MITx Data Science Program Alternatives

Do you want to pursue an alternative to a data science master’s degree that is affordable and entirely online? Then the MITx Data Science Program is a great choice. But before you commit to this 14-month course, you might want to consider your options one more time.

When it comes to data science courses – there is no one-size-fits-all. Every learner has their individual goals and needs, and it’s the role of the data science community to help them realize their full potential. With that in mind, we compiled a list of alternatives to the MIT data science course – not because we believe it’s not worth it, but because we understand the importance of making an informed decision when it comes to your future career.

We’ll first look at the MIT course in detail – exploring its main features, pros, and cons. Then we will dive into the list of alternatives giving you an honest overview of four categories and the individual courses that fall under them. You will find that the world of data science is full of variety but not all upskilling paths favor the absolute beginner.

Table of Contents

1. MITx Data Science Program: Overview

2. MIT Data Science Course Alternatives

1. University Data Science Courses
2. Online Data Science Courses 
3. Joined Data Science Courses
4. Professional Data Science Courses

3. MITx Data Science Program Alternatives: Next Steps

MITx Data Science Program: Overview

The MITx MicroMaster's Program Credential in Statistics and Data Science is an A-Z track that develops skills in key areas of data science and machine learning. Starting with the fundamentals of statistics and probability, the program progresses onto creating predictive analytics models in Python and solving real-world problems with data. To gain the final MicroMaster's credential you need to pass four verified courses (3 mandatory and 1 of 2 electives) and sit a virtually proctored capstone exam. All the courses are designed and taught by MIT faculty from the departments of Economics, Computer Science, and Data Science. It’s important to note that this program does not constitute a full master’s degree from MIT, and it is delivered via the online learning provider edX.

Pros

The Data Science MicroMaster's from MIT is a solid choice for anyone who wants to build in-demand data skills from the ground up. As an interdisciplinary program rooted in the expertise of senior faculty from one of the leading technological institutions in the world, this program has got a lot going for it:

1. No application requirements and no selection process. Anyone from any background can enroll. That said, MIT does recommend having a foundation in advanced mathematics and basic Python programming skills, if you want to be successful in the program.
2. Fully online. You can take the courses in this track while working full-time, though most of them require around 10-14 hours per week to complete and there is a timeframe during which you can pass them before they expire.
3. Earn credits for further study. If you’re set on enrolling in a full master’s course or a PhD after you obtain this data science credential, MIT ensures the courses you took can be converted into credits that count towards obtaining a degree in one of their partner institutions around the world.

Cons

While the online format in combination with the world-leading expertise of MIT certainly makes for a rewarding learning experience, the program may not be suitable for you if:

1. You are interested in obtaining a more varied skillset. The MicroMaster's in Data Science consists of 5 courses in total. But in reality, you can only take 4 of these as you must choose between 1 of 2 electives. These are all fairly in-depth, but still, they don’t offer nearly as wide a range of skills as other programs in our list of alternatives.
2. You want to learn at your own pace. Teaching in the MIT data science course is done through pre-recorded lectures that are released according to a preset schedule. The courses themselves expire after a certain period and if you don’t manage to complete them in time, you have to wait to try again. Moreover, even if you go for the paid version of a particular course in the track (this is mandatory if you want to obtain any kind of credential), the expiry data remains unchanged.
3. You are conscious of overspending time and money. The data science program at MIT takes about 14 months to complete and there is no way to speed up this process as courses are conducted based on a strict schedule. In addition, to gain the credential in Statistics and Data Science from MIT you have to take the verified (i.e. paid) versions of all 4 courses and also pay to sit the capstone exam. You can’t obtain the final program award unless you complete the 5 modules that each cost 300USD. In other words, your total investment will be 1,500USD, or 1,350USD if you purchase all the courses at once.

MIT Data Science Program Alternatives

Your data science journey in its initial steps will mostly coincide with the upskilling pathway you embark on, so it’s very important to get this first part right. Parse the suggestions on our list carefully – they all represent great ways to master sought-after data skills and gain prestigious credentials. Yet they also feature widely different approaches to teaching that may or may not correspond to your individual expectations.

University Data Science Courses

Data science degrees are a new addition to the curriculums of traditional institutions. Computer science, economics, and other social sciences are still the go-to qualifications of budding data scientists. That being said, as the skills gap in data continues to grow universities are looking for ways to address this issue by offering a wide range of programs:

1. Bachelor’s degrees – these 3–4-year programs are still gaining traction and only a handful of universities across the USA and Europe offer them. Check out our article about data science BAs to learn more about their projected rise.
2. Master’s degrees – lasting between 1-2 years, these courses are predominantly still on-campus and are known for their exorbitant tuition fees and lack of funding.
3. Postgraduate certificates – distilling the advanced knowledge of a full master’s program into 6 to 12 months of learning, courses that lead to a postgraduate certificate in data science are gaining popularity among aspiring data professionals.

The short, typically online format of the latter undoubtedly makes these university data science courses the crowd favorite. But all three options share some common pros and cons, nonetheless.

Pros

You will find that regardless of the university ranking, data science courses at traditional institutions all boast:

1. Well-structured curriculums. Often the professionals who design and lead data science courses at universities are experienced instructors who can distill complex topics into easy-to-understand chunks and who also grasp the logical progression of topics.
2. Top-tier facilities. Whether it’s lab sessions or demonstrations, every university data science degree will feature some kind of practical element that draws on the on-site research capacities of the institution.
3. Strong community. A traditional degree is a great opportunity to build your data science network. From direct communication with instructors to team projects and other peer-to-peer events, you will have plenty of opportunities to make valuable connections in the field.

Cons

Universities are certainly doing a great job in serving their public duty of providing education in the areas that are most in need of fresh talent. At the same time, they are traditional institutions and as such have some all-too-familiar drawbacks:

1. Stringent entry requirements. In complete contrast to the MIT data science program, university courses usually entail lengthy (and costly) application procedures with multiple stages. On top of that, they require applicants to have either advanced mathematical skills, a previous higher education degree, or years of work experience.
2. High tuition costs. In the US tuition fees for a bachelor’s degree range between 20,000USD - 40,000USD a year. Postgraduate certificate courses rarely fall under 10,000USD. While master′s degrees can go up to 60,000USD/year.
3. Lots of theory. Because teaching at universities is mostly done by academics, the data science courses they offer tend to be very heavy on the theory (sometimes to the detriment of real-world practice.)

Online Data Science Courses

You can access data science courses from a wide variety of e-learning providers: from huge companies such as Coursera, Udemy, edX that offer courses on many topics besides data, to niche platforms such as DataCamp, DataQuest, and 365 Data Science. Similar to university degrees and the MIT data science program, you can expect expert-led tuition packaged in an online format. At the same time, all of these platforms employ very different business models and teaching styles which means you should consider some additional factors when making your choice.

Pros

Despite the many differences, online data science courses share these common perks:

1. Certificates. Perhaps the most widespread and appealing feature of e-learning platforms in general (and with good cause) are certificates of completion. These demonstrate you’ve passed a particular course successfully and have mastered a sought-after set of data skills. In addition to these standard credentials, 365 Data Science offers industry-recognized career track certificates that are more comprehensive and demonstrate to employers you have a wide range of capabilities as a data scientist, data analyst, or business analyst.
2. Practical skills. DataCamp, DataQuest, and 365 Data Science all put heavy emphasis on learning by doing. On all three platforms, you will find lots of real-world use cases, helpful demonstrations, and practice projects.
3. Self-paced tuition. While DataCamp and 365 Data Science both offer on-demand video lessons, DataQuest relies on practical examples and lots of reading. In any case, these platforms all allow you to learn at your own pace and combine your studies with your day job or other responsibilities.

Cons

Even just among the three main data-science-only platforms, there are significant differences that make some more appealing than others when it comes to building data science skills from the ground up. Here are what we consider the main drawbacks of e-learning providers specializing in data from the viewpoint of a complete beginner:

1. May require additional software. This is where DataQuest and DataCamp excel by offering coding in the browser capabilities – though it’s unlikely you will ever get asked to code in the browser at a job interview or in a work setting. 365 Data Science on the other hand encourages you to complete all the assignments in a dedicated coding environment (Python, SQL, or another), which you must download and install separately with guidance from your instructor.
2. Can be hard to find your feet. As a complete novice in data science, you might struggle to see the logical thread of the skills you need to acquire to get ahead. In that sense, DataCamp’s extensive library of courses is great for more experienced professionals looking to solve a particular problem but may be overwhelming for someone just starting out.
3. Staying motivated might be a challenge. As with all on-demand learning, you will have to keep yourself on track when pursuing an online data science program. This is where the lack of streamed video lessons makes DataQuest particularly challenging for beginners who are just discovering what appeals to them in data science.

Joined Data Science Courses

Harvard, Berkley, and Columbia are all among the world-class universities offering data science courses in conjunction with e-learning providers such as edX. As direct competitors to the MIT data science program, these ‘joined’ courses seem to offer the best of both worlds:

1. The world-class expertise of the institutions that design and lead the curriculums
2. Flexible, on-demand learning.

Aside from ostensible disparities in the tools that these courses cover (for example MIT focuses on Python, while Harvard prefers R), as well as differences in length and price, the courses in this category are all very similar.

Pros

In that sense, many of the same benefits found in the MIT data science program are also present in other joined courses:

1. Low barrier to entry. Some courses in this category require very basic mathematical skills, while others have no prerequisites at all.
2. Fully online. All the programs offered by universities in conjunction with e-learning platforms are entirely online.
3. Useful credentials. All joined courses offer certificates issued by the organization that created the course and the platform that distributes it. MicroMaster’s programs such as the ones by MIT and Adelaide also contribute credits towards further study.

Cons

As you can expect, the similarities in these programs don’t just end with the pros. When considering joined courses as an option, you must also be aware of:

1. High costs. The most inexpensive program in this category will set you back around 300USD, while the most expensive ones are in the range of 1,500USD.
2. Inflexible course schedules. As the programs are made up of individual courses, you have to be aware of when each starts and ends so you don’t miss your deadlines. This can feel restrictive even in an entirely online format.
3. Narrow range of skills. In general, joined data science programs are not very extensive. With as low as 4 courses on some and as many as 10 on others, they don’t offer much variety to beginner data scientists who are looking to gain a well-rounded skillset.

Considering how similar all the courses in this category are, when deciding on which one to take, make sure you consider factors such as – what kind of projects will you be working on, how relevant will the skills you gain be to your future career goals, can you afford the time and money investment?

Professional Data Science Courses

Large data-driven corporations offer specialized courses that help aspiring data scientists become familiar with core concepts in the field as well as the particular technologies used by their teams. The most popular among these are the IBM Data Science Professional Certificate and its alternatives – courses offered by Google, AWS, and Microsoft.

Pros

As concise introductions to data and analytics, these programs, drawing on the expertise of some of the largest companies in the world, are a fantastic way to:

1. Master some of the most popular technologies on the market. For example, learning to operate the Amazon cloud ecosystem is a highly valued skill that can land you a job as a data entry specialist, or a database expert at a company that relies on this particular cloud service without too much effort.
2. Make a change in your data science career. If you’re already working in data science, upskilling in the particular technologies used at IBM, Google, or Microsoft can offer a shortcut to a position with these companies. Alternatively, you may be one of their competitors in which case you’d probably be interested in gathering some intel.
3. Break into data science consulting. Consultants usually advocate for a particular technology they are most comfortable with. Take the example of IBM Consulting – their team primarily builds and deploys solutions in the IBM-native Watson studio environment.

Cons

If you’re looking for a wider range of skills, though, and don’t want to limit yourself to one particular technology early on in your data science journey, then maybe you should explore some of the other alternatives on this list. Other downsides you should consider may include:

1. Relatively high time and money investment. For example, the IBM data science certificate takes 6 months to complete and costs around 249USD. Keep in mind that a lot of that time will be spent learning about Watson studio and other technologies particular to IBM.
2. Can be confusing for beginners. If you just want to learn the basics of data science and analytics, the narrow focus of these professional data courses might distract you from your main goal.
3. Lack of community. It’s exceedingly difficult with some of these courses, particularly those offered through Coursera, to get in touch with your instructors or to put course-specific questions to your peers. 

MITx Data Science Program Alternatives: Next Steps

Now that you’ve had a taste of the enormous variety of data science courses on the market, you should be in a much better position to make the best choice. Besides that, you must feel at least a little inspired to keep learning and developing your skills. Whether this list solidified your initial decision to pursue the MITx data science program, or you are now considering other options, it’s important to remember that you’re not alone in your choice.

With over 2M graduates to date, 365 Data Science is a fast-growing community open to anyone who wants to upskill in data. We are committed to removing barriers to entry and offering our students a best-in-class learning experience with our signature-style animated lessons, a plethora of practice exams, quizzes, and case studies, as well as an expert-led curriculum designed with the beginner data scientist in mind.

Sing up for a free account to explore what our program has to offer and stay tuned for more free resources you can rely on to guide you through your data science journey.