Data Science Skills you will learn

  • Business analytics
  • R programming and its packages
  • Data structures and data visualization
  • Apply functions and DPLYR function
  • Graphics in R for data visualization
  • Hypothesis testing
  • Apriori algorithm
  • kmeans and DBSCAN clustering

Who should learn Data Science with R

  • Data Scientist
  • Data Analyst
  • IT Professionals
  • Software Developers

What you will learn in Data Science with R Program

  • Data Science with R Programming

    • Lesson 00 - Course Introduction

      01:31
      • Course Introduction
        01:31

    • Lesson 01 - Introduction to Business Analytics

      21:06
      • 1.001 Overview
        00:44
      • 1.002 Business Decisions and Analytics
        04:33
      • 1.003 Types of Business Analytics
        03:53
      • 1.004 Applications of Business Analytics
        08:57
      • 1.005 Data Science Overview
        01:29
      • 1.006 Conclusion
        01:30
      • Knowledge Check

    • Lesson 02 - Introduction to R Programming

      26:35
      • 2.001 Overview
        00:31
      • 2.002 Importance of R
        05:20
      • 2.003 Data Types and Variables in R
        02:14
      • 2.004 Operators in R
        04:39
      • 2.005 Conditional Statements in R
        02:45
      • 2.006 Loops in R
        05:07
      • 2.007 R script
        01:44
      • 2.008 Functions in R
        02:58
      • 2.009 Conclusion
        01:17
      • Knowledge Check

    • Lesson 03 - Data Structures

      50:57
      • 3.001 Overview
        01:04
      • 3.002 Identifying Data Structures
        13:14
      • 3.003 Demo Identifying Data Structures
        14:05
      • 3.004 Assigning Values to Data Structures
        04:51
      • 3.005 Data Manipulation
        09:23
      • 3.006 Demo Assigning values and applying functions
        07:46
      • 3.007 Conclusion
        00:34
      • Knowledge Check

    • Lesson 04 - Data Visualization

      29:40
      • 4.001 Overview
        00:29
      • 4.002 Introduction to Data Visualization
        03:03
      • 4.003 Data Visualization using Graphics in R
        18:50
      • 4.004 ggplot2
        05:14
      • 4.005 File Formats of Graphic Outputs
        01:08
      • 4.006 Conclusion
        00:56
      • Knowledge Check

    • Lesson 05 - Statistics for Data Science-I

      14:10
      • 5.001 Overview
        00:21
      • 5.002 Introduction to Hypothesis
        02:06
      • 5.003 Types of Hypothesis
        03:13
      • 5.004 Data Sampling
        02:48
      • 5.005 Confidence and Significance Levels
        04:33
      • 5.006 Conclusion
        01:09
      • Knowledge Check

    • Lesson 06 - Statistics for Data Science-II

      29:55
      • 6.001 Overview
        00:28
      • 6.002 Hypothesis Test
        00:47
      • 6.003 Parametric Test
        14:36
      • 6.004 Non-Parametric Test
        08:31
      • 6.005 Hypothesis Tests about Population Means
        02:09
      • 6.006 Hypothesis Tests about Population Variance
        00:45
      • 6.007 Hypothesis Tests about Population Proportions
        01:11
      • 6.008 Conclusion
        01:28
      • Knowledge Check

    • Lesson 07 - Regression Analysis

      45:04
      • 7.001 Overview
        00:26
      • 7.002 Introduction to Regression Analysis
        01:11
      • 7.003 Types of Regression Analysis Models
        01:38
      • 7.004 Linear Regression
        08:59
      • 7.005 Demo Simple Linear Regression
        07:29
      • 7.006 Non-Linear Regression
        03:49
      • 7.007 Demo Regression Analysis with Multiple Variables
        13:29
      • 7.008 Cross Validation
        01:48
      • 7.009 Non-Linear to Linear Models
        02:06
      • 7.010 Principal Component Analysis
        02:45
      • 7.011 Factor Analysis
        00:26
      • 7.012 Conclusion
        00:58
      • Knowledge Check

    • Lesson 08 - Classification

      01:05:14
      • 8.001 Overview
        00:31
      • 8.002 Classification and Its Types
        04:24
      • 8.003 Logistic Regression
        03:35
      • 8.004 Support Vector Machines
        04:26
      • 8.005 Demo Support Vector Machines
        11:13
      • 8.006 K-Nearest Neighbours
        02:34
      • 8.007 Naive Bayes Classifier
        02:53
      • 8.008 Demo Naive Bayes Classifier
        06:15
      • 8.009 Decision Tree Classification
        09:47
      • 8.010 Demo Decision Tree Classification
        06:25
      • 8.011 Random Forest Classification
        02:01
      • 8.012 Evaluating Classifier Models
        06:04
      • 8.013 Demo K-Fold Cross Validation
        04:09
      • 8.014 Conclusion
        00:57
      • Knowledge Check

    • Lesson 09 - Clustering

      28:10
      • 9.001 Overview
        00:17
      • 9.002 Introduction to Clustering
        02:57
      • 9.003 Clustering Methods
        07:47
      • 9.004 Demo K-means Clustering
        11:15
      • 9.005 Demo Hierarchical Clustering
        05:02
      • 9.006 Conclusion
        00:52
      • Knowledge Check

    • Lesson 10 - Association

      23:13
      • 10.001 Overview
        00:15
      • 10.002 Association Rule
        06:20
      • 10.003 Apriori Algorithm
        05:19
      • 10.004 Demo Apriori Algorithm
        10:37
      • 10.005 Conclusion
        00:42
      • Knowledge Check

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Learn the Basics of Data Science with R

Course Advisors

  • Ronald van Loon

    Ronald van Loon

    Top 10 Big Data and Data Science Influencer, Director - Adversitement

    Named by Onalytica as one of the three most influential people in Big Data, Ronald is also an author of a number of leading Big Data and Data Science websites, including Datafloq, Data Science Central, and The Guardian. He also regularly speaks at renowned events.

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Why you should learn Data Science with R

$40.6 billion by 2023

expected growth of big data analytics market

50% higher salary

than the the rest of the IT industry as per Randstad report

Career Opportunities

FAQs

  • What are the prerequisites to learn the Data Science with R basics program?

    There are no prerequisites to take this Data Science with R basics program. However, knowledge of any programming language and core mathematics would be an added advantage.

  • How do beginners learn data science basics?

    Beginners can learn about Data Science by getting an overview of business analytics, how data-driven decisions help achieve more profits, and a broad understanding of how data is analyzed.

  • How long does it take to learn Data Science with R?

    Professionals with a technical background and some prior knowledge of Data Science tend to progress faster in learning Data Science. Beginners can also achieve a similar pace by taking this Data Science R basics free course, as it helps you learn the basics from scratch.

  • What should I learn first in the Data Science with R free course?

    Data Science learners can first get a basic understanding of statistics, core math concepts, data manipulation, and data analysis in this free course.

  • Are Data Science with R basics program easy to learn?

    This Data Science with R basics free course has been designed keeping different learner needs in mind. Learners who don’t have any clear idea about data science can still follow this program easily.

  • What is Data Science used for?

    Companies are widely using Data Science to gain meaningful insights from their customer data and make more informed decisions for building their products. It involves a range of processes right from collecting data, cleaning, processing to data analysis, and data visualization.

  • Why is Data Science so popular?

    Traditionally it has been hard for companies to understand their customer requirements and feedback regarding their products and services. With the help of data science, companies are now able to gather huge amounts of customer data and understand their expectations. Data Science has become popular because it assists companies in achieving reduced costs, better utilization of resources, improved quality of products, and enhanced customer satisfaction.

  • Can I complete this Data Science with R free course in 90 days?

    The 6 hours of in-depth video lessons covered in this data science fundamentals course are quite easy to grasp even for beginners. So, depending on the learner's pace, the free course can be easily completed within 90 days.

  • What are my next best learning options after completing this Data Science free course?

    After completing this Data Science basics free course, you can try our other courses like Data Scientist Master’s Program or Post Graduate Program in Data Science.

  • What are the career opportunities in data science?

    Data Science is one of the fastest-growing career fields and a lot of job opportunities are available across countries. Some of the top job roles that require knowledge of Data Science are Data Scientists, Data Analysts, Machine Learning Engineers, and Data Architects.

  • Will I get a certificate after completing the Data Science basics free course?

    Yes, You will receive a Course Completion Certificate from SkillUp upon completing the Data Science basics free program. You can unlock it by logging in to your SkillUp account. As soon as the certificate is unlocked, you will receive a mail with a link to your SkillUp learning dashboard on your registered mail address. Click the link to view and download your certificate. You can even add the certificate to your resume and share it on social media platforms.

  • Why learn Data Science with R basics?

    Data Science continues to be one of the hottest technologies of the 21st century. You should learn Data Science with R if you are an aspiring Data Scientist or Data Analyst. There is huge demand for professionals trained in these technologies because almost every enterprise is data-driven today. You will develop the expertise in performing exploratory data analysis, data visualization, and building machine learning models. These capabilities are much sought-after across industries and in the field of research. 

  • Who can learn Data Science?

    You can learn Data Science if you are a:

    • Manager
    • Data science professional
    • Data analysis professional
    • Engineering graduate with a passion for data
    • Business analyst

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