By Narayana Murthy, Data Scientist.

I am happy to announce the release of my ebook, Learn Data Science with R. It is available for download as a book giveaway offer for a limited period.

Learning Data Science

Data science is a combination of various skill sets. The Data scientist needs proficiency in statistics, mathematics, programming, and other skills.

So the logical question is, Where to start?

Statistics

Statistics is understanding and interpreting data. It is the core of data science. One of the best places to start is the Statistics Foundations course from Pluralsight. It covers both descriptive statistics and inferential statistics.

Programming Languages

Python, R, and Julia are popular programming languages for data science projects. Each language has unique merits. Python is the most popular data science language, and Julia is the fastest. Core Python is a recommended course for learning the python language.

R language is the best option for beginners, academics, and domain experts. It is overwhelming to learn data science and a general-purpose language like Python in parallel. R is easier to grasp as it is a data science language. It has good community support.

Machine Learning

Machine learning is the science of building models automatically. It is a subset of artificial intelligence. Learn machine learning after mastering statistics and programming. The book Linear Algebra and Learning from Data covers both simple and advanced topics. It teaches linear algebra together with deep learning and neural nets.

Introduction of Learn Data Science with R

Courses on specific topics give a good understanding of data science concepts. It would be nice to learn all the skills in one place.

Learn Data Science with R covers statistics, basic mathematics, the R language, visualization, and machine learning algorithms. It is precise and complete. It is a 250-page book.

The chapters include:

1. Getting Started
2. Statistics and R
3. Mathematics
4. Data Wrangling
5. Exploratory Data Analysis
6. Machine learning
7. Types Of Machine Learning
8. Advanced Supervised Learning
9. Hands-on Projects
10. Use cases of Data Science
11. Final Notes

Writing the book was a great experience for me. The book received positive feedback in Goodreads and LibraryThing. Download the ebook. I hope you enjoy reading it. Your feedback is appreciated.

Bio: Narayana Nemani is currently working as a Lead Data Scientist, and is involved in the teaching and research of data science.

Related:

• Data Science Learning Roadmap for 2021
• R or Python? Why Not Both?
• 5 Tips for Writing Clean R Code