Kickstart your Data Science and Machine Learning career with the R Language – submit your first Kaggle project!
- Computer with at least 4 GB of RAM
- Knowing the Basics of R Programming (R Objects, Functions and Libraries)
So are you looking to jump into one of the most exciting fields to work on today? And are you looking for a course that explains all the theory behind algorithms with coding?
This course was designed to be your first complete step into Data Science! We will delve deeper into the concepts of Linear and Logistic Regression, understand how Tree Based models work and learn how to evaluate predictive models. Additionally, you will develop your first end-to-end kaggle project!
This course contains lectures around the following groups:
- Code along lectures where you will see how we can implement the stuff we will learn;
- Test your knowledge with questions and practical exercises with different levels of difficulty;
This course was designed to be focused on the practical side of coding in R – other than studying the functions that let us build algorithms automatically we will investigate deeply how models are trained and how they get to the optimum solution to solve our data science challenges. And why will we use R?
R is one of the de facto languages for a lot of Data Science projects today – either for enterprise-level projects or research, R is a modern and flexible language with a smooth learning curve that enables most professionals to build predictive models in quick fashion.
At the end of the course you should be able to contribute to data science projects – understanding the choices you have to make when it comes to algorithms and learn how to evaluate those choices. Along the way you will also learn how to manipulate data with Dplyr because a huge percentage of the time spent in a Data Science project is focused on data preparation!
Here are some examples of things you will be able to do after finishing the course:
- Solving Regression problems using Linear Regression or Regression Trees.
- Solving Classification problems using Logistic Regression or Classification Trees.
- Learn how to evaluate algorithms using different metrics.
- Understanding the concept of bias and variance.
- Using Random Forests and understanding the reasoning behind them.
- Manipulating data using Dplyr.
- Build your own Kaggle Data Science project!
Join thousands of professionals and students in this Data Science journey and discover the amazing power of R as a statistical open-source language.
This course will be constantly updated based on students feedback.
Who this course is for:
- Entry-Level Data Scientists
- R Coders
- Business Analysts
- Financial Modelers