.png){kind=logo}
- Insights
- Cryptocurrencies
- Stocks
- White Papers
- Industry
- Geography
- Insights
- Cryptocurrencies
- Stocks
- White Papers
- Industry
- Geography
In the dynamic landscape of technology and business, data science has emerged as a critical field, driving innovation and decision-making across various industries. Recognizing the increasing demand for skilled data scientists, Harvard University offers a series of online courses that bring the prestige and expertise of this renowned institution directly to learners around the world. In this article, we will explore Harvard's online courses on data science and why enrolling today can be a transformative step towards a rewarding and intellectually enriching career.
This course will demonstrate how inference and modelling may be used to construct statistical methodologies that make polls a useful tool, and how to do so using R Programming. You will learn the ideas required to construct estimates and margins of error, as well as how to apply them to produce reasonably accurate forecasts and offer an estimate of forecast precision.
Once you've understood this, you'll be able to understand two important data science concepts: confidence intervals and p-values. Then, to grasp assertions regarding the likelihood of a candidate prevailing, you'll learn about Bayesian models. Finally, at the end of the course, we will bring everything together to develop a simpler version of an election forecast model and apply it to the 2016 election.
This course, which is a component of the Professional Certificate Program in Data Science, will teach you how to structure your file system and use Unix/Linux to handle files and directories on your computer. Git, a potent version control system for monitoring and documenting code changes, will be presented to you. Additionally, we provide you an overview of GitHub and demonstrate how to use it to store your work in a shared repository.
This course, part of the Havard Professional Certificate Programe in Data Science, will teach you popular machine learning methods, principal component analysis, and regularization through the creation of a movie recommendation system.
You will get knowledge about training data and how to identify potentially predictive associations in a piece of data. You will get experience in developing algorithms with training data to forecast the outcome of subsequent datasets as you work on the movie recommendation system. Additionally, you will learn about overtraining and how cross-validation may help prevent it. Machine learning requires each of these abilities.
This course is part of the Havard Professional Certificate Program in Data Science and will teach you the fundamental principles of probability theory. This course is motivated by the conditions surrounding the 2007-2008 financial crisis. The danger of some assets sold by financial institutions was overestimated, which contributed to the financial catastrophe. To comprehend this very convoluted occurrence, we must first grasp the fundamentals of probability.
This covers key topics such as random variables, independence, Monte Carlo simulations, expected values, standard errors, and the Central Limit Theorem. These statistical ideas are essential for doing statistical tests on data and determining whether the data you're analyzing is most likely due to an experimental procedure or chance.
This is the first course in the Havard Professional Certificate Program in Data Science, and it will teach you the fundamentals of R programming. You can better remember R when you apply it to tackle a specific problem, so you'll use a real-world dataset regarding crime in the United States. You will gain the R skills required to address critical questions concerning the disparities in crime among states.
The university starts with R's functions and data types, then moves on to how to work with vectors and when to employ advanced functions like sort. You will learn how to use common programming concepts such as "if-else" and "for loop" commands, as well as how to organize, analyze, and visualize data.
