# Data Science Academy: Master Data Science In R

**R Based Data Science: The Ultimate Data Science Course With Practical Examples & Hands-On Training**

**_ THIS IS GONNA BE A OVER +40 HOUR OF CONTENT COURSE!_**

**_ (MORE CONTENT IS BEING ADDED ALMOST ON A DAILY BASIS)_**

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This is **Your Complete Guide** to mastering statistical modelling, data visualization, machine learning and basic deep learning in R.

** BOOST YOUR CAREER TO THE NEXT LEVEL :**

This course covers ALL the aspects of practical data science, which makes this course **The Only Data Science Training You Need**.

By the end of the course, you’ll be able to store, filter, manage, and manipulate data in R to give yourself & your company a competitive edge.

** LEARN FROM AN EXPERT DATA SCIENTIST**

** WITH +5 YEARS OF EXPERIENCE:**

My name is MINERVA SINGH and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).

I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals.

Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning.

This gives student an incomplete knowledge of the subject. This course will give you a robust grounding in all aspects of data science, from statistical modeling to visualization to machine learning.

And UNLIKE other courses out there, we are not going to stop at the statistical and machine learning. We will also cover data mining, web-scraping, text mining and natural language processing along with working with spatial data in this course.

With such a rigorous grounding in so many topics, **you will** **be an unbeatable data scientist**.

** WHAT YOU WILL LEARN:**

This course is your one shot way of acquiring the knowledge of statistical data analysis skills that I acquired from the rigorous training received at two of the best universities in the world, perusal of numerous books and publishing statistically rich papers in renowned international journal like PLOS One. Specifically the course will:

(a) Take You (Even if you don’t have prior R and/or statistics background) from a basic level to performing some of the most common advanced data science techniques using the powerful R based tools.

(b) Equip You to use R for performing the different statistical data analysis and visualization tasks for data modelling.

© Introduce You to some of the most important statistical and machine learning concepts in a practical manner such that you can apply these concepts for practical data analysis and interpretation.

(d) You will get a strong understanding of some of the most important data science techniques.

(e) & You will be able to decide which data science techniques are best suited to answer your research questions and applicable to your data and interpret the results.

*More Specifically, here’s what’s covered in the course:*

- Getting started with R, R Studio and Rattle for implementing different data science techniques.
- Data Structures and Reading in Pandas, including CSV, Excel, JSON, HTML data.
- How to Pre-Process and “Wrangle” your R data by removing NAs/No data, handling conditional data, grouping by attributes, etc.
- Creating data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, and more!
- Statistical analysis, statistical inference, and the relationships between variables.
- Machine Learning, Supervised Learning, Unsupervised Learning in R.
- AND much more!

We will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects.

** NO PRIOR R or STATISTICS/MACHINE LEARNING**

** KNOWLEDGE IS REQUIRED:**

You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.

My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real -life

After taking this course, you’ll easily use packages like caret, dplyr to work with real data in R. You’ll even understand deep concepts like statistical modeling and the difference between statistics and machine learning (including hands-on techniques).

I will even introduce you to some very important practical case studies- such as detecting loan re-payment using machine learning.

With this Powerful All-In-One R Data Science course, you’ll know it all: visualization, stats, machine learning, data mining, and deep learning!

The underlying motivation for the course is to ensure you can apply R based data science on real data into practice today. Start analyzing data for your own projects, whatever your skill level and IMPRESS your potential employers with actual examples of your data science projects.

**ENROLL NOW!** On top of the course, you’ll also have my continuous support at all times as well to make sure your experience with the course is a SUCCESS!

- Anyone who wishes to learn practical data science using R and RStudio
- People looking to work with real life data in R
- Anyone wanting to master important pre-processing steps such as data cleaning, summarizing and visualization in R
- Anyone wanting to learn about the most important statistical concepts & their implementation in R
- Anyone interested in learning how to implement machine learning algorithms using R
- Anyone interested in using R for web-scraping and data mining
- Anyone interested in working with text data and text mining
- Anyone interested in Natural Language Processing & Sentiment Analysis
- People interested in using R for spatial data analysis
- People interested in learning about artificial neural networks

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