Create Your Calculator: Learn R Programming Basics Fast

Description

Why learn Data Analysis and Data Science?

According to SAS, the five reasons are

1. Gain problem solving skills

The ability to think analytically and approach problems in the right way is a skill that is very useful in the professional world and everyday life.

2. High demand

Data Analysts and Data Scientists are valuable. With a looming skill shortage as more and more businesses and sectors work on data, the value is going to increase.

3. Analytics is everywhere

Data is everywhere. All company has data and need to get insights from the data. Many organizations want to capitalize on data to improve their processes. It's a hugely exciting time to start a career in analytics.

4. It's only becoming more important

With the abundance of data available for all of us today, the opportunity to find and get insights from data for companies to make decisions has never been greater. The value of data analysts will go up, creating even better job opportunities.

5. A range of related skills

The great thing about being an analyst is that the field encompasses many fields such as computer science, business, and maths.  Data analysts and Data Scientists also need to know how to communicate complex information to those without expertise.

The Internet of Things is Data Science + Engineering. By learning data science, you can also go into the Internet of Things and Smart Cities.

This is the bite-size course to learn R Programming. You will learn R Programming very fast and You will create your own calculator very soon after learning the course.

You can take the course as follows, and you can take an exam at EMHAcademy to get SVBook Certified Data Miner using the R certificate : 

- Create Your Calculator: Learn R Programming Basics Fast (R Basics)

- Applied Statistics using R with Data Processing (Data Understanding and Data Preparation)

- Advanced Data Visualizations using R with Data Processing (Data Understanding and Data Preparation, in the future)

- Machine Learning with R (Modeling and Evaluation)

Content

1. Getting Started

2. Hello World Software

3. Variables and Data Types

4. Data types Conversion

5. Vector

6. List

7. Matrix

8. Data Frame

9. Arithmetic Operators

10. Relational Operators

11. Logical Operators

12. Decision Making I (IF statements)

13. Loop (while loop, for loop)

14. Functions

15. Create Your own Calculator

16. Import CSV

17. Descriptive Statistics with Summary()

18. Plot Graphs

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