Top Most In-Demand Data Science Skills

Top skills that are required to excel in Data Science Field

6 min readMar 12, 2022

#0 Data Engineering

If you’re dealing with small datasets, data engineering is essentially entering some numbers into a spreadsheet.

When you operate at a more impressive scale, data engineering becomes a sophisticated discipline in its own right. Someone on your team will need to take responsibility for dealing with the tricky engineering aspects of delivering data that the rest of your staff can work with.

#1 Decision-Science

Make sure you have a decision-scientists who understands the art and science of data-driven decision-making.

Decision-Science skills have to be in place before a team can get value out of data.

Decision Science is responsible for identifying decisions worth making with data, framing them (everything from designing metrics to calling the shots on statistical assumptions), and determining the required level of analytical rigour (quality of being extremely thorough and careful), Based on the potential impact on the business.

Practising decision Science will help to avoid someone saying this “Oh, whoops, that didn’t even occur to me as I was thinking through this decision.” It helps them already thought of it. And that. And that too.

Decision Scientist = half of decision-maker + half of data scientist. They are force-multiplier. Unfortunately, they’re rare and hard to hire.

If you’re lucky enough to hire one of these, hold on to them and never let them go. Learn more about this expertise here.

#2 Data Analytics

Everyone is qualified to look at data and get inspired, the only thing that might be missing is a bit of familiarity with software that’s well-suited for the job.

Learning to use tools like R and Python is just an upgrade over MS Paint for data visualization; they’re simply more versatile tools for looking at a wider variety of datasets than just red-green-blue pixel matrices.

If the entire workforce is empowered to do that, you’ll have a much better advantage on your business than if no one is looking at any data at all.

The important thing to remember is that you shouldn’t come to conclusions beyond your data.

That takes specialist training. Experience will make you look at more data faster. The game here is speed, exploration, discovery… fun! (Another term for analytics is data mining.)

This expertise is not concerned with rigour and careful conclusions. Instead, Data Analyst is the person who helps your team get eyes on as much of your data as possible so that your decision-maker can get a sense of what’s worth pursuing with more care.

The job here is speed, encountering potential insights as quickly as possible.

#3 Statistics

Statistics will help around to put a damper on the uncontrolled excitement after discovering new insights. Statistics prevent the team from making unwarranted conclusions and it help decision-makers come to conclusions safely beyond the data.

Inspiration is cheap, but rigour is expensive.

For example, if your machine learning system worked in one dataset, all you can safely conclude is that it worked in that dataset. Will it work when it’s running in production? Should you launch it? You need Statistical skills to deal with those questions.

If you want to make serious decisions where you don’t have perfect facts, let’s slow down and take a careful approach.

#4 Applied Machine Learning

An applied machine learning best attribute is not an understanding of how algorithms work. Machine learning is essentially making thing-labeling recipes using examples instead of instructions.

Applied ML focuses on using algorithms, not building them.

Applied machine learning can involve either supervised models, meaning that there is an algorithm that improves itself on the basis of labelled training data, or unsupervised models, in which the inferences and analyses are drawn from data that is unlabeled.

Applied machine learning is characterized in general by the use of statistical algorithms and techniques to make sense of, categorize, and manipulate data.

A fundamental of ML is pattern recognition which refers to the process of putting a label on specific data based on regularities.

Applied Machine Learning Engineer will run the data through a bunch of algorithms as quickly as possible and see if it seems to be working… with the reasonable expectation that they’ll fail a lot before you succeed. A huge part of the job is immersing blindly, and it takes a certain kind of personality to enjoy that.

Perfectionists tend to struggle as Applied ML Experts

Because your business problem’s not in a textbook, you can’t know in advance what will work, so you can’t expect to get a perfect result on the first go. That’s okay, just try lots of approaches as quickly as possible and iterate towards a solution.

The strongest applied ML engineers have a very good sense of how long it takes to apply various approaches.

#5 Qualitative Research

Many don’t realize how valuable qualitative researchers are. They’re usually better equipped at translating the intuitions and intentions of a decision-maker into concrete metrics.

Qualitative researchers help the decision-makers clarify ideas, examine all the angles, and turn ambiguous intuitions into well-thought-through instructions in a language that makes it easy for the rest of the team to execute.

Augment your data science team with a Qualitative Research.

Qualitative Research team typically consists of social science and data background — behavioural economists, neuroeconomics, and psychologists. They’re also a trusted advisor, a brainstorming companion, and a sounding board for a decision-maker. Having them on board is a great way to ensure that the project starts out in the right direction.

#6 Experts who will enhance your Data Science Team.

Data collection specialist
Domain expert
Data product manager
Ethicist
Interactive visualizer / graphic designer
Reliability engineer
UX designer

Many projects can’t do without them — the only reason they aren’t mentioned in my priority list is, Data Science is not their primary business.

After reading all that, you might feel overwhelmed that there is so much Expertise! Depending on your needs, you may get enough value from the first few expertise themselves.