15 Skills for Data Science and the Data Scientist
Data science is one of the highest professions for skills. Data scientists are sought after for their data taming and insight-generating abilities. The discipline presents an enticing career path for the analytically-minded.
While data scientists start their careers via many different pathways, there are core data science skills that you’ll need to develop. If you’re curious about the top skills for a data science career, this list of hard and soft skills is a great start. At the end, we identify the best skills for a junior data scientist to learn.
Hard Skills for Data Science
The hard skills required of a data scientist include statistics, programming, SQL, statistical modelling, transforming raw data into usable formats, and presenting data insights visually. It’s also beneficial to understand machine learning, natural language processing and scalable computing.
1. Statistics
Data scientists regularly use statistical and mathematical techniques in their data analysis work. A firm grounding in statistics and math is a fundamental data science skill.
You should be familiar with standard deviation, distribution curves, variance, probability, and statistical modelling and analysis. Likewise, top candidates benefit from past exposure to linear algebra, multivariable calculus, and similar mathematical concepts.
When you Google for the math requirements for data science, the three topics that consistently come up are calculus, linear algebra, and statistics. The good news is that — for most data science positions — the only kind of math you need to become intimately familiar with is statistics.
Mathematical ability is, however, notionally more important than being strong in specific areas connected with data science practice. If you’re good at math and have taken plenty of mathematics subjects before, you’re well positioned. You can quickly refresh or study certain topics as required by the daily demands of your data scientist job.
2. Programming
While data scientists typically aren’t software engineering experts, their work requires coding ability – particularly when cleaning messy datasets – and other computer science skills. As part of your responsibilities, you may need competency in code version control using tools like GitHub and Git.
Even as AI handles more coding tasks, programming knowledge is still needed to direct and check its output. The 2022 Kaggle survey found Python was the most widely used programming language among respondents, followed by SQL and R. Other programming languages you may encounter include C, C++, C#, Java, and Julia.
Related: How AI and Data Science Evolve Together
3. SQL
SQL allows data scientists to retrieve, filter, combine and summarise information stored in relational databases. Even when Python or R is used for analysis, SQL is often needed to obtain the data first.
You should be able to write queries, join tables, group records and work confidently with database structures. Strong SQL skills also help you investigate data quality problems before modelling begins.
4. Statistical modelling
A core skill for the data scientist is the ability to build and interpret statistical models. Regression analysis is one of the main techniques. When you run a regression, you establish how well a set of independent variables combine to predict the value of the dependent variable. For example, using a simple linear model (y = a + b * x), you might see how well the daily temperature for a city (x) predicts the amount of ice-cream purchased (y).
Businesses, economists and data scientists are all interested in the results of statistical modelling. For a data scientist, running regressions uncovers which known variables are most useful for predicting the dependent variable.
Linear and logistic regression are useful starting points. You should also know how to test assumptions, compare models and judge whether the results are reliable enough for business use.
5. Data wrangling
Data wrangling refers to data manipulation activities that help transform data from one format to another, especially converting unstructured data into usable forms. Other terms for it are data cleaning, data remediation and data munging.
Raw data may contain errors, inconsistencies and outliers that make a dataset initially unusable. By inspecting the data and writing transformation and filtering algorithms, you can overcome these deficiencies.
Data wrangling cleans, structures and enriches raw data so it can be used for analysis, data science and machine learning.
Data wrangling supports and complements database design and management and ultimately allows you to build models and perform deep data analysis. A data scientist should also be skilled at feature engineering, a process to transform raw data into usable features (properties, characteristics, attributes) for machine learning.
6. Data visualisation
Presenting data visually helps the data scientist understand patterns in the data better. The practice is also useful as a way to play with data and make discoveries. Expertise with data visualization software and techniques is also vital to showcase the facts you’ve unearthed. After all, data insights are useless if you cannot communicate them.
Tableau and Power BI are standard data visualization tools you should be good at using. It also helps if you know how to present data using multiple formats such as bar charts, pie charts, scatter plots, heat maps, line graphs, and more. You can use free software such as Google Charts and basic versions of Infogram and Microsoft Power BI to generate diagrams.
Building data visualization skills is really just a matter of practice. You can grab some data and try different software to produce visuals that are uncluttered and tell a story quickly. See what other data scientists are doing and keep practising your craft.
7. Machine learning
Machine learning (ML) is at the heart of data science’s most impactful results, from speech recognition to Netflix’s recommendation engine. All data scientists should have some familiarity with how to build a machine learning model, though you may not use your skills on a day-to-day basis. If you’re eager to get involved in machine learning, a good foundation in ML modelling is a must.
To make machine learning happen, you’ll need core data science skills plus the ability to produce models and algorithms that direct the learning process. Some of the competencies data scientists need for ML are knowledge of K-nearest neighbour, random forests, decision trees and K-means clustering. Deep learning is an advanced sub-field based on artificial neural networks.
8. Scalable computing
Large datasets present some of the most exciting opportunities for companies. Many essential data science skills apply equally well to large and small datasets. You’ll need extra computing capability, however, when data is too large or complex for ordinary desktop tools.
Spark and cloud platforms such as Azure, Amazon Web Services (AWS) and Google Cloud allow data scientists to store, process and analyse data at scale. Hadoop and MapReduce may still appear in established systems, but they are no longer the whole story.
9. Natural language processing
Natural language processing allows computers to analyse and generate human language. Data scientists may encounter NLP when working with customer feedback, search results, documents, speech or large language models.
NLP is a specialised area, and most data scientists do not need to understand the underlying technology in depth. A basic awareness of text classification, sentiment analysis and language models is normally enough unless you intend to specialise in the field.
Soft Skills for Data Science
Soft skills are the non-technical skills that are difficult to learn from a textbook or computer science course. They need to be cultivated for a data scientist to have impact in the workplace and enjoy a successful career. Here are key soft skills for data scientists to develop.
10. Problem-solving
Successful data scientists, from junior data scientists to experienced pros, love solving problems and relish the challenge of complex data questions. But keep in mind that the issues you’ll tackle aren’t merely theoretical; they will have real world implications that drive business results.
Top data scientists bring a solution-oriented approach to the role. You’re not there to simply solve interesting questions; you’re unearthing data insights that could change a business profoundly.
Pan Wu describes a problem-solving approach which illustrates how the job of a data scientist is to bring mathematical rigor to real-world issues. Generating a business solution has three stages: (1) Understand the problem and define it in mathematical terms (2) Decompose the problem, construct an algorithm solution and build it out and (3) Re-think the problem and solution in a business context to potentially generate non-incremental improvements.
11. Critical thinking
Critical thinking is pivotal to problem-solving, which is why it’s one of the top data science skills. You’ll routinely encounter questions that force you to analyse problems, see all the angles objectively, and reason analytically, all of which are central tenets of critical thinking.
Lateral thinking is perhaps a more apt term when it comes to challenges faced by data scientists. While one may be well trained in solving abstract problems or writing algorithms, thinking outside the box often counts more in terms of results. The non-obvious can easily make or break a project.
Rahul Agarwal identifies five business-orientated critical thinking skills: (1) re-check for data flaws that make your nice-looking datasets unclean (2) connect the business to the data clearly in your own mind to understand the data better (3) thoughtfully choose performance metrics for the particular case (4) be sceptical of high-level statistical findings and (5) be careful of simplifications and try to look deeper at data patterns.
12. Communication
Data scientists need to explain findings clearly through writing, speech and visuals. Don’t be surprised if decision-makers ignore results they don’t properly understand or cannot explain to anyone else.
Storytelling helps by giving the analysis a logical narrative, context and relatable examples.
If you want people to make the right decisions with data, you have to get in their head in a way they understand. Throughout human history, the way to do that has been with stories.
Good communicators also adjust to their audience. Some colleagues understand mathematics and statistics, while others need plain language. Presentation, listening, documentation and reporting are all part of the job.
13. Analytical judgement
Analytical judgement is the ability to recognise patterns, anomalies and stand-out features in data sets. Patterns may not always be readily apparent, so skilled data scientists know what to look for during exploratory data analysis or data mining.
An intuitive feel for data patterns might not be easily obtainable for newbies, often coming from great familiarity and experience with various data types. Thankfully, you can polish the skill by attending data science bootcamps and you’ll naturally improve over time.
14. Intellectual curiosity
Intellectual curiosity is the drive to find answers, even when they may not be apparent or easily extracted. Data scientists with intellectual curiosity are willing to entertain innovative ideas, challenge the status quo, and pursue results using creative methods.
The field requires tenacity, problem solving, and grit to dig into the details and realize that most projects are never really finished, but constantly evolving as we uncover more information.
Data science is about uncovering hidden truths and unearthing the most important secrets within data. The best data scientists don’t accept “just enough” but are committed to finding complete and valuable answers.
15. Collaboration
While data scientists have a reputation for being lone rangers, the ability to freely collaborate as required is an essential trait for success. This is a good soft skill to include on your data science skills resume. You can make yourself stand out by impressing people with openness, friendliness and willingness to make professional connections.
In your work, you’ll have to interact meaningfully with other data science professionals and non-technical colleagues within and outside your direct duties. So, you must recognise the importance of teamwork and actively seek and appreciate other team members’ input.
Skills Required for a Junior Data Scientist
Building up all the skills on this list takes time. Maybe you’re wondering about the essential skills for a junior data scientist, who is just starting out in data analytics.
To establish the technical skills required, we examined the subjects students take in a Graduate Certificate in Data Science. This is a 4-subject postgraduate course that provides the foundational training to get you started on a data science career.
Common skills initially taught to data scientists in-training are:
- Data science foundations (an overview of data science as a discipline as well as an introduction to key topics)
- Statistical methods for data science (probability distribution foundations for regression analysis and multivariate analysis)
- Database systems (skills and tools to create and use a database system using SQL)
- Data visualization (design approaches and techniques of data visualization using Tableau)
- Programming principles (an introduction to programming in Python, covering data structures and algorithms, debugging and testing, and simulation).
In writing this list of skills required to be a data scientist, even just a junior one, we noticed that the breadth of skills is large and somewhat daunting. But we were also reminded of a few factors from which you can take comfort from when learning to become a data scientist.
You can start with foundation skills and become at least a little familiar with each sub-field quite quickly. If you do an extended data science course, such as a Master of Data Science, you’ll build expertise across all major areas you need to cover. And, when working as a data scientist, specialization is common into different kinds of data science jobs. So, you can focus on a limited range of professional skills at any given time.
Which technical and soft skills are most important for building a successful career in data science?
which of these skills do you think employers value most when hiring junior data scientists?
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Thank you for sharing. It is very informative. You can also learn about data analytics with AI courses in Australia at Extratech.
What about emotional intelligence? Understanding your team and stakeholders is key to getting your data insights implemented.
Good point. EI goes beyond understanding people, and is maybe too large to include as a specific skill on your resume. It covers managing your own emotions, empathising with others, and handling social complexities. It also takes in self-awareness, self-regulation, and strong interpersonal skills.
This article is very helpful and gives a fine idea about where you stand right now on the way to becoming a data scientist. Turns out, I’m not at par. Am motivated and inspired to work harder and check all the points on this list asap.
This article really helped me decide on my path for higher education. I was torn about whether to go back and learn a new skill or set of skills, and this opened my eyes to the job opportunities available in data science. It would obviously be difficult to learn everything all at once, so you also need to be adaptible and willing to learn as you go.