Harvard offers nine free courses to help you become an expert in data science.
Data science has become one of the most in-demand skills in the job market of today. In a variety of industries, from finance to healthcare and beyond, the capacity to glean insightful information from massive amounts of data has become essential. One of the most prominent universities in the world, Harvard University, has acknowledged the value of data science and provides a number of free courses that might assist you in mastering this subject. In this article, we'll examine the nine free Harvard courses that can provide you the expertise you need to succeed in data science.
Programming
Learn to code as the initial step in your data science studies. Your favorite method can be used to complete this.Ideal programming languages are Python or R.
Harvard University provides Data Science: R Basics, an introductory R course created specifically for data science students, if you're interested in learning more.
You will learn about R concepts including variables, vector arithmetic, data types, and indexing in this course. Additionally, you will discover how to create charts to display data and how to alter data using programs like dplyr.
Take Harvard's free CS50 Introduction to Programming with Python course if Python is your preferred language. This course will cover a variety of concepts, including functions, variables, arguments, data types, conditional expressions, loops, methods, and objects.
The aforementioned programs can be completed at your own leisure. On the other hand, the Python course is more in-depth than the R program andis more time-consuming to complete. Additionally, R is used to teach the other courses in this roadmap, so knowing it can be beneficial if you want to follow up rapidly.
Visualization of data
One of the most effective methods for explaining your data results to someone else is visualization.
The Harvard Data Visualization program will teach you how to express data-driven insights as well as how to construct visuals in R using the ggplot2 tool.
Probability
You will learn crucial probability concepts in this course, which are essential for running statistical analyses on data. Among the topics discussed are random variables, Monte Carlo simulations, independence, expected values, standard errors, and the Central Limit Theorem.
The aforementioned subjects will be instructed througha case study, allowing you to apply what you've learned to data from the real world.
Statistics
After learning about probability, you can enroll in this course to learn the fundamentals of statistical inference and modeling.
In addition to introducing you to the fundamentals of Bayesian statistics and predictive modeling, this program will show you how to create population estimates and margins of error.
Tools for Productivity
The study of data science has nothing to do with this elective project management course. Instead, you'll discover how to use GitHub for version control, Unix/Linux for file management, and R for report creation.
You'll save a ton of time and be better able to manage complete data science projects if you can do the following.
Pre-processing of DataData Wrangling, the course that comes after it on this list, will teach you how to organize data and convert it into a form that machine learning models can easily understand.
The topics of data import into R, handling string data, data cleaning, HTML parsing, interacting with date-time objects, and text mining are all covered.
