Woman smiling off-camera. To the left, is navy blue and the woman is wearing a backpack

Data science is a hot career at the moment and for good reason. The job outlook is positive, the salary starts great and gets better with experience, and growth opportunities abound. You might be one of many aspiring data scientists and wondering what skills you’ll need to acquire to land that first job.

Look no further because we’ve got you covered.

Does data science require coding?

Like every tech job, coding languages form the basis of data science. If you’re interested in a data science career, coding is the first place you should start.

1. Programming

Proficiency in languages like Python, R, and SQL is essential for data scientists. They should be able to write efficient and clean code to manipulate, analyze, and visualize data.

Python, the world’s first and most popular programming language, is used extensively by data scientists. Python has various libraries that complete essential tasks such as data manipulation and visualization. For example, Pandas, a Python library, offers powerful data manipulation capabilities, including reading and writing data in different formats, transforming data, handling missing values, and merging datasets.

How to learn or improve this skill Since programming is a widely used skill by beginners and experts alike, coding resources can be found anywhere. You can learn via books, websites like GitHub and FullStack, or take an introductory course online. The same goes for improving your programming chops; online communities are an excellent environment for learning from others or finding a mentor that can help you advance.

Is SQL required for data science?

Absolutely. SQL is a vital tool in data science. It becomes even more powerful when integrated into other programming languages such as Python, R, or Java. SQL is used in data extraction, transformation, and analysis. SQL also allows data scientists to interact with databases, including creating and modifying database schemas, designing tables, setting up indexes for performance optimization, and managing access controls and permissions.

2. Statistics and Mathematics

Math and stats may have gotten a bad rap in the past, but data scientists rely on them to do some pretty awesome stuff. Data scientists need a strong foundation in statistics and mathematics to understand the underlying principles of data analysis, hypothesis testing, regression, and machine learning algorithms. Data scientists use math and statistics, as well as computer science principles to improve systems. Uber Eats uses statistical modelling to optimize delivery routes and ensure your food arrives hot. Activision Blizzard, the company behind games like Call of Duty and Candy Crush, has used math to improve their online gaming experience.

How to learn or improve this skill Anyone can learn math and statistics, even if you don’t consider yourself good with numbers. You can start by familiarizing yourself with concepts important to data science, like probability, hypothesis testing, and descriptive statistics. The easiest way to learn would be to take an online course and get involved in online communities that can help you improve your skills.

3. Machine Learning

Machine learning is, in a good way, taking over the world. You've encountered machine learning if you interact with Spotify, Netflix, or even food delivery apps. Familiarity with machine learning algorithms, such as linear regression, decision trees, random forests, and neural networks, is crucial for data scientists to build predictive models and extract insights from data.

How to learn or improve this skill This skill is more advanced than others, but like anything, it’s good to start with the basics. Learning to code with languages like Python will set you up for success. Next, you’ll need to know the theory behind machine learning, like data wrangling and processing and presenting models. The best way to up your skills is to work on open-source projects to get the hang of it.

4. Data Manipulation and Analysis

Data scientists should be skilled in data wrangling, cleaning, and preprocessing techniques. They should be able to work with large datasets, perform exploratory data analysis, and extract relevant features for modelling.

How to learn or improve this skill Practise, practise, practise. Acing data manipulation takes time and effort. Luckily, there are many online communities that can help you out, like Scikit-learn, TensorFlow, KNIME, and BigML.

5. Data Visualization

Presenting data in an understandable way is the peanut butter to data manipulation's jelly. Remember, you are the data expert, but those you’re presenting your findings to likely aren’t. Data scientists should be proficient in data visualization libraries and tools like Matplotlib, Seaborn, Tableau, or Power BI. They should be able to create clear and compelling visualizations to communicate insights effectively.

How to learn or improve this skill Just like the previous skill, the best thing you can do is practice using the libraries mentioned above. Joining an online community like Kaggle, StackOverflow, and Driven Data can challenge you to improve your data science skills and connect you with the right people to help you grow.

What soft skill is needed by data science?

Data scientists may be absolute wizards when it comes to statistics, probability, and numeration. Still, those skills must be accompanied by the right soft skills to be practically helpful. In completing a data science bootcamp, you’ll pick up the required soft skills like:

  • Effective communication with non-technical audiences.
  • Collaboration with other data scientists and outside departments.
  • Analytical problem-solving abilities.

6. Analytical Thinking

Data scientists need to look problems in the eye unafraid. You’ll need strong analytical skills to break down complex problems, identify patterns, and extract meaningful insights from data. Thinking critically and applying logical reasoning to solve data-related challenges is key.

How to learn or improve this skill It may seem less evident when it comes to learning soft skills. You can develop your analytical thinking skills by practising hard skills like data analysis and manipulation. The more you work with tools such as Python, Pandas, and Tableau, the easier it will be to think analytically.

7. Communication Skills

Effective communication is essential for data scientists to convey their findings and insights to technical and non-technical stakeholders. You should be able to present complex information clearly and concisely.

How to learn or improve this skill Effective communication is an ability that is built up over time and with a lot of practice, so you don’t have to be too worried if you feel unprepared in this area. You can be a “second” on a presentation and let your colleague or mentor take the lead. There are also countless workshops, books, and YouTube videos that can help you.

8. Problem-Solving

From formulating hypotheses, designing experiments, and applying suitable algorithms to tackle real-world problems, data scientists are undercover creatives when it comes to finding innovative solutions.

How to learn or improve this skill The more you work through various bugs and technical hurdles, the more natural a problem-solver you’ll become. Trying multiple ways to solve the same problem is a good idea. That way, when you face a similar issue in your data science job, you’ll know more than one way around it. You can also join online events and challenges like hackathons which purposely put you through challenges to improve your abilities.

9. Domain Knowledge

Having domain knowledge in the industry or field of application is advantageous for data scientists. Understanding the context and specific challenges of the domain helps in framing relevant questions, selecting appropriate features, and interpreting results effectively.

How to learn or improve this skill As you complete data science training and start your career, you’ll gain familiarity with various domains used in the field. It’s also a good idea to keep up with updates and improvements in the tech sphere. You can do this by subscribing to domains’ newsletters and other helpful tech resources like Stack Overflow, The Sequence, Data Elixir, and more.

10. Collaboration and Teamwork

As a data scientist, you’ll work with fellow data pros and across multi-disciplinary teams. It’s best to approach your work with a flexible attitude and ready to contribute their expertise to achieve team goals.

How to learn or improve this skill This may be obvious, but teamwork will teach you how to work collaboratively. Besides this, you should go into your job with an open mind, unafraid of constructive criticism, and be aware of the needs of the people around you.

Ready to launch your career as a data scientist with the team that achieved an 86% employment raite for our Data Science bootcamp graduates? Lighthouse Labs offers a comprehensive Data Science Program covering the necessary hard and soft skills to help you succeed in the long term. Choose between the full-time intensive 12-week Bootcamp or the part-time 30-week Flex option. Both programs follow the same curriculum and are equally challenging, differing only in their schedules. Whichever you choose, you'll be ready to tackle your first day on the job.