Choosing the perfect Master’s in Data Science program is a tedious decision. With hundreds of good universities all over the world, it’s quite confusing. Hence, to help you make an informed decision, our team presents this well-researched complete guide to Masters in data science graduate programs in 2022.
- What is Data Science?
- What are the best Data Science Programs in 2023?
- But is a Master’s in Data Science necessary?
- Data Science Programs and related degrees
- Master’s in Data Science
- Master’s in Data Analytics
- Master’s in Business Analytics
- Things to watch for when applying for a university
- Safety Index
- Student support
- Program structure
- Difference between the online program and offline program
- Is the program available part-time or is it full-time only?
- Master’s in Data Science Program General Prerequisites
- Undergrad scores
- Academic requirements
- Application requirements
- Test scores
- Personal statement
- Recommendation letters
What is Data Science?
In Data Science, you’ll use various tools, algorithms, and scientific methods, all of this is done with one simple goal in mind – discovering hidden patterns from the data provided. These hidden patterns are insights, which are further used in decision-making for government welfare policies, and the business world.
What are the best data science programs in 2023?
There are multiple variables you should assess when choosing a university, and this takes a lot of research. But don’t worry, we have tried our best to help you with this. Check out the following two articles to find which are the best universities in Europe and USA.
But is a master’s in Data Science necessary?
Yes, a master’s in Data Science is necessary and essential. Why? For the following reasons-
- A master’s degree in a data-related specialization will make you an ‘unfirable’ employee.
- Employees holding a Master’s degree in the relevant specialization are paid much higher than others. For example, 90% of Data Scientists have a master’s degree, and 47% hold a PhD.
- Huge job opportunities. India is the second-highest country (First is the USA) to recruit employees in the field of data science with 93,500 positions unfilled. (Source)
- The fear of job loss due to automation is at an all-time high. Having a Master’s degree in Data Science will make you immune to this danger.
Data Science Programs and Related Degrees.
Master’s in Data Science is a widespread field. With specializations from data system administration to machine learning, there are varieties of programs to choose for your master’s degree. Let’s look at a few major ones.
Master’s in Data Science
A Master of Science in Data Science is an interdisciplinary degree program designed to provide studies in a broad range of data-related processes. This can be helpful in various careers such as Data Engineering, Data Architecture etc.
You will dive deeper into following topics
- Programming R, SQL, Python
- Practical Machine learning: Algorithms
- Data preparations and Database management
Master’s in Data Analytics
A master’s in data analytics is a good idea if you are planning to work outside the business context. You will become data architect learning to prepare data for advanced analytics. The programs focus on gaining mastery with the tools and techniques used for data governance and analysis.
You will dive deeper into following topics
- Data Analytics and Management
- Data Models and Visualization
Master’s in Business Analytics
This program can be termed as a mix of data analysis and business decision making. Business analytics professionals use analytical tools for improved business decision making. Unlike Master’s in Data Analytics students, business analytics students learn to work in a specific business domain.
Earning a master’s in business analytics can help you develop the skills needed to transform large amounts of data into actionable business strategy insights.
Things to watch for when applying for a university
When applying for the master’s the first thing you should do is make a list of the universities you want to apply for.
Following are things you should definitely check for when shortlisting a university.
Universities offering higher education go through a rigorous process of evaluation. This process evaluates whether these institutions meet educational standards. This is known as accreditation. Accreditations can be institutional and program(special).
Accreditation is important as it pushes institutions to meet and maintain their high standards.
So basically, an accreditation tells you the quality of a program or a university. While you are looking at the rankings of a university, you should also look at the accreditations.
Many aspirants miss out on the most important aspect of choosing a university, the safety of living there’. How would you even focus on your studies if you are always looking over your shoulder for a thief or even a murderer? So make sure you look at the safety ratings of the university you are applying for.
Credible source to check for safety index is Niche. You can also check the stats provided by local police body.
Master’s program in Data Science in many universities have a similar core structure. As a student, you can specialize in a particular section of Data Science. Data engineering, machine learning master’s, business analytics, artificial intelligence are few of the popular options you can look into.
You must understand that the specialization you choose will affect the job roles you receive in the future. Proper research of each program is crucial.
This aspect is an important one. A university must have a dedicated team to assist you in navigating the post-graduation world. The help could be in the form of personal counselling, on-campus networking events, tutoring in creating good quality resumes, tutoring in ‘how to face job interviews’.
An effective university program must provide you with decent internship opportunities and also have noteworthy placement record.
The program structure will play an important role in deciding at what pace the program will run. Programs generally have two types, cohort-based learning and self-paced learning. Cohort-based learning is a collaborative learning style in which a group of peers advance through a program together. Whereas self-paced learning is flexible, the university will you to finish the program at your own pace. All they ask for in return is you fulfilling their basic requirements.
A cohort-based learning program (although slow) has great benefits such as good networking, better understanding etc.
Differences between the online and offline programs
- Flexibility to study around a work schedule and leading family life.
- Such programs provide asynchronous course content, meaning you can study from any location.
- Online programs cost much cheaper than offline programs.
On-campus – Traditional program
- Face-to-face i.e. personal interaction with faculties and peers.
- Much better networking opportunities, able to attend networking events.
- Accessibility to student services.
Is the program available part time or is it full time only?
The main difference between a part-time and full-time program? It’s the test of patience. The part-time program is for students with a time crunch and some extra patience. A part-time program may end up being twice the length of a full-time program.
Pursuing a full-time degree means getting into the field sooner, and inching towards your data scientist dream faster. But part-time degrees are at times cheaper, so by paying less you’ll also have extra time to work part-time while finishing the curriculum.
Master’s in Data Science Program General Prerequisites
The following list provides checkboxes of requirements you must fulfil to even just apply for a university.
The prerequisites are in a general sense. That is, the prerequisites given are common in multiple high-end universities. The actual figures may vary for each university.
Generally, a GPA of 3.0 is necessary in a university ranked in the top 100 list in the world. This is even higher for leading universities.
Few universities at times even exempt a not satisfactory GPA score. In such cases, the application is assessed on a case to case basis and you are considered if you display outstanding test scores and professional achievements.
In our research, we observed that GPA requirements for Data Science are higher than that of other disciplines in the engineering field.
Academic requirements usually vary from program to program. But we’ll discuss the common ones here. Generally, a decent knowledge in Linear algebra, statistics and familiarity with introductory probability is enough on the maths side. This, coupled with working knowledge of high-level programming(Python/R) language is what universities look for.
Standardized tests such as GRE and TOEFL hold importance for admission process of only a few universities. For example, Harvard does not use a cutoff for your GRE. Whereas, UC Berkley insists on top 15% score in GRE.
These standards tend to vary according to school. So, it’s your job to conduct proper research. You should also consider contacting your admission counsellor to find out more.
A blog by Best colleges mentions how online colleges nowadays increasingly ignore standardized testing altogether. They are opting for more holistic methods of evaluation.
A personal statement is a short essay that is used by the admission committee to assess the student. It helps the committee to know the person behind the letter. The essay allows students to explain their academic history, work experience, and career goals.
A personal statement is slightly different than a ‘statement of purpose’. As the name suggests, a personal statement should have a personal touch. You can include significant life experiences or challenges that shaped you as a person.
This one is a universal requirement. Almost every university asks for recommendation letters. You are expected to submit 2-3 recommendation letters from professors, supervisors who can attest to your character, accomplishments and abilities from an objective perspective.