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Projects in R Learn R Creating Data Science Projects

- |
- Lectures:
**82** - |
- Videos:
**8.5 hours** - |
- Level:
**All** - |
- Language:
**English** - |
- Last Updated:
**07/2017**

- Instructor : Eduonix Learning Solutions

R Programming Language is not an easy language to learn, and requires extensive practice in addition to the theory. Simply understanding in theory, how R Programming language works and everything that you can do with R is just not enough – you require a complete breakdown of how to go about doing it.

This is why we have designed this comprehensive project-based course! In this course, we attempt to break down this complex programming language and environment into an easy to follow structured tutorial that will help you not only understand this statistical language, but also become more familiar with how you can go about using it.

R is a programming language and environment for statistical computing and graphics. It allows developers to work with a range of statistical and graphical techniques including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, etc.

Our course will help you go through a step by process of understanding how R can help you become a more efficient data miner, analyst and statistician. However, it won’t just list or show you how to do that. The instructor will lead you through real world projects that will show you exactly how you can do them, while urging you to follow all the projects along with the instructor.

This project based course a great way for you to understand the fundamentals using a hands-on approach. No more confusing resources or boring theories, but rather you would actually get a hands on with the R Programming Language and environment.

In this course, you would learn the fundamentals of R programming language, including the basic concepts such as lists, functions, arrays, vectors, matrices, strings, etc.

There are five major aspects that you will learn in this course.

**1. Practical approach to the R Programming** – If you already have some background in R programming, or even have the knowledge, then this will help you gain a practical approach to R programming.

**2. Learn Different Forms of Data Visualization** – Visualizations of data has become a popular trend, as it makes the data more prominent and easier to understand. These include different types of visualizations such as bar graph, charts, heat map, etc.

**3. Learn efficient ways to visualize data –** Data should be efficient, especially if you are working with partial data. If the data is not efficient, the analysis would not be faulty and can be misunderstood.

**4. Learn ways to manipulate data –** Data isn’t always constant and it is often used to analyze past data and make future predictions. For this data is required to be manipulated to create predictions for multiple scenarios.

**5. Learn to generate reports using R –** Now we come to the most important stage of data mining and analysis. Here you will learn to generate effective reports that will help you put out a clean set of data analysis for consumption.

All of this and so much more in packed in this course. So, enroll now and let’s get down to programming with R!

1 | Intro | ||

2 | Intro to R | ||

3 | R in windows | ||

4 | Installing R studio |

5 | R as Calculator | ||

6 | Working with vectors Part 1 | ||

7 | Working with vectors Part 2 | ||

8 | Working with character vectors | ||

9 | Working with Matrix | ||

10 | Generating Numbers | ||

11 | Pacakge in R |

12 | Data visualization and R - What will you learn | ||

13 | Reading CSV files- Part 1 | ||

14 | Reading CSV files -Part 2 | ||

15 | Reading other formats | ||

16 | Extracting values from vectors - Part 1 | ||

17 | Extracting values from vectors - Part 2 | ||

18 | Subsetting Dataframe using | ||

19 | Subsetting using subset function |

20 | Working with vectors | ||

21 | Factors | ||

22 | Know types of data | ||

23 | Text Processing | ||

24 | Working with Logical Values | ||

25 | Working with Missing Values |

56 | Spatial Data | ||

57 | Maps Part 1 | ||

58 | Maps Part 2 | ||

59 | Maps Part 3 | ||

60 | Maps Part 4 | ||

61 | Maps Part 5 | ||

62 | Maps Part 6 | ||

63 | Maps Part 7 | ||

64 | Heatmap in R |