Do You Need a Master’s Degree to Become a Data Scientist?

Data science is a hot topic. The Internet is full of heated discussions about how to become a data scientist and whether you need an academic degree to do so. In this article, I will try to dispel any doubts on this subject. Read on!

The potential of creating business value from data has attracted the attention of many. Organizations in a wide range of industries have started to invest in data science to take advantage of this potential. As a result, data science has recently experienced a tremendous increase in popularity.

What comes with this popularity is the high demand for data scientists. Thus, many people from different professions have made a change toward a career in data. The transition, of course, requires learning not only theoretical knowledge but also software tools and packages.

Luckily, there are many options for learning data science. Getting a master’s degree is an option. However, you do not need a master’s degree to become a data scientist. There are a ton of online resources that are more practical compared to getting a master’s degree.

Your skills and knowledge matter more than having a title. At job interviews, you will be asked questions that test your knowledge. What will make you stand out are your answers, not a master’s degree.

Feel free to go through the 15 Python interview questions for data science jobs and test your knowledge. Nobody will care about your master’s degree if you answer the questions thoroughly.

In this article, I will try to explain why a master’s degree is not the optimal path for becoming a data scientist. I will also discuss an alternative learning path that is cheaper and more efficient.

Why Not a Master’s Degree?

First of all, you take classes with other students when you’re in a master’s degree program. The pace of learning might be very different for each student. To account for this, the instructor teaches at a pace that considers every student. If you are a fast learner or already know some of the basics, you are likely to waste time in a class environment. You may even get bored and lose a bit of motivation.

Another downside with a master’s degree program could be the inflexible scheduling since you need to adjust your life around your classes. For example, if you already have a job, it might be difficult to schedule classes.

In contrast, using online resources provides you the flexibility you need. You can learn at your own pace and whenever you have time. Online resources offer a tailor-made plan that fits your preferences the best.

Second, data science is an extremely broad subject. Although the fundamentals are the same, how you approach and solve a problem with data is different depending on the field of application. For instance, if you want to work in finance, you need to have extensive knowledge of time series analysis. If you want to do natural language processing (NLP), you probably do not want to spend your time learning time series analysis.

When applying for data scientist jobs and other jobs related to data, specializing in a subfield makes your resume stand out and puts you ahead of the competition. A master’s degree program is likely to teach you data science from a general point of view. You can still take some elective courses in a particular field, but it will not be as flexible as learning on your own.

You can easily find online resources in a specific field. After learning the basics, you can spend time on a particular domain. Thanks to the rich selection of online resources, you have the freedom to choose a course on any subject.

Last but not least, a master’s degree program in data science is quite expensive. Sure, a data science job pays well. However, not everyone has the financial resources to take on the expenses of a formal degree program to make a career change. This may be especially true for those choosing to do so for a better income. Online resources are much cheaper than a master’s degree program.

What Are the Alternatives?

You do not need a master’s degree to become a data scientist. That said, you do need a proper, well-structured alternative. The number of online resources is almost infinite, and it is up to you to use them efficiently and wisely.

Let’s start by laying out the most critical skills a data scientist should have:

• Python
• SQL
• Statistics
• Data cleaning and manipulation
• Data visualization

You need software tools and packages to do data science. Python is the most preferred programming language among data scientists for a few reasons. It is easy to learn and has an understandable syntax. The rich selection of data science libraries also contributes to Python’s popularity.

This Python track for data science is a great resource for learning Python for an aspiring data scientist. The interactive dashboard makes it easier to practice, which is key to learning a new programming language.

SQL is another must-have skill for a data scientist. It is a programming language used for managing data stored in a relational database. Since most organizations store at least some data in relational databases, having a decent level of SQL knowledge will make you stand out as a candidate data scientist.

SQL stands for Structured Query Language. However, it is capable of doing much more than just querying a database. SQL has several functions and statements that make it a highly efficient data analysis and manipulation tool.

LearnSQL.com is a great platform for learning SQL. It offers a full track as well as several mini tracks. You will also have a chance to practice a lot, which is fundamental for learning a new programming language.

Data science is all about creating value of some form using data. The first step for turning data into value is to understand the data very well.

It is an interdisciplinary field, and one of the core disciplines is statistics. You might have heard some call machine learning “glorified statistics.” Statistics can be considered the most impactful tool to understand, interpret, and evaluate data.

Real-life data is usually messy and requires a lot of cleaning and preprocessing. In most cases, as a data scientist, it will be your job to preprocess raw data. This step is vital for the tasks that follow. For instance, if you are creating a machine learning model, its performance will be greatly impacted by the input data quality. Garbage in, garbage out!

Python has very practical libraries for data analysis and manipulation such as Pandas and NumPy. They provide several functions and methods to expedite and make data preprocessing tasks easier. Here is an article that involves some cool Pandas and Python tricks.

As with many professions, storytelling is important for data science. It does not matter how effective your product is unless you can demonstrate it. Simply looking at numbers is not so appealing for many people, especially for those with non-technical backgrounds. Not only do you need to be able to explain your models, findings, or results, how you explain them should be concise and intuitive.

I think storytelling is a soft skill that will make one a better data scientist. One aspect of storytelling is how you explain things, and another is how you demonstrate them. Data visualization is of crucial importance for impactful demonstrations. As a well-known saying goes, a picture is worth a thousand words.

There are many data visualization libraries in the Python ecosystem, such as Matplotlib, Seaborn, and Altair. They allow for creating highly informative visualizations with a few lines of code.

Learn Data Science Properly and Efficiently

Having a degree is not the main requirement to become a data scientist. Your skills are what really matter. If you gain the skills mentioned in this article, your chances of landing a data scientist job will increase substantially. I believe the data science community is on the same page about what to learn for data science; how you learn is totally up to you.

We are lucky to have a tremendous volume of online resources. It is quite easy to access them as well. You can make use of them whenever and wherever you want.

A master’s degree program is a valid option for learning data science. However, as mentioned, it is much more expensive than using online resources, and it does not have a flexible schedule and environment.

That said, it is important to emphasize that the vast array of online resources might turn into a disadvantage if not used wisely. You need a well-structured plan to make the most of them. LearnPython.com offers many tracks for learning Python and data science properly and efficiently. Check it out!