Description
Are you one of those who wonders what programming language to learn and what data science course to take? Is the idea of learning R for data science knocking on your mind’s door, but you still have doubts of letting it in? In data science, R is getting more and more popular, and it’s not an accident. Data manipulation, data visualization, and machine learning are at the basis of building R, and the package ecosystem lets you do amazing things which can be otherwise much more complicated using other programming languages. Especially in data visualization.
The Value of Enrolling a Data Science Course Today
If you’ve recently checked job offers, you must have noticed the increasing demand in Data Scientists. And that is a global trend. In the next 3 years, 10 Million+ new jobs will be vacant for those who spare a bit of their time to take a data science course and learn R programming. If you want to secure a job for a long time, and also earn a high salary already today, invest your time to learn R from scratch.
I intend this data science course to guide you through the basics of machine learning with R. In this course, you’ll learn data science from scratch from me, an R-native tutor, by working on 4 projects and exploring 8 case studies. These will help you with rapid progress to learn data science from scratch. R is commonly known as a bit challenging to learn, but I’ll be there to take you through the basics until you feel confident.
Realistic examples will help you answer both, “how” and “why” learn R for data science. In this 30+ hours data science course you’ll make use of 100+ pieces of study material as well as code templates, so you can put your hands on R easier and saving your time.
Course Benefits
- When you learn R programming from scratch with me, you’ll start from the fundamentals of how data science, machine learning and artificial intelligence are beneficial in the modern world.
- I’ll explain the value of your decision to learn data science from scratch and share my experience and tips on career switching in data science.
- I’ll make sure you’re clear with such fundamentals as importing/exporting your data with R.
- You’ll also see the benefits of the dplyr package for easy data manipulation, and you’ll see why ggplot2 is so suitable for data visualization.
- We’ll explore things like data visualization, data manipulation, R’s conditional statements.
These bullet points are just the tip of data science course iceberg. We’ll power on our yellow submarine to explore R even in greater depth.
Google, Facebook, Twitter, AirBnB, Uber and others use R for specific purposes
Now, I know there’s no one single programming language that will suit any needs. It’s just life. I can only tell you that Google exploits R at assessing ad effectiveness and making economic forecasts. Facebook uses R for user’s behavior analysis. Twitter's team does data visualization with R, too. Microsoft, AirBnB, Uber and other big names are also hiring R-competent data scientists, so it seems like a good idea to take a data science course in 2019 and learn programming with R. Trust me to guide you through: learn R from scratch, dip your toes into data science and discover machine learning with R.
What Will You Learn?
- You'll know how and why use R for data science and machine learning
- You'll get practical tips and advise on Data Scientist's career
- You'll work on practical tasks and examine case studies to fully understand the fundamentals of R programming
- You'll be able to use the core features and packages of R
Requirements
- No prior knowledge is required to enrol this course
- Software and data needed in the course will be provided for free
Curriculum
Course consist of total 22h 31min of content, in total.
Section 1: Meet Your Instructor
Section 2: Introduction to Data Science
Section 3: Course Curriculum Overview
Section 4: Introduction to R
Section 5: R Programming
Section 6: R Data Structure
Section 7: Import and Export in R
Section 8: Data Manipulation
Section 9: Data Visualization
Section 10: Introduction To Statistics
Section 11: Hypothesis Testing
Section 12: Hypothesis Testing in Practice
Section 13: Machine Learning Toolbox
Section 14: Business Case Understanding
Section 15: Data Pre-Processing
Section 16: Supervised Learning : Regression
Section 17: Classification Overview
Section 18: Logistic Regression
Section 19: K-NN Study
Section 20: SVM Study
Section 21: Naive Bayes
Section 22: Decision Tree
About the Instructor
We are a team of amazon, flipkart, Google. We love to teach people. We mainly teach different IT related topics!
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