Quality Seal Emagister EMAGISTER CUM LAUDE

Certified Data Scientist with R Language

simplilearn
Online

Price on request
You can also call the Study Centre
81510... More
Compare this course with other similar courses
See all

Important information

  • Course
  • Online
Description

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.

Important information

Opinions

R

19/03/2014
What I would highlight Course material is good and the trainer is excellent in knowledge sharing.

Would you recommend this course? Yes.

Course programme

Course Preview
  • 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

Additional information

  • What is this course about?

    Simplilearn’s R training is an ideal package for aspiring data analysts to gain expertise in data analytics. Participants at the end of the training will be technically competent in R programming language concepts such as data visualization, exploration; statistical concepts like linear & logistic regression, cluster analysis and forecasting.


Compare this course with other similar courses
See all