data scientist skills

Trying to Get Better At Data Science? Here’s How to Fast Track Your Career

5 Steps to Growing Your Data Science Skills Quickly

Data Science is one of the hottest jobs in the tech industry and a very lucrative career option for those who want to step into the world of Big Data. A data scientist is expected to be a ‘jack of all trades,’ be constantly learning, and evolving in their craft. Organizations are eagerly scouting for talented recruits and jobs are expected to grow by up to 16% from 2018 to 2028 according to the Bureau of Labor Statistics (BLS).

Data scientists use numbers and information to make real-world decisions and translate business requirements.  It’s clear there is a huge demand for professionals but the problem is finding them. If you’re trying to improve your data scientist skills and stand out from the competition, here is your roadmap to getting better fast.

Trivia: Did you know that the median salary of a data scientist in the U.S. is $122,840? Some of the highest earning professionals make more than $200,000 a year depending on their skills and expertise! 

Step 1 – Take an Online Data Science Course

The hardest part about transitioning from an aspiring data scientist to a full-blown six-figure professional is mastering the skills required to get there. If you’re trying to break into the industry, the best way to get started is familiarizing yourself with the latest industry tools and practices.

There are numerous open-source and free data science courses available online but the best programs are led by industry experts who have been where you are now. You can get a head-start in your Data Science career by signing up for a 11 Months PG Program with Great Learning.

Step 2 – Read, Read, and Read

One of the fastest ways to improve your data scientist skills is to actively read and study academic papers. There are many educational resources online and free ebooks that students can go through to grasp the basics. LinkedIn is a great website where learners can connect with data science experts and stay in touch with the latest industry trends.  

Here are a few books we recommend for improving data scientist skills and thinking:

  • Think Stats: Allen B. Downey 
  • Naked Statistics: Stripping the Dread from the Data
  • Python Data Science Handbook: Essential Tools for Working with Data, 1st Edition
  • Deep Learning (Adaptive Computation and Machine Learning Series) by Ian Goodfellow, Yoshua Bengio and Aaron Courville

Step 3 – Be An Active Member of the Data Science Community

It’s important to understand that connections matter. When it comes to career growth, learning about key influencers and keeping an eye out for professionals will bring numerous opportunities for networking and referrals. Having a hard time finding the perfect gig?

You might just get that offer by meeting up with a company official at a Data Science event or conference. The Data Science career trajectory is not linear and you’ll find yourself switching from Data Scientist to Data Analyst and other popular machine learning designations the more you grow your skills and acquire different specialisations. Study the top data science blogs and read articles on Medium, GreatLearning, KDNuggets, and similar websites. If you find a concept confusing, studying various blogs will give you a fresh perspective and understanding of the subject matter.

For an added extra challenge, you can write blogs on data science topics and start your own blog. It’s a great way to revisit old concepts and check if you have truly mastered your new data scientist skills. Starting your own blog will show recruiters how serious you are about your career and demonstrate the initiatives you take for your personal learning.

Don’t neglect to be an active member of the data science community. Show up at data science events in your area and make in-person appearances to connect with peers. It’s just as important to get involved with the community than passively learn from the sidelines if you’re serious about making a mark.

Never stop growing, and sharing is the mantra. Stick to it and it’ll pay off.

Step 4 – Participate in Open Source Projects

Become a part of an open source project that revolves around your hobbies and interests as a data scientist. You’ll find a majority of opportunities lurking on GitHub and one of the best ways to land new jobs is by making contributions to these. If you want to delve deeper into Big Data, explore and experiment, and continue growing your skills, participating in open-source data science projects is the secret sauce to all that.

Newcomers can gain a significant advantage over the rest of the competition by adding open source projects to their portfolio. You can also scrape websites and build your own datasets in free time when you’re not busy contributing to open-source projects.

Step 5 – Master Technical Skills

Technical skills are specialized tools, programming knowledge, and industry techniques used by data scientists to design use-cases for business products and services. Usually, most data scientists improve skills related to data visualizations, statistical analysis, and learn how to apply Machine Learning and Artificial Intelligence models to business requirements. Having a brief understanding of software engineering principles is a must along with being able to correctly collect, organize, and present raw data to non-technical experts. 

The top five tools used by professional data scientists are R, Tableau, Python, SQL, and Hadoop and learners should have a solid grasp of these. Strong knowledge of linear algebra and multivariable calculus is also recommended for those who are serious about improving their data science skills. Understanding the basics of statistical thinking will help aspirants ask the right questions when modeling and designing products for clients. In addition, data scientists require a strong foundation in mathematics, linear regression, and various statistical functions in order to write code and make Machine Learning models do their jobs effectively. Artificial Intelligence and Deep Learning Neural Networks are the forefront of Machine Learning innovations in the industry.

Non-Technical Areas to Master

The following is a list of non-technical skills that every data scientist works on:

  • Strong Communication Skills

Having the ability to communicate complex concepts in simple ways to business professionals and stakeholders is required by data experts. There are non-technical skills involved when advancing one’s career besides improving data science skills. Data scientists carry out various processes such as data wrangling, scraping, cleaning, and mining which are all important phases of the data analysis process. Data visualization is in itself an art and the way findings are presented to the audience is a skill that’s mastered through experience.

  • Business Acumen

Data scientists are looked upon as experts who can use the profound insights from data and algorithmic modeling to arrive at key business decisions. Every industry vertical has its own goals and data science is regarded as the compass for growing in the right direction. A data scientist must possess solid business acumen and be able to understand client requirements from a business perspective.

It’s not just about building and designing applications but making sure they align with the brands’ vision. Every industry has its unique vocabulary and manner of gathering data which a data scientist must study and analyze. There are industry-specific Key Performance Indicators (KPIs) and every data scientist has to study the industry they’re designing solutions for. Once one acquires a grasp of the main metrics, they will be able to make sound recommendations to the management. The job role of a data scientist in an organization is vast since it encompasses identifying and understanding the pain-points of the business, fine-tuning workflows, and helping teams speeding up business decision-making processes. Apart from this, one must know which technology stacks and techniques to use for achieving the desired business objectives.

  • Other Tips

Covering the basics and building a strong foundation is a good way to grow your skills. Most Machine Learning problems require algorithmic thinking and aspiring data scientists should have a handle of statistical concepts before delving further into advanced data science topics. Understanding various business domains and acquiring domain expertise are also pre-requisites when it comes to improving data science skills for aspirants.

Conclusion

It’s no doubt that being a data scientist is a highly rewarding career but professionals have to stay on top of the game to reap the benefits. Solving real-world problems and capturing massive volumes of data on a regular basis that can help extract insights for making business decisions are the hallmark traits of talented data scientists. To improve your data scientist skills, you have to observe industry trends, demonstrate critical problem-solving skills, and acquire an eye for detail.

The good news is that these traits are attainable and with practice, smart work, and dedication – you’ll get there.

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