ebook: Learn Data Science with R – free download
Check out this new book for data science beginners with many practical examples that covers statistics, R, graphing, and machine learning. As a source to learn the full breadth of data science foundations, "Learn Data Science with R" starts at the beginner level and gradually progresses into expert content.
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 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.
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 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:
- Getting Started
- Statistics and R
- Data Wrangling
- Exploratory Data Analysis
- Machine learning
- Types Of Machine Learning
- Advanced Supervised Learning
- Hands-on Projects
- Use cases of Data Science
- 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.