In a data-driven world, the ability to analyze and interpret information is critical. Harvard University’s Professional Certificate in Data Science provides a structured, comprehensive program for gaining expertise in this evolving field.
The self-paced course blends foundational theory with practical projects, teaching industry-standard tools and techniques that enable learners to transform complex data into actionable, real-world insights.
The e program focuses on applied learning, reinforcing statistical and programming skills through practical, real-world projects.
Build a strong foundation in R programming for data analysis and statistical computing.It will help students to develop a solid base in the R programming language for statistical computing and data analysis.
Learn to apply probability theory, inferential quality control techniques, and hypothesis testing to resolve real life issues.
Hands on data wrangling (e.g. formats, databases…) and data visualization using dplyr and ggplot2.Students will train to use Unix/Linux, Git, GitHub, and RStudio to produce better work processes.
Explore machine learning algorithms for predictive analytics and decision-making.Apply skills through real-world case studies to reinforce concepts and practical expertise.
The program costs $1,481 and is flexible in terms of accessibility and time. The program allows participants to work at their own pace based on their schedule. However, the program requires high levels of motivation from students, especially those who are new to the many areas of study available.
Upon successful completion of the course, students will receive a Premier Certificate in Data Science, Mathematics, and Computer Science. This certificate gives them a recognized credential that can assist students in advancing their career or education.
Key components of the curriculum include:
Data Science: R Basics: Build a foundation in R and learn how to wrangle, analyze, and visualize data.
Data Science: Visualization: Learn basic data visualization principles and how to apply them using ggplot2.
Data Science: Probability: Learn probability theory, essential for a data scientist using a case study on the financial crisis.
Data Science: Interference and Modelling: Learn how to use two of the most popular methods of data analysis; Inference and Modelling.
Data Science: Productivity Tools: Learn how to keep projects organized and provide reproducible output using GitHub, Git, Unix/Linux and RStudio.
Data Science : Wrangling: Learn how to take raw data and put it into a format that can be used for analysis.
Data Science: Linear Regression: Learn how R can be used to create linear regression, which is one of the most commonly used statistical models in data science.
Data Science: Building Machine Learning Models: Learn how to create a Movie Recommendation System and the reasons for its tremendous success as one of the more commonly used techniques in data science.
Data Science: Capstone: Demonstrates mastery of concepts and skills acquired throughout the Professional Certificate in Data Science program.
Several features differentiate the Professional Certificate in Data Science offered by Harvard University from other data science programs such as hands-on project methodology, as demonstrated by case studies where students use statistics and programming.
Developing technical acumen, along with the ability to accurately interpret and communicate results and by combining instructional content with applied, real-world experience, students achieve a foundation for success in the field of data science.
The Professional Certificate in Data Science program at Harvard University provides a comprehensive and meaningful educational experience while presenting a challenge to its students. Students receive a thorough education through expert instructors, applied projects and exposure to critical data analytical tools and methods.
Developing one's understanding of data science and developing the necessary skills to be successful, along with establishing a network of connections, is an important strategic pathway towards building professional development in the field of data science.