Data Science Professional Certificate Part 1

Overview

Please note: to take advantage of the free offer, please fill in the 'Request Information' form above and one of our consultants will be in touch.

The demand for skilled data science practitioners in industry, academia, and government is rapidly growing. The HarvardX Data Science programme prepares you with the necessary knowledge base and useful skills to tackle real-world data analysis challenges. The programme covers concepts such as probability, inference, regression, and machine learning and helps you develop an essential skill set that includes R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with Unix/Linux, version control with git and GitHub, and reproducible document preparation with RStudio. 

Part 1 of the Data Science Programme includes: R Basics, Visualisation, Probability, Productivity Tools 

  • Fee
    R2999
  • Institution
    HarvardX
  • Programme Duration
    8 weeks
  • Language
    English
  • Estimated Effort
    1-2 Hours / Week
  • Courses
    4 courses
Start Dates
 
  • Fundamental R programming skills
  • Statistical concepts such as probability, inference, and modeling and how to apply them in practice
  • Gain experience with the tidyverse, including data visualization with ggplot2 and data wrangling with dplyr
  • Become familiar with essential tools for practicing data scientists such as Unix/Linux, git and GitHub, and RStudio
  • Implement machine learning algorithms
  • In-depth knowledge of fundamental data science concepts through motivating real-world case studies

The Harvard University Professional Certificate Programme from Harvard includes the following courses:

In each course, we use motivating case studies, ask specific questions, and learn by answering these through data analysis. Case studies include: Trends in World Health and Economics, US Crime Rates, The Financial Crisis of 2007-2008, Election Forecasting, Building a Baseball Team (inspired by Moneyball), and Movie Recommendation Systems.

 

Throughout the programme, we will be using the R software environment. You will learn R, statistical concepts, and data analysis techniques simultaneously. We believe that you can better retain R knowledge when you learn how to solve a specific problem. Furthermore, HarvardX has partnered with DataCamp for all assignments, which use code checking technology that will permit you to get hands-on practice during the courses.

 

Included courses:

 

R Basics

 

Build a foundation in R and learn how to wrangle, analyze, and visualize data.

 

Visualisation

 

Learn basic data visualization principles and how to apply them using ggplot2.

 

Probability

 

Learn probability theory -- essential for a data scientist -- using a case study on the financial crisis of 2007-2008.

 

Productivity Tools 

 

Keep your projects organized and produce reproducible reports using GitHub, git, Unix/Linux, and RStudio.

Upon successful completion, participants will earn a professional certificate from Harvard University.

  • Fundamental R programming skills
  • Statistical concepts such as probability, inference, and modeling and how to apply them in practice
  • Gain experience with the tidyverse, including data visualization with ggplot2 and data wrangling with dplyr
  • Become familiar with essential tools for practicing data scientists such as Unix/Linux, git and GitHub, and RStudio
  • Implement machine learning algorithms
  • In-depth knowledge of fundamental data science concepts through motivating real-world case studies

 

 

  • R is listed as a required skill in 64% of data science job postings and was Glassdoor’s Best Job in America in 2016 and 2017. (source: Glassdoor)
  • Companies are leveraging the power of data analysis to drive innovation. Google data analysts use R to track trends in ad pricing and illuminate patterns in search data. Pfizer created customized packages for R so scientists can manipulate their own data.
  • 32% of full-time data scientists started learning machine learning or data science through a MOOC, while 27% were self-taught. (source: Kaggle, 2017)
  • Data Scientists are few in number and high in demand. (source: TechRepublic)

Arlene Lanser

Pearson Student Advisor

David Bell

Marketing Expert at The Wharton School of the University of Pennsylvania