7 Skills Every Data Scientist Should Have

Data scientists use data to determine which questions teams should ask and help teams answer those questions by creating algorithms and data models to forecast outcomes. Their insights are used in business decisions to help drive profitability or innovation.

Data scientists need technical skills, such as manoeuvring and wrangling massive amounts of data, to make sense of it all. However, these professionals also need interpersonal skills since data scientists work collaboratively with business analysts and data analysts to conduct analyses and communicate their findings to stakeholders.

Delve into the skills every data scientist should have—and some classes you can take to build them.

7 essential skills for a data scientist

As a data scientist, you’ll engage in diverse activities, including gathering and analysing data, evaluating data security, and explaining data-driven insights to technical and non-technical colleagues and stakeholders. To accomplish those tasks and embark on a successful career as a data scientist, you’ll need a robust skill set, including the seven detailed below. 

1. Programming

Programming languages, such as Python or R, are necessary for data scientists to sort, analyse, and manage large amounts of data (commonly called “big data”). As a data scientist just starting, you should know the basic data science concepts and begin familiarising yourself with Python. Popular programming languages include:

• Python

• R

• SAS

• SQL

2. Statistics and probability

Data scientists need to learn statistics and probability to write high-quality machine-learning models and algorithms. For machine learning, it is essential to use statistical analysis concepts like linear regression. Data scientists need to be able to collect, interpret, organise, and present data and fully comprehend concepts like mean, median, mode, variance, and standard deviation. The following lists different types of statistical techniques you should know:

• Probability distributions

• Over and undersampling

• Bayesian and frequentist statistics

• Dimension reduction

3. Data wrangling and database management

Data wrangling involves cleaning and organising complex data sets to make them easier to access and analyse. Manipulating the data to categorise it by patterns and trends and to correct any input data values can be time-consuming but necessary to make data-driven decisions. It is also related to understanding database management—you will need to extract data from different sources, transform it into a suitable format for query and analysis, and then load it into a data warehouse system. Helpful tools for data wrangling include:

• Altair

• Talend

• Alteryx

• Trifacta

• Tamr

Database management tools include:

• MySQL

• MongoDB

• Oracle

4. Machine learning and deep learning

As a data scientist, you’ll want to immerse yourself in machine learning and deep learning. Incorporating these techniques helps you improve as a data scientist because you’ll be able to gather and synthesise data more efficiently while also predicting the outcomes of future data sets. For example, using linear regression, you can forecast how many clients your company will have based on the previous month’s data. Later, you can boost your knowledge by including more sophisticated models like Random Forest. Some machine learning algorithms to know include:

• Linear regression

• Logistic regression

• Naive Bayes

• Decision tree

• Random forest algorithm

• K-nearest neighbour (KNN)

• K means algorithm

5. Data visualisation

You need to know how to analyse, organise, and categorise data, and you’ll also want to build your skills in data visualisation. Creating charts and graphs is essential to being a data scientist. With solid visualisation skills, you can present your work to stakeholders so that the data tells a compelling story of the business insights. Familiarity with the following tools should prepare you well:

• Tableau

• Microsoft Excel

• PowerBI                         

6. Cloud computing

As a data scientist, you'll likely need to use cloud computing tools that help you analyse and visualise data stored in cloud platforms. Some certifications will specifically focus on cloud services, such as:

• Amazon Web Service (AWS)

• Microsoft Azure

• Google Cloud

These tools provide data professionals access to cloud-based databases and frameworks associated with advancing technology. Many industries use them, making it vital to become familiar with the concepts behind cloud computing.

7. Interpersonal skills

You’ll want to develop workplace skills such as communication to form strong working relationships with your team members and be able to present your findings to stakeholders. Just as data visualisation is vital for communicating the data insights you uncover as a data scientist, so is being able to collaborate with teams successfully. Check out some interpersonal skills you can build upon:

• Active listening

• Effective communication skills

• Sharing feedback

• Attention to detail

• Leadership

• Empathy

• Public speaking

How to develop your data science skills

Now that you know what skills you want to work on, how? Whether you are a data science novice or a seasoned data scientist, the following tips can help you improve your skills.

Sign up for a course, certificate, or bootcamp.

Once you've decided to build your programming, database management, or cloud computing skills, you may want to enroll in an online course or certificate programme.

Another option is a data science bootcamp, which can be done either in person or online. These are intensive, often full-time, and immersive so that you can learn quickly and efficiently over a few weeks or months. While this is a great way to advance your career or switch careers, taking time off work can be a privilege.

The IBM Data Science Professional Certificate gave me a lot of confidence. I never saw myself as a computer person, but the programme has you do all these complicated-seeming things like working in the Cloud and connecting to APIs, and it was so cool to me to see how easy Watson Studio actually was to use and how much you could do on it.

— Sam B.

Immerse yourself in the media.

Plenty of media can help you learn the terminology and become familiar with trends in data science. Examples include:

• Blogs

• Books

• Podcasts

• YouTube videos

Get involved in the community.

Learning from others in the industry can help you gain a network of individuals who could become your future peers or mentors. The following list provides ideas for you to get involved:

• Network: Find data science communities near you and attend networking events, panels, and happy hours. Some of these virtual events open your opportunities beyond your immediate location. For such events, you can also seek out online communities on Slack, MeetUp, Discord, Facebook, and more.

• Attend a conference: These days, you can find data science conferences for nearly any niche, making it easier to listen to talks and meet new people in the data science field.

Become a data scientist with Coursera

To build a successful career as a data scientist, you will need a diverse skill set that includes technical skills, such as programming and data wrangling, and interpersonal skills to communicate your findings to others. Data scientists need to continuously develop their skills and stay up-to-date with the latest technologies and trends in the field.

Grow your career as a data scientist with IBM’s Data Science Professional Certificate, one of Coursera's popular programmes. You'll gain job-ready skills and tools like Python, databases, SQL, data visualisation, data analysis, statistical analysis, and machine learning algorithms.