Explore these latest data science courses available at Havard University

Data science is one of the most sought-after and rewarding fields in the 21st century. It combines the power of mathematics, statistics, computer science, and domain knowledge to extract insights and value from data. If you are interested in learning or advancing your skills in data science, Harvard University offers a variety of courses that cover the latest trends and applications of data science in 2024. In this article, we will introduce some of the best data science courses at Harvard in 2024.

Data Science Principles

Data Science Principles, a Harvard University online course, provides an overview of data science with a code- and math-free introduction to prediction, causation, data wrangling, privacy, and ethics. Data Science Principles is an introductory data science course for anybody who wants to improve results and get insights from their company's data gathering and analysis activities. This training is designed to help beginners and managers better understand data science and how to deal with data scientists.

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Data Science: Machine Learning

This course is part of our Professional Certificate Programme in Data Science. In this course, you will learn popular machine learning methods, principal component analysis, and regularization through the creation of a movie recommendation system. You will also learn about training data and how to use a collection of data to identify potentially predictive associations. As you develop the movie recommendation system, you will learn how to train algorithms with training data to predict the outcome for future datasets. You will also learn about overtraining and how to avoid it, using cross-validation.

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Data Science Professional Certificate

The programme covers concepts like probability, inference, regression, and machine learning, and it will help you develop an essential skill set that includes R programming, data visualization with ggplot2, data wrangling with dplyr, file organization with Unix/Linux, version control with git and GitHub, and reproducible document creation with RStudio.

In each course, you will utilize inspiring case studies, pose particular issues, and learn by addressing them using data analysis. Case studies include global health and economic trends, crime rates in the United States, the 2007-2008 financial crisis, election forecasting, baseball team building (influenced by Moneyball), and movie recommendation systems. Throughout the programme, you will be taught how to use the R software environment. You will be taught R, statistical concepts, and data analysis processes all at the same time. We feel that learning how to address a specific problem helps you remember R information more effectively.

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Data Science: Wrangling

As part of this Professional Certificate Programme in Data Science, you will cover various typical data wrangling tasks, including importing data into R, cleaning data, string processing, HTML parsing, dealing with dates and timings, and text mining. These wrangling stages are rarely required in a single study, but a data scientist will most likely encounter them all at some point.

Data is rarely easy to get in a data science endeavour. The data is more likely to be stored in a file, or database, or extracted from documents like web pages, tweets, or PDFs. In these circumstances, the initial step is to load the data into R and clean it with the tidyverse package. Data wrangling refers to the procedures involved in converting raw data into a clean form.

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Data Science: Capstone

To become an expert data scientist, you must gain practice and experience. By completing this capstone project, you will be able to use the information and abilities you have learned in R data analysis throughout the course. This final project will put your knowledge of data visualization, probability, inference and modelling, data wrangling, data organization, regression, and machine learning to the test.

Unlike the remainder of our Professional Certificate Programme in Data Science, this course will provide far less supervision from the professors. When you finish the project, you will have a data product to show off to potential companies or educational programmes, demonstrating your knowledge in the subject of data science.

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