The main advantage of attending a prestigious name-brand data science certification program is the reputation of that esteemed organization that it carries with it. Other than providing tech students and rookies with better opportunities to find an entry-level job at that company (such as Microsoft), it’s a great badge for the more experienced professionals as well.
However, there are several high-level courses available, such as the ones through edX at IBM, Microsoft, MIT, UC San Diego and Harvard. Each one is different, and tailored to fit the needs of a variety of different professionals at many levels. In this article, we will take a look at these different programs, summarize their most important characteristics, the skills you’re going to acquire (as well as those you need before taking the course), and why you should choose one of them over another.
- Python Data Science Professional Certificate from IBM
- Data Science Professional Certificate from Harvard
- Data Science MicroMasters Program from UC San Diego
- Microsoft Professional Program in Data Science
- Statistics and Data Science MicroMasters Program from MIT
Python Data Science Professional Certificate from IBM
Another entry-level program, the IBM Data Science course is focused on Python, the most popular language for this field. A particularly hands-on program, the IBM course is focused on job readiness and will grant you no-charge access to tools like Jupyter Notebooks in the IBM Cloud to work with real datasets.
You will utilize popular Python toolkits and libraries such as pandas, NumPy, matplotlib, seaborn, Folium, SciPy, and scikit-learn to visualize and analyze data while you get a quick introduction in machine learning.
The 4 courses are self-paced with varying degrees of effort per course — usually 2-5 hours per week for up to 6 weeks. At the end you will apply and demonstrate all the skills you learned with a capstone project involving a real-life business problem.
What you will learn:
- Python Basics for Data Science
- Analyzing Data with Python
- Visualizing Data with Python
- Machine Learning with Python: A Practical Introduction
Data Science Professional Certificate from Harvard
For people who do not possess a programming background, the Harvard program is the perfect opportunity to learn data science. Instead of Python, the course will teach the student how to build a foundation in the R programming language to wrangle, analyze and visualize data, using real-world case studies.
All the bases will be covered, from learning the basic statistical concepts such as probability, inference, and modeling, to how to use the tidyverse, ggplot2 for data visualization, and dplyr. Through the course, the student will become familiar with essential tools used by practicing data scientists such as Unix/Linux, Git and GitHub, and RStudio, as well as with many machine learning algorithms. (If you want to learn about computer science, check out 10 Essential Computer Science Courses You Can Take Online.)
The Harvard program consists of 9 courses including the capstone exam, but it’s much more fast-paced than the previous ones. In fact, all the courses require just 1-2 hours per week for 8 weeks, but since it’s self-paced (no instructors required), you can go as fast as you want. The capstone exam at the end is an opportunity to apply the knowledge and skills in R data analysis that you have gained throughout the series and requires an estimated 15-20 hours per week for 2 weeks.
What you will learn:
- R Basics
- Inference and Modeling
- Productivity Tools
- Linear Regression
- Machine Learning
Data Science MicroMasters Program from UC San Diego
This program is the online version of a master’s degree program in data science at UC San Diego, except all the university professors will teach you everything you need to know online. It is an advanced course for professionals who want to dive deeper in data science, and learn how to clean even the noisiest real-world data through the wise use of machine learning models.
The course will provide a well-rounded understanding of the tools needed to visualize complex data, such as Apache Spark to analyze data that does not fit within the memory of a single computer.
Just like the MIT program, the UC San Diego course is an “instructor-paced” offering with an eye toward practicality, so you’ll learn how to enhance your portfolio by working on practical projects and assignments.
However, it is a little shorter and more advanced since it requires a certain level of programming knowledge with languages such as Java, C, Pascal, Fortran, C++, Python or PHP, and some familiarity with loops, if/else and variables. The program consists of 4 courses of 10 weeks (each week you need to devote 8-12 hours to the course).
What you will learn:
- Python for Data Science
- Probability and Statistics in Data Science Using Python
- Machine Learning Fundamentals
- Big Data Analytics Using Spark
Microsoft Professional Program in Data Science
The Microsoft program is a solid professional program that is suited to all needs due to its great flexibility. You will learn how to use a broad range of Microsoft products like Transact-SQL, Excel and Azure to explore topics like data queries, data analysis, data visualization and how statistics informs data science practices.
Its great emphasis on both theory and practice makes it the ideal course for tech professionals who want to dive deeper in a specific subfield of data science, as well as rookies who want to build a solid foundation in data science research methods and machine learning.
This professional program by Microsoft is highly flexible and modular, so you can choose to take the full program or any one of the 10 individual, self-paced courses of just 16-32 hours per course. You can also choose if you want, for example, to complete a course in either R or Python, depending on your familiarity with each programming language.
The program includes a capstone exam, and it’s divided into 3 modules: Fundamentals, Core Data Science and Applied Data Science.
What you will learn:
- Fundamentals — Learn data science basics.
- Core Data Science — Learn essential programming languages to manipulate data and discover the fundamentals of machine learning.
- Applied Data Science — Dive deeper into data science programming languages and start leveraging data to develop intelligent solutions.
Statistics and Data Science MicroMasters Program from MIT
This program consists of a total of five master’s level courses in order to learn the foundations of machine learning, data science and statistics. The student will learn how to use probabilistic modeling and statistical inference to analyze big data and make data-driven predictions.
Since it’s built to teach practical skills, the student will understand how to extract meaningful information from data that could be used in decision making — one of the most sought-after skills many organizations are looking for. (To learn more about big data, see 5 Helpful Big Data Courses You Can Take Online.)
On top of that, a solid understanding of machine learning algorithms, deep neural networks and other supervised methods will allow the novice data scientist to make sense of seemingly unstructured data. No dataset will be too large to be analyzed anymore. Proficiency in Python is a prerequisite since the course will teach how to use it together with R to make sense of even the most complicated dataset.
This MIT program is “instructor-paced,” meaning that the courses are taught by the instructors at specific times of the year, as opposed to being constantly available. The program consists of 4 courses of 13-16 weeks (each week you need to devote 10-14 hours to the course), plus a capstone exam of two weeks.
What you will learn:
- Probability — The Science of Uncertainty and Data.
- Data Analysis in Social Science — Assessing Your Knowledge.
- Fundamentals of Statistics.
- Machine Learning with Python: From Linear Models to Deep Learning.
All edX courses are extremely simple to follow since the lectures are short, understandable and exceptionally to the point. You’ll get all the information you need to hone your skills or learn new techniques, as well as gain all the necessary experience to be comfortable with your new role.