Data science skills

15 Skills for Data Science

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In terms of skills, data science would have to be one of the top professions. Data scientists are highly sought after for their data wrangling and insight-generating abilities. The discipline presents an enticing career path for analytically-minded students and professionals seeking a new experience. 

While data science experts start their careers through various pathways, there are specific skills common to successful candidates. If you’re curious about the must-have skills for a career in data science, the following list of hard and soft skills for data scientists is a great start. At the end, we identify the best skills for a junior data scientist to learn.

Hard Skills for Data Science 

Hard skill: coding

1. Statistics and mathematics

Since data scientists regularly employ statistical and mathematical techniques in their work, it’s no surprise that a firm grounding in statistics and math is fundamental. Familiarity with standard deviation, distribution curves, variance, probability, and statistical modelling and analysis will be pivotal. 

Likewise, top candidates will be fluent in linear algebra, multivariable calculus, and similar mathematical concepts. 

2. Programming 

While data scientists typically aren’t software engineering experts, their work requires coding ability – particularly when cleaning messy datasets. Additionally, as part of your responsibilities, you may need competency in code version control using tools like GitHub and Git.

According to Kaggle, Python is the most common programming language used by data scientists, followed by SQL and R. Other programming languages you may encounter in the profession include C, C++, C#, Java, and Julia. 

3. Regression analysis 

Competence in regression analysis is essential, primarily due to the modelling fundamentals it teaches. It is a widely-used analysis method in data science tasks, often including the building of classification or predictive models. 

You’ll typically employ these models in uncovering linear relationships between variables and the insights that can prove helpful for business purposes. Fluency in logistic regression will be an added advantage since it builds on basic regression skills for greater flexibility in analysing data sets. 

4. Data wrangling 

Data wrangling refers to activities that help transform data from one format to another. When building models or performing deep analysis, you’ll need proficiency in manipulating data, and that means familiarity with data wrangling. 

You should also be skilled at feature engineering, which is key for extracting raw data features and database design and management. For example, you’ll likely need to explore and combine different databases or build data pipelines to centralise data assets in your work. 

5. Data visualisation 

Remember that data insights are useless if you cannot communicate them. Expertly showcasing the facts you’ve unearthed requires expertise with data visualisation tools and techniques. 

Tableau, Power BI, and D.js are standard data visualisation tools you should be good at using. It also helps if you know how to present data in various ways using bar charts, pie charts, scatter plots, heat maps, line graphs, and more. 

6. Machine learning 

Machine learning is at the heart of data science’s most impactful results, from speech recognition to Netflix’s recommendation engine. So if you’re eager to do similarly relevant work, a good foundation in machine learning is a must. 

Some of the competencies you’ll possess include knowledge of K-nearest neighbour, random forests, decision trees, K-means clustering, and deep learning. 

7. Big Data frameworks

Big Data presents some of the most exciting opportunities for companies, including its potential to usher in the internet of everywhere (IoE). But contributing to this vertical requires an aptitude for Big Data tools like Apache Hadoop, Apache Spark, and MapReduce. 

Exploiting massive data assets also demands expert-level familiarity with cloud ecosystems like Azure, AWS, Google Cloud, IBM Cloud. 

8. Natural language processing 

If you prefer a career path that focuses on human-machine interaction, proficiency in natural language processing will be important. 

While you’ll employ most of the tools and competencies already listed above, your work will tend towards teaching algorithms to recognise and correctly interpret human speech. 

Soft Skills for Data Science

Businesspeople smiling

9. 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. 

Therefore, top candidates will bring a solution-oriented approach to the role. You’ll recognise that you’re not simply solving interesting questions; you’re unearthing data insights that could change the world. 

10. Critical thinking 

Critical thinking is pivotal to problem-solving, which is why it’s a must-have skill in data science.

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.

11. Storytelling

Storytelling aids data science professionals in communicating data insights clearly and logically. It adds context and weight to data results, helping decision-makers appreciate your perspective and the implications of the results. 

Additionally, storytelling helps create a narrative that makes data results easier to understand for non-technical people. 

12. Communication

As with all professional roles that involve collaboration with others, good communication skills are a basic requirement for a career in data science. In fact, great communication is often one of the best indicators of workplace excellence, according to Google

You should be able to convey your thoughts clearly, whether in written or spoken communication. Likewise, documentation and reporting are routine tasks in data science, and doing them well requires excellent communication skills. 

13. Data intuition 

Data intuition is the ability to instinctively recognise patterns and stand out features in data sets. These patterns may not always be readily apparent, so skilled data scientists know when to look beneath the surface and what to look for.  

However, this skill might not be easily obtainable for data science newbies. It often comes from great familiarity and experience with various data types. Thankfully, you can polish the skill by attending data science bootcamps. 

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. 

Data science is about uncovering hidden truths and unearthing the most important secrets within data. So they don’t accept “just enough” but are committed to finding complete and valuable answers. 

15. Collaboration 

Lastly, while data scientists have a reputation for being lone rangers, collaboration is an equally important trait for success in these roles. This is a good soft skill to include on your data science skills resume.

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, it’s critical to recognise the importance of teamwork and learn to actively seek and appreciate other team members’ input.

Skills Required for a Junior Data Scientist

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.

To establish the base 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 visualisation (design approaches and techniques of data visualisation using Tableau)
  • Programming principles (an introduction to programming in Python, covering data structures and algorithms, debugging and testing, and simulation).

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