Bootcamp on Data Science using R language

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Prag Robotics

7:14:31

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1 - About the Program

1 - Course Introduction.mp4

03:04

2 - Course Outline.mp4

03:56

2 - Introduction to Data Science

3 - What is Data Science.mp4

04:21

4 - What is Data.mp4

06:35

5 - Whats the Job with Data.mp4

03:19

6 - Data Science Tools Technologies.mp4

03:23

7 - Data Science Process Flow.mp4

01:54

8 - Applications of Data Science.mp4

05:39

3 - Foundations of R

9 - Introduction to R Language.mp4

04:07

10 - Installation of R Language and R Studio.mp4

07:25

11 - Handling R Environment.mp4

09:09

12 - Setting Working Directory.mp4

02:12

13 - Data Types and Variables.mp4

11:01

14 - Arithmetic Operations.mp4

08:58

15 - Data Frames.mp4

11:54

4 - Data Collection

16 - Data Science Methodology.mp4

07:58

17 - Data Collection Techniques.mp4

02:38

18 - Introduction to Web Scraping.mp4

03:34

19 - Web Scraping Using R Language.mp4

18:13

5 - Data Preprocessing

20 - Significance of Data Preprocessing.mp4

09:05

21 - Checking Data Formats.mp4

11:58

22 - Handling Missing Data.mp4

13:27

23 - Handling Categorical Data.mp4

05:19

24 - Outlier Analysis.mp4

03:02

25 - Data Scaling.mp4

02:29

6 - Descriptive Statistics

26 - Significance of Statistics in Data Science.mp4

09:37

27 - Descriptive Statistics Tools for Data Science.mp4

10:29

28 - Measure of Central Tendency.mp4

08:22

29 - Variation in Data.mp4

04:47

30 - Association of Variables.mp4

05:54

7 - Inferential Statistics

31 - What is Inferential Statistics.mp4

10:17

32 - Confidence Intervals.mp4

21:48

33 - Confidence Intervals in R Language.mp4

12:01

34 - Student TDistribution.mp4

04:46

35 - TTest in R Language.mp4

10:18

36 - Hypothesis Testing.mp4

14:41

37 - Hypothesis Testing in R Language.mp4

19:38

8 - Predictive Modelling

38 - What is Predictive Analytics.mp4

02:50

39 - Introduction to Linear Regression.mp4

07:35

40 - Simple Linear Regression in R Language.mp4

16:14

41 - Introduction to Multiple Linear Regression.mp4

05:48

42 - Multiple Linear Regression in R Language.mp4

09:16

9 - Classification

43 - Introduction to Classification Models.mp4

06:50

44 - Introduction to Logistic Regression.mp4

05:14

45 - Implementation of Logistic Regression.mp4

19:38

46 - Introduction to Random Forest Classification.mp4

12:25

47 - Random Forest Classification in R Language.mp4

12:57

10 - Dimensionality Reduction

48 - Introduction to Dimensionality Reduction.mp4

06:53

49 - Introduction to Principle Component Analysis.mp4

08:43

50 - Principle Component Analysis in R Language.mp4

19:44

11 - About the Program

51 - Course Conclusion.mp4

03:06

Description

Building Data Science Pipelines

What You'll Learn?

Definition of Data Science
Data Collection & Pre-processing
Statistics
Predictive Modelling

Who is this for?

Anyone interested in the field of Data Science

What You Need to Know?

None

More details

Description
Data science is a multidisciplinary field that uses a combination of techniques, algorithms, processes, and systems to extract meaningful insights and knowledge from structured and unstructured data. Data science is of significant importance in today's world due to its transformative impact on various aspects of business, research, and decision-making. It incorporates elements of statistics, computer science, domain expertise, and data analysis to analyse and interpret complex data. Data science enables organizations to make informed decisions based on data analysis rather than relying solely on intuition or experience. This leads to more accurate and effective decision-making processes. During this course, students will learn the entire process of developing a data science project. During this course, students will learn the nuances of Data science, data collection, data cleaning, data visualization, Significance of statistics and Machine learning etc. We will be using r programming language to develop data pipelines. R is a programming language and environment specifically designed for statistical computing and graphics. It is open-source and widely used by statisticians, data scientists, researchers, and analysts for data analysis, statistical modelling, and visualization. R has a rich ecosystem of packages and libraries that extend its functionality. These packages cover a wide range of domains, from machine learning and data manipulation to bioinformatics and finance. So, letâ€™s buckle up!!!
Who this course is for:
Anyone interested in the field of Data Science

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Prag Robotics is a “Centre of Excellence” for Robotics and Artificial Intelligence studies, has very quickly established a mark in manufacturing & academic circles. Prag has established RoboLab–4.0 in Universities, Colleges and Schools, delivering educational programs in the emerging technologies viz. Robotics, Artificial Intelligence and Data Science – deploying the “Real-time” Robots, Equipment and most recent Software. These intricately designed programs are preparing our students to compete with the best in the world of Robotics and Automation.The course curriculums are in line with 23 legendary universities across the globe and industry insights with our superlative industry connect. Our programs are clear, to-the-point, expertly designed and delivered by roboticists who have accomplished their masters from eminent educational institutions.In this journey, conducted awareness sessions for over 18000 students and individuals, educated 6000+ young engineers, made them industry ready thus supported them to find placement in their domain or pursue their higher education with ease.

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