The demand for skilled data science practitioners is rapidly growing, and these course series will prepare you to tackle real-world data analysis challenges.
This course series by Harvard will cover concepts such as probability, inference, regression, and machine learning and help you develop an essential skill set that includes R programming, data wrangling, data visualisation with ggplot2, file organisation with Unix/Linux, version control with git and GitHub, and will also teach to reproducible document preparation with RStudio.
In this article, we will see a 9-course series by Harvard called HarvardX’s Data Science Professional Certificate Course, a series of data science courses offered by Harvard through the EdX platform. To gain maximum through the course it is recommended to take the course in the order they are mentioned.
9-course series by Harvard on Data Science
Data Science: R Basics
This course will introduce learners to the basics of R programming. Learners will better retain R through this course as they will learn it through solving a specific problem, so through this course, the instructor will use a real-world dataset about crime in the United States. You’ll also learn how to apply general programming features like “if-else,” and “for loop” commands, and how to wrangle, analyse and visualise data.
- Level: Introductory
- Time Commitment: 1-2 hours per week
- Prerequisites: An up-to-date browser is recommended to enable programming directly in a browser-based interface.
- Course Language: English
- Video Transcripts: اَلْعَرَبِيَّةُ, Deutsch, English, Español, Français, हिन्दी, Bahasa Indonesia, Português, Kiswahili, తెలుగు, Türkçe, 中文
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Data Science: Inference and Modeling
This course instructor will teach you key concepts like Statistical inference and modelling through a motivating case study on election forecasting. This course will show you how inference and modelling can be applied to develop the statistical approaches that make polls an effective tool and the instructor will show you how to do this using R. Once you learn this you will be able to understand two concepts that are ubiquitous in data science: confidence intervals, and p-values.
- Level: Introductory
- Duration: 8 weeks long
- Time Commitment: 2 – 4 hours per week
- Prerequisites: Data Science: Probability or a basic knowledge of probability theory.
- Course Language: English
- Video Transcripts: اَلْعَرَبِيَّةُ, Deutsch, English, Español, Français, हिन्दी, Bahasa Indonesia, Português, Kiswahili, తెలుగు, Türkçe, 中文
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Data Science: Visualisation
Through this course, the instructor will teach learners the basics of data visualisation and exploratory data analysis. Instructor will use motivating examples and ggplot2 which is a data visualisation package for the statistical programming language R. This course will give you the skills you need to utilise data to reveal valuable insights to advance your career in data science.
- Level: Introductory
- Duration: 8 weeks long
- Time Commitment: 2 – 4 hours per week
- Course Language: English
- Video Transcript: English
- Prerequisites: An up-to-date browser is recommended to enable programming directly in a browser-based interface.
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Data Science: Probability
Through this course, the instructor will teach learners valuable concepts in probability theory. The motivation for this course is the circumstances surrounding the financial crisis of 2007-2008. The instructor will introduce important concepts such as random variables, independence, Monte Carlo simulations, expected values, standard errors, and the Central Limit Theorem. These statistical concepts are fundamental to conducting statistical tests on data and understanding whether the data you are analysing is likely occurring due to an experimental method or chance.
- Level: Introductory
- Time Commitment: 2 – 4 hours per week
- Prerequisites: None
- Course Language: English
- Video Transcripts: اَلْعَرَبِيَّةُ, Deutsch, English, Español, Français, हिन्दी, Bahasa Indonesia, Português, Kiswahili, తెలుగు, Türkçe, 中文
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Data Science: Productivity Tools
Through this course, the instructor will teach learners how to use Unix/Linux as a tool for managing files and directories on their computer. The instructor will also introduce you to GitHub and demonstrate how you can use this service to keep your work in a repository. Learners will learn to write reports in R markdown which permits you to incorporate text and code into a document.
- Level: Introductory
- Duration: 8 weeks long
- Time Commitment: 2 – 4 hours per week
- Prerequisites: None
- Course Language: English
- Video Transcripts: اَلْعَرَبِيَّةُ, Deutsch, English, Español, Français, हिन्दी, Bahasa Indonesia, Português, Kiswahili, తెలుగు, Türkçe, 中文
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Data Science: Wrangling
Through this course Instructor we cover several standard steps of the data wrangling process like importing data into R, tidying data, string processing, HTML parsing, working with dates and times, and text mining. Knowing how to wrangle and clean data will enable you to make critical insights that would otherwise be hidden.
- Level: Introductory
- Duration: 8 weeks long
- Time Commitment: 2 – 4 hours per week
- Prerequisites: None
- Course Language: English
- Video Transcript: English
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Data Science: Linear Regression
This course will cover how to implement linear regression and adjust for confounding in practice using R. Linear regression is a powerful technique for removing confounders but it is not a magical process. In this course, the instructor will teach when it is appropriate to use linear regression, and this course will teach you when to apply this technique.
- Level: Introductory
- Duration: 8 weeks long
- Time Commitment: 2 – 4 hours per week
- Prerequisites: None
- Course Language: English
- Video Transcripts: اَلْعَرَبِيَّةُ, Deutsch, English, Español, Français, हिन्दी, Bahasa Indonesia, Português, Kiswahili, తెలుగు, Türkçe, 中文
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Data Science: Machine Learning
Through this course, you will learn popular machine learning algorithms, principal component analysis, and regularisation by building a movie recommendation system. These skills are fundamental to machine learning. You will learn about training data, and how to use a set of data, you will also learn about overtraining and techniques to avoid it such as cross-validation.
- Level: Introductory
- Duration: 8 weeks long
- Time Commitment: 2 – 4 hours per week
- Prerequisites: This course is part of our Professional Certificate Program in Data Science and we recommend the preceding courses in the series as prerequisites.
- Course Language: English
- Video Transcripts: اَلْعَرَبِيَّةُ, Deutsch, English, Español, Français, हिन्दी, Bahasa Indonesia, Português, Kiswahili, తెలుగు, Türkçe, 中文
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Data Science: Capstone
By completing this capstone project you will get an opportunity to apply the knowledge and skills in R data analysis that you have gained throughout the series.
This capstone project will assess your competencies in the visualisation of data, probability and inference theory, data modelling, cleaning and organizing as well as regression and ML techniques. The final component of the series will be distinct from the rest of the courses in that you will be given minimal to no direction from the trainers. After undertaking this project, you will have a data-related product to show before employers or schools, which will demonstrate your level of competence in data science.
- Level: Introductory
- Duration: 2 weeks long
- Time Commitment: 15 – 20 hours per week
- Prerequisites: None
- Course Language: English
- Video Transcript: English
Apply here
In conclusion, as we are seeing, the demand for data science skills is growing very fast, and these Harvard courses offer a comprehensive way to prepare for real-world challenges with real-world examples. The series of courses includes significant aspects such as R programming, data analytics, machine learning, etc. Every course in this series will enhance your understanding of the subject in stages, starting with basic R programming up to advanced machine learning methods. By pursuing this series of courses, you will acquire useful skills that are highly relevant to the profession of a data scientist and also, you will have a solid background to be able to deal with any data-driven field.
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