The Top 5 Data Science Qualifications

1. Introduction
2. Programming
3. Business Savvy and Intelligence
4. Statistics and Mathematics
5. Machine Learning
6. Visualization
7. Summary
8. References

As someone who has interviewed with several companies for Data Scientist positions, as well as someone who has searched and explored countless required qualifications for interviews, I have compiled my top five Data Science qualifications. These qualifications are not only expected to be required by the time of interview, but also just important qualifications to keep in mind at your current work, even if you are not interviewing. Data Science is always evolving so it is critical to be aware of new technologies within the field. These requirements may differ from your personal experiences, so keep in mind this article is stemming from my opinion as a professional Data Scientist. These qualifications will be described as key skills, concepts, and various experiences that are expected to have before entering the new role or current role. Keep reading if you would like to learn more about the top five Data Science qualifications for interviewing and/or for your current job as a Data Scientist.

As a Data Scientist, when you first study, you might be surprised to find out that coding and programming are often skipped in the curriculum (sometimes), as the programs you might enroll in are already expecting you to know how to code. However, it is incredibly important to have some sort of proficiency in a programming language. You may first learn advanced mathematics, statistics, Machine Learning algorithms, general theory, and Data Science processes before learning how to code. Do not be overwhelmed if this situation is your case, as there is no better time to learn than now.

In my programming experience, I actually learned SAS first, then moved onto R, then finally on to Python. I think this progression is a nice way to slowly ease into programming. However, sometimes it is best to just jump into more object-oriented programming right away if you are in a rush to learn everything Data Science. But, in my case, I focused mainly on statistics and SAS was a great platform for turning theory into practice from the start.

SAS

SAS [3] stands for Statistical Analysis System. In this programming language, you can perform most of the statistical approaches of Data Science in some way. The main functions are to perform data manipulation, descriptive statistics, and reporting. Here are the main facets of the SAS product that they highlight:

• intuitive and flexible programming
• libraries with common procedures
• automated management and monitoring
• data analysis tools
• cross-platform and multi-platform support

The benefits that I have experienced with SAS are the amount of statistical power and practical procedures that are not necessarily as common and robust as in other languages like R or Python. Import statements include, but are not limited to PROC GLM, which includes regression, multiple regression, ANOVA (analysis of variance), partial correlation, and MONOVA (multivariate analysis of variance). In addition to these different analyses, you can also visualize and describe your data with plots. Some of the ones that I have used the most are:

Fit Diagnostics:RStudentQuantileCook’s D

Overall, this is a great first programming language to learn as a Data Scientist as it serves as a proper transition from theory to practical application, especially with statistical importance.

R

This next language, R [4], is a step above SAS in the fact that you can use Machine Learning centered programming too. With this programming language and the addition of RStudio [5], you can also create valuable statistical solutions and descriptive plots. The process of using R code with Data Science applications usually starts with importing your dataset, importing your libraries, examining the data — with and without plots, and finally, building models. Some of the Machine Learning algorithms that I have used with R programming include LDA, KNN, and Random Forest. There are much more, but it is similar to Python and sklearn, which I will be discussing below. I always like to think of R as a balance of SAS and Python. Ultimately, it is up to you and the company you are applying for if you want to have this qualification. Some companies use it, some do not. If you like it, then you should find a company that requires R, especially since switching from R to Python can sometimes cause confusion and hence, slow down productivity.

Here are some of the reasons why I like R:

— statistical power

— visualizations

— documentation

Python

I prefer to use Python over R, mainly because it is easier to integrate with a company’s current infrastructure and codebase. I have not experienced too many companies that use R over Python. In addition to this benefit, I also feel like there are more Machine Learning libraries in Python. Some of my favorite libraries in Python include sklearn, TensorFlow, and seaborn. It is also useful to use Python when I am working alongside Software Engineers and Data Engineers. I find there to be more documentation on products that use Python for Data Science applications as well.

Here are some of the reasons I use Python:

• ability to use powerful Machine Learning libraries
• versatility in deployment and production
• prefer to use Python in a Jupyter Notebook over R in RStudio

Next, I will take a break from the more technical qualifications and discuss the business side of Data Science.

This next qualification is often skipped with certifications and general educational experiences. Being business savvy and business intelligent means understanding the business well and knowing why you need Data Science in the first place. It can be easy to start applying advanced Machine Learning algorithms right away to the company data, but the business use case needs to be established and thoroughly vetted in order to provide the biggest return on investment. For example, if you are able to classify birds with some computer vision algorithm, you have to understand why classifying them is useful. Is it because it will be more efficient? Is it because manual, human classification is inaccurate? In addition to understanding the business problem, you will usually need to work with a Product Manager to lay down how much money and time your Data Science project will save your company.

Once you have an understanding of the needs of the business, and get used to finding the needs of the business faster, you will become well qualified in the business aspect of Data Science. You may need to provide proof of why the algorithm you chose is useful in solving this problem. Once you get buy-in, you can then work and improve upon the current process and start to show off your algorithmic results.

Here are some ways that you can ensure you have the business savvy and intelligence qualification:

• learn products, common pitfalls, and popular product solutions
• practice or study product management
• have a strong knowledge of data analysis
• understand key metrics for any business (e.g., clicks per user, etc.)

Overall, employing and studying business analysis in relation to Data Science, is an incredibly critical qualification to have on your resume and current job.

While this qualification might seem obvious, sometimes you can focus more on the libraries that perform a lot of the statistics and mathematics for you. Assuming you already are proficient in using Machine Learning libraries or packages, and if you have at least a general understanding of statistical calculations, you will then be well qualified. Certain mathematics and statistics can be especially useful to know when you are conducting experiments that deal with significance.

Some important Data Science statistics that can help you to be qualified include the following:

• hypothesis-testing
• probability distributions
• Bayesian thinking
• oversampling (and under)

I recommend using your GitHub account to show a company your aptitude in statistics and mathematics by writing your own functions and discussing the significance of tests.

This qualification is more of a reminder that there is almost a new, best Machine Learning algorithm every year that you should be studying and practicing. For instance, many Data Scientists were using the Random Forest Machine Learning algorithm, and then later realized that all of the Data Science competitions had been using XGBoost instead. Therefore, it is beneficial to keep up-to-date in the Data Science community. You are not guaranteed to have this knowledge handed down to you so it is important to look for it yourself.

A particularly prominent site for this updated knowledge is Kaggle [9]. This site serves as a Data Science community where you can collaborate, share your code, learn, and ask questions about Data Science.

Their main products consist of:

• competitions
• datasets
• notebooks
• learn

Overall, of course, practice the main Machine Learning algorithms, especially with an example use case for each one, and explore new algorithms that might be even more powerful than the ones before. Setting up a GitHub account with your code, notebook, and examples is a great way to fulfill this Machine Learning qualification.

Lastly, is the visualization qualification. As a Data Scientist, it is important to know how to code, use Machine Learning algorithms, and have a great sense of business, so one of the ways that you can tie all of these facets together is by visualizing.

Here are some popular and useful visualization tools:

• Tableau
• Google Data Studio
• Looker
• MatPlotLib
• Seaborn
• Pandas Profiling
• new Python libraries that include stored visualizations

Some of the ways you can visualize the Data Science process is with exploratory data analysis, business problems and its respective data, error metrics or the accuracy, and how the results of the Data Science model have improved the business.

Data Science requires a lot, and interviews can be daunting; being well-qualified is one way to calm yourself and introduce more confidence into yourself. The top Data Science qualifications that I have stood by, include the following:

1. Programming2. Business Savvy and Intelligence3. Statistics and Mathematics4. Machine Learning5. Visualization

If you have practiced the above qualifications, then you will be a well-qualified Data Scientist. Having examples of programming, a business use case, an understanding of statistics and mathematics, several examples of Machine Learning algorithms, and an overall sense of visualizing your process and results, will allow you to either land the job, or make you a better Data Scientist.

I hope you found my article both interesting and useful. Please feel free to comment down below if you agree or disagree with the Data Science qualifications that I have discussed. Have you fulfilled these qualifications?

These are my opinions, and I am not affiliated with any of these companies. Thank you for reading!

Please feel free to check out my profile and other articles, as well as reach out to me on LinkedIn.

[1] Photo by LinkedIn Sales Navigator on Unsplash, (2017)

[2] Photo by James Harrison on Unsplash, (2020)

[3] 2021 SAS Institute Inc., SAS, (2021)

[4] The R Foundation, R, (2021)

[5] 2021 RStudio, PBC, RStudio, (2021)

[6] Photo by Austin Distel on Unsplash, (2019)

[7] Photo by Jeswin Thomas on Unsplash, (2020)

[8] Photo by Arseny Togulev on Unsplash, (2019)

[9] Kaggle Inc., Kaggle, (2021)

[10] Photo by William Iven on Unsplash, (2015)