predictive Modelling Techniques 1
      • 4.2 Linear Regression
      • 4.3 Logistic Regression
      • 4.4 Cluster Analysis
      • 4.5 Time Series Analysis
      • 4.6 Thank You
      • 4.7 Quiz
  • Free Course SAS Base Programmer
    • Module 00 - SAS Base Programmer Certification
      • 0.1 Introduction
      • 0.2 Objective Slide
      • 0.3 Introduction to SAS
      • 0.4 SAS Functions and Consumers of SAS
      • 0.5 SAS Base Programmer Certification Exam
      • 0.6 SAS Base Programmer Certification Exam Registration
      • 0.7 Benefits of SAS Base Certification for Professionals
      • 0.8 Benefits of SAS Base Certification for Employers
      • 0.9 Thank You
    • Module 01 - Lesson 01 SAS Base Programmer Certification
      • 1.1 Introduction
      • 1.2 Objective Slide
      • 1.3 Introducing SAS
      • 1.4 Components of SAS software
      • 1.5 SAS GUI Environment
      • 1.6 SAS Program
      • 1.7 SAS Program (contd.)
      • 1.8 SAS Program (contd.)
      • 1.9 SAS Library
      • 1.10 SAS Library (contd.)
      • 1.11 SAS Library (contd.)
      • 1.12 SAS Library (contd.)
      • 1.13 SAS Library (contd.)
      • 1.14 SAS Library (contd.)
      • 1.15 SAS Data Sets
      • 1.16 SAS Data Sets (contd.)
      • 1.17 SAS Data Sets (contd.)
      • 1.18 SAS Data Sets (contd.)
      • 1.19 SAS Data Sets (contd.)
      • 1.20 SAS Variable Attributes
      • 1.21 SAS Variable Attributes (contd.)
      • 1.22 SAS Variable Attributes (contd.)
      • 1.23 SAS Variable Attributes (contd.)
      • 1.24 SAS Variable Attributes (contd.)
      • 1.25 SAS Variable Attributes (contd.)
      • 1.26 Summary
      • 1.28 Thank You
      • 1.29 Quiz
    • Module 01 - Lesson 02 SAS Base Programmer Certification Course
      • 1.1 Introduction
      • 1.2 Objective Slide
      • 1.3 Creating SAS Data sets (Instream method)
      • 1.4 Creating SAS Data sets (Instream method) (contd.)
      • 1.5 Creating SAS Data sets (Instream method) (contd.)
      • 1.6 Importing Raw Data files in SAS Data set
      • 1.7 Importing Raw Data files in SAS Data set (contd.)
      • 1.8 Importing Raw Data files in SAS Data set (contd.)
      • 1.9 Importing Raw Data files in SAS Data set (contd.)
      • 1.10 Importing Raw Data files in SAS Data set (contd.)
      • 1.11 Importing Raw Data files in SAS Data set (contd.)
      • 1.12 Importing Raw Data files in SAS Data set (contd.)
      • 1.13 Column Pointer Control
      • 1.14 Column Pointer Control (contd.)
      • 1.15 Column Pointer Control (contd.)
      • 1.16 Column Pointer Control (contd.)
      • 1.17 Column Pointer Control (contd.)
      • 1.18 Importing Raw Data files in SAS Data set
      • 1.19 Reading non standard numeric data
      • 1.20 Reading non standard numeric data (contd.)
      • 1.21 Reading non standard numeric data (contd.)
      • 1.22 Reading non standard numeric data (contd.)
      • 1.23 Reading non standard numeric data (contd.)
      • 1.24 Writing non standard numeric data (contd.)
      • 1.25 Writing non standard numeric data (contd.)
      • 1.26 Importing Raw Data files in SAS Data set
      • 1.27 Importing Raw Data files in SAS Data set (contd.)
      • 1.28 Importing Raw Data files in SAS Data set (contd.)
      • 1.29 Importing Raw Data files in SAS Data set (contd.)
      • 1.30 Importing Raw Data files in SAS Data set (contd.)
      • 1.31 Importing Raw Data files in SAS Data set (contd.)
      • 1.32 Importing Raw Data files in SAS Data set (contd.)
      • 1.33 Importing Raw Data files in SAS Data set (contd.)
      • 1.34 Importing Raw Data files in SAS Data set (contd.)
      • 1.35 Importing Raw Data files in SAS Data set (contd.)
      • 1.36 Importing Raw Data files in SAS Data set (contd.)
      • 1.37 Importing Raw Data files in SAS Data set (contd.)
      • 1.38 Combining multiple Data Sets
      • 1.39 Combining multiple Data Sets (contd.)
      • 1.40 Combining multiple Data Sets (contd.)
      • 1.41 Combining multiple Data Sets (contd.)
      • 1.42 Combining multiple Data Sets (contd.)
      • 1.43 Combining multiple Data Sets (contd.)
      • 1.44 Combining multiple Data Sets (contd.)
      • 1.45 Accessing an Excel Work Book using SAS
      • 1.46 Summary
      • 1.47 Quiz
      • 1.48 Thank You
    • Module 02 - SAS Base Programmer Certification Course
      • 2.1 Introduction
      • 2.2 Objective Slide
      • 2.3 Exporting Data from SAS
      • 2.4 Exporting SAS data set Using Proc Export
      • 2.5 Exporting SAS data set Using Proc Export (contd.)
      • 2.6 Exporting SAS data set Using Proc Export (contd.)
      • 2.7 Controlling Observations to be Processed
      • 2.8 Controlling Observations to be Processed (contd.)
      • 2.9 General Form of SAS Functions
      • 2.10 SAS Date Value
      • 2.11 SAS Date Value (contd.)
      • 2.12 SAS Date Value (contd.)
      • 2.13 SAS Date Value (contd.)
      • 2.14 SAS Date Value (contd.)
      • 2.15 SAS Date Value (contd.)
      • 2.16 SAS Date Value (contd.)
      • 2.17 SAS Date Value (contd.)
      • 2.18 SAS Date Value (contd.)
      • 2.19 SAS Time Functions
      • 2.20 Statistical Analysis of SAS Variables
      • 2.21 Statistical Analysis of SAS Variables (contd.)
      • 2.22 Statistical Analysis of SAS Variables (contd.)
      • 2.23 Summary
      • 2.24 Quiz
      • 2.25 Thank You
    • Module 03 - Lesson 01 SAS Base Programmer Certification Course
      • 3.1 Introduction
      • 3.2 Objectives Slide
      • 3.3 Assignment Statements in DATA Step
      • 3.4 Modifying Character Values with SAS Functions
      • 3.5 Modifying Character Values with SAS Functions (contd.)
      • 3.6 Modifying Character Values with SAS Functions (contd.)
      • 3.7 Modifying Character Values with SAS Functions (contd.)
      • 3.8 Modifying Character Values with SAS Functions (contd.)
      • 3.9 Modifying Character Values with SAS Functions (contd.)
      • 3.10 Modifying Character Values with SAS Functions (contd.)
      • 3.11 Modifying Character Values with SAS Functions (contd.)
      • 3.12 Modifying Character Values with SAS Functions (contd.)
      • 3.13 Modifying Character Values with SAS Functions (contd.)
      • 3.14 Modifying Character Values with SAS Functions (contd.)
      • 3.15 Modifying Character Values with SAS Functions (contd.)
      • 3.16 Modifying Character Values with SAS Functions (contd.)
      • 3.17 Modifying Character Values with SAS Functions (contd.)
      • 3.18 Modifying Numeric Values with SAS Functions
      • 3.19 Modifying Numeric Values with SAS Functions (contd.)
      • 3.20 Converting Datatypes Using SAS Functions
      • 3.21 Converting Datatypes Using SAS Functions (contd.)
      • 3.22 Processing Data with Do Loops
      • 3.23 Processing Data with Do Loops (contd.)
      • 3.24 Processing Data with Do Loops (contd.)
      • 3.25 Processing Data with Do Loops (contd.)
      • 3.26 Processing Data with Do Loops (contd.)
      • 3.27 Processing Data with Do Loops (contd.)
      • 3.28 Processing Data with Do Loops (contd.)
      • 3.29 Processing Data with Do Loops (contd.)
      • 3.30 SAS Arrays
      • 3.31 SAS Arrays (contd.)
      • 3.32 SAS Arrays – Different Ways of Specifying Dimensions
      • 3.33 SAS Arrays – Different ways of specifying elements (contd.)
      • 3.34 SAS Arrays (contd.)
      • 3.35 Summary
      • 3.36 Quiz
      • 3.37 Thank You
    • Module 03 - Lesson 02 SAS Base Programmer Certification
      • 3.1 Introduction
      • 3.2 Objectives Slide
      • 3.3 Selecting Variables to be Processed
      • 3.4 Modify Variable Attributes in DATA Step
      • 3.5 SAS Expressions
      • 3.6 Modifying Variables using Operators
      • 3.7 Modifying Variables using Operators (contd.)
      • 3.8 Conditionally Execute SAS Statements
      • 3.9 Conditionally Execute SAS Statements (contd.)
      • 3.10 Conditionally Execute SAS Statements (contd.)
      • 3.11 Conditionally Execute SAS Statements (contd.)
      • 3.12 Conditionally Execute SAS Statements (contd.)
      • 3.13 Conditionally Execute SAS Statements (contd.)
      • 3.14 Sorting Data
      • 3.15 Sorting Data (contd.)
      • 3.16 Investigating SAS Libraries
      • 3.17 Investigating SAS Libraries (contd.)
      • 3.18 Accumulate Column Totals in DATA Statement
      • 3.19 Accumulate SUB Totals in DATA Statement (contd.)
      • 3.20 Accumulate SUB Totals in DATA Statement (contd.)
      • 3.21 Summary
      • 3.22 Quiz
      • 3.23 Thank You
    • Module 04 - SAS Base Programmer Certification Course
      • 4.1 Introduction
      • 4.2 Objectives Slide
      • 4.3 SAS Report
      • 4.4 Creating a Basic Report
      • 4.5 Creating a Basic Report (contd.)
      • 4.6 Specifying Titles and Footnotes in a Report
      • 4.7 Creating Enhanced List and Summary Reports
      • 4.8 Creating Enhanced List and Summary Reports (contd.)
      • 4.9 Creating Enhanced List and Summary Reports (contd.)
      • 4.10 Creating Enhanced List and Summary Reports (contd.)
      • 4.11 Creating Enhanced List and Summary Reports (contd.)
      • 4.12 Creating Enhanced List and Summary Reports (contd.)
      • 4.13 Creating Enhanced List and Summary Reports (contd.)
      • 4.14 Creating Enhanced List and Summary Reports (contd.)
      • 4.15 Creating Enhanced List and Summary Reports (contd.)
      • 4.16 Creating Enhanced List and Summary Reports (contd.)
      • 4.17 Creating Enhanced List and Summary Reports (contd.)
      • 4.18 Creating Enhanced List and Summary Reports (contd.)
      • 4.19 Creating Enhanced List and Summary Reports (contd.)
      • 4.20 Creating Enhanced List and Summary Reports (contd.)
      • 4.21 Creating Frequency Reports
      • 4.22 Creating Frequency Reports (contd.)
      • 4.23 Creating Frequency Reports (contd.)
      • 4.24 Creating Frequency Reports (contd.)
      • 4.25 SAS Output Delivery System
      • 4.26 SAS Output Delivery System (contd.)
      • 4.27 SAS Output Delivery System (contd.)
      • 4.28 Summary
      • 4.29 Quiz
      • 4.30 Thank You
    • Module 05 - SAS Base Programmer Certification Course
      • 5.1 Introduction
      • 5.2 Objectives Slide
      • 5.3 SAS LOG
      • 5.4 Syntax Errors
      • 5.5 Syntax Errors (contd.)
      • 5.6 Syntax Errors (contd.)
      • 5.7 Syntax Errors (contd.)
      • 5.8 Syntax Errors (contd.)
      • 5.9 Data Errors
      • 5.10 Testing Your Program to Avoid Data Errors
      • 5.11 Testing Your Program to Avoid Data Errors (contd.)
      • 5.12 Logical Programming Errors
      • 5.13 Summary
      • 5.14 Quiz
      • 5.15 Thank You

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Certified Data Scientist with R, SAS and Excel

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