What Skills Data Scientists Need to Master in 2020?

by February 10, 2020 0 comments

Data Scientist

Without the help of experts, it is impossible to churn out the appropriate insights from the given data across any organization. The interdisciplinary field of data science is growing with great relevance and so do data scientists. They deal with processes and systems to extract knowledge or insights from large amounts of data.

It was estimated by McKinsey that big data initiatives in the US healthcare system “could account for US$300 billion to US$450 billion in reduced healthcare spending or 12 to 17 percent of the US$2.6 trillion baselines in US healthcare costs”. On the other hand, though, bad data is estimated to be costing the US roughly US$3.1 trillion a year.

Therefore, it is quite obvious that an enormous amount of data generated daily needs efficient processing and analysis and this is where the need for data scientists grows.

However, in the year 2020, it will not be an easy task to be a data scientist as with the accelerating jobs across market the skill-demand is also at its peak. Today the budding data-professionals need to be extra productive and effective with the following capabilities to mark their arrival in the market.


Non-Technical Skills

Ability to solve business problems

While expertise in programming languages and business communication are vital, they don’t mean much without an awareness of the business problems faced by the organization and how to solve them. This starts with the ability to translate business requirements into a data-driven problem that can be solved as part of a data science project.
It is worth noting that while some business challenges will be surfaced by company executives and departmental heads, the onus is on data scientists to identify real and present issues that may be unidentified or missed out even by stakeholders.

Finally, when it comes to dealing with people, data scientists will do well to remain cognizant of interpersonal sensitivities and inter-departmental rivalries. Mastering these aspects of the corporate environment can go a long way towards the successful completion of a data science initiative, instead of it being mired in endless bureaucratic delays.


Effective business communication

For all the brilliance of a data scientist in algorithms and modeling, all these would be for naught if the data scientist isn’t able to communicate their results. Aside from data visualization tools, this rests upon the ability of the data scientist to communicate the significance of his data findings.

Indeed, effective business communication is one of the most important skills for data scientists that are often overlooked. Specifically, a data scientist needs to be persuasive, whether it comes to probing stakeholders for their challenges, or when it comes to communicating analytical insights and solutions in a concise yet clear manner.



Agile is a method of organizing work that is already much used by dev teams. Data Science roles are filled more and more by people who’s original skillset is pure software development, and this gives rise to the role of Machine Learning Engineer.

More and more, Data Scientists/Machine Learning Engineers are managed as developers: continuously making improvements to Machine Learning elements in an existing codebase.

For this type of role, Data Scientists have to know the Agile way of working based on the Scrum method. It defines several roles for different people, and this role definition makes sure that continuous improvement and be implemented smoothly.



To become an accomplished Data Scientist, you need to channelize your Data Science learnings to accelerate the pace of the output to ensure the sustainable growth of your organization. You cannot do this alone. You have to collaborate with your team(technical and non-technical), stakeholders, and end-users. Thus, if you have the required people skills, you can collaborate with others to observe their pain points and overcome organizational challenges.

According to Indeed, in just three years, the number of Data Scientists’ job posting has increased by 78 percent. According to Glassdoor, Data Scientists ranked first among the 50 best jobs in the United States. Moreover, almost 60 percent of global companies cannot analyze or classify their data. This is why they are in desperate need of Data Scientists. Thus, ‘now’ is the perfect time to start a career and acquire all the skills for a data scientist.



It is always suggested that data science students must seek out new data sets and experiment, experiment, experiment! Data scientists can never get enough practice working with previously unknown data sources. Fortunately, the world is alive with data. It’s just a matter of matching the passions (environmental, economic, sports, crime stats) with available data so one can carry out the steps of the “data science process” to better hone his skills. The experience he gains from his own pet data experiments will only help him professionally down the line.


Data Storytelling

It’s always important to increase one’s data storytelling skills. This is probably the most difficult for data scientists since it’s a “soft” skill with a lot of creativity required.

This skill is all about networking, and interpersonal skills. It’s a path toward differentiating an efficient data scientist among his data science peers (because few do it well). One must engage with stakeholders and they will lift them when an organization needs it.


Technical Skills

According to a report, here are the following technical skills aspiring data scientists require to thrive in their field.



Git (a version control system that lets you manage and keep track of your source code history) and GitHub (a cloud-based hosting service that lets you manage Git repositories) are tools for developers that are of great help when managing different versions of the software. They track all changes that are made to a code base and in addition, they add ease in collaboration when multiple developers make changes to the same project at the same time.

For the role of data scientist, Git is becoming a serious job requirement and it takes time to get used to best practices for using Git. It is easy to start working on Git when one is working solo, but when he joins a team or collaboration with Git experts, he might struggle more.


Preparing for Production

Historically, the data scientist is the staff member who answers business questions with machine learning. But now data science projects are more and more often developed for production systems. At the same time, advanced types of models now require more and more compute and storage resources, especially when working with deep learning.
In terms of job descriptions for the position of data scientist, it’s important to think about the accuracy of the model, but it’s becoming equally important to work directly with data engineering members of the team to place data science solutions in production environments.



Apparently, the cloud is king for data science and machine learning in 2020 and beyond. Moving to compute and storage resources to cloud vendors like AWS, Microsoft Azure or Google Cloud makes it very easy and fast to set up a machine learning environment that can be accessed remotely. This requires data scientists to have a basic understanding of cloud infrastructure.

Knowledge of cloud is not mandatory but it’s getting that way. If one has this experience, it definitely works as a valuable skill-set. Some services to take a look are Google Colaboratory, Google ML Kit, Kaggle, IBM Watson, and NVIDIA Cloud.


Deep Learning

Deep learning, a class of machine learning best suited for specific problem domains like image recognition and NLP, has received a lot of press in 2019. But for more routine data science applications using structured/tabular data, routine machine learning algorithms like XGBoost, are recommended. As a result, it has been accepted for most data scientists to consider that image recognition and NLP as mere specializations of data science that not everyone needs to master.

Moving into 2020, however, the use cases for image classification and NLP are getting more and more frequent even in typical enterprise applications. Experts, therefore, recommend that all data scientists acquire at least basic knowledge of deep learning. Even if he does not have direct applications of deep learning in his current job, experimenting with an appropriate data set will allow him to understand the steps required if the need arises in the future.


Math and Statistics

Knowledge of various machine learning techniques is integral to being a data scientist. The machine learning experience is a primary differentiator from a data analyst. A fundamental understanding of the mathematical foundation for machine learning is critical to avoid just guessing at hyperparameter values when tuning algorithms. Knowledge of Calculus (e.g. partial differential equations), linear algebra, statistics (including Bayesian theory), and probability theory is important to understand how machine learning algorithms work.

Experts recommend that data science students should strive to understand the theoretical basis of machine learning found in “The Machine Learning Bible,” Elements of Statistical Learning, by Hastie, Tibshirani, and Friedman.



Many times a data sets for a data science project come from an enterprise relational database, so SQL becomes a conduit for acquiring data. One should be well versed in SQL to gain maximum benefit for data acquisition. In addition, using R packages like sqldf is a great way to query data in a data frame using SQL.



The idea behind AutoML tools is to expand the capabilities of a resource, the data scientist that is in short supply. By automating many of the routine tasks carried out by the data scientist, training and evaluating machine learning models, more work can be achieved with a smaller team. The technology is being taken seriously by many companies, so to widen the experience with all available tools, it would be wise for budding professionals to take a closer look.


Data Visualization

Data visualization is a remarkable thing one can do with data. It is the best way to showcase the results coming from a machine learning algorithm. It’s a primary ingredient to data storytelling. With only a few non-technical words of description during a presentation for project stakeholders, key results will be understood if one has a well-crafted visualization. Experts always look for new data visualization techniques (using newly discovered packages to make the process easy) as this skill is a key to success for data science projects.

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