big data skillsMore and more companies have become reliant on data science and analytics. When journalists pay as much heed to data scientists as they do political pundits during election years, it’s clear that the data science industry has become a high-visibility, high demand profession. The relative newness of this emerging career path has made the type of education need to pursue it not as definable as other more traditional markets. Though every company values such skills and tools different from one another, this is a discernible pattern for those who had found success in the data science market. The following list of qualifications is just the tip of the iceberg as what type of skills and training can lead to a lucrative role in this field.

Soft Skills

An inquisitive mind

It’s not enough to go to school with this kind of career goal in mind – you need to have the kind of personality and interests that make you more predisposed to be fascinated by analytical data and the kinds of methods to apply it in a creative and enlightening way.

Nate Silver, for example, isn’t one of the most recognized data scientists in the world because he excelled in a career path he felt would be the most profitable. He’s famous because he has a genuine passion for the way statistical data illuminates our understanding of the world and how people behave and business respond. This type of intellectual curiosity is vital to being able to wade through all those numbers and patterns, and come up with a conclusion businesses and media platforms can act upon.

Domain knowledge

A good amount of business acumen, especially in the type of industry you hope to specialize in, is also a bonus for data scientist candidates. This allows you to be able to discern with challenges that are most urgent and how to best leverage any data generated by analytical models or raw statistical gathering.

Communication and influencing skills

Finally, a good data scientist has influencing and communication skills to impart their findings to management in a way that’s easy to understand, and therefore act upon. Qualified insights communicated in a way that clarifies obstacles and reveals opportunities faster are much preferred to data and statistics that are dense, indecipherable and devoid of relevant context.

Technical Skills

Analytics

Education: Master’s Degree or higher in the following fields: Mathematics and Statistics, Computer Science, or Engineering. Roughly 88% of data scientists have their Master’s Degree in one or more of these fields, while 46% have a Ph.D.

Also, you need to have knowledge and training in data analytics software such as SAS, R or both.

Computer Science

Coding and training in data analytics is essential for having the right qualifications to enter this field. Learning at least some kind of basic coding language will give a data scientist the tools to construct the most effective and optimized analytics models that will help companies make successful, data-driven decisions.

  • Python coding is the most common of these coding language, alongside Java, Pen or C.C++.
  • Training in the right kind of coding platform is also helpful, such as Hadoop, Hive or Pig. Increase familiarity with cloud tools, like Amazon S3, can help data scientist collate and store gathered data effectively as well.
  • SQL Database/Coding and the ability to write and execute complex queries in SQL is are also pre-requisites of any data science candidate.
  • Finally, anyone interested in entering the data science market has to be able to work with and have some experience with unstructured data. These types of non-traditional data sources (social media, video or audio feeds) are more abstract yet can illuminate the scope of the kinds of opportunities and challenges data analytics can reveal.

Informative Resources

Now that you know what skills you need to enter data science market, you can select various options available. Though not many institutions have data science programs, advanced STEM, and computer science programs are a good place to start and gain foundational education towards a career in data science. Also, MOOCs (Massive Open Online Courses) like Coursera, Udacity and CodeAcademy are great direct providers in the kinds of computer science training needed to build data analytical models and software. There are even boot camps geared towards the data science market.

Regarding job opportunities in this field, effective networking is a must. LinkedIn groups are of course also always informative and can help data science professionals interact with each other.

Also, predictive modeling company Kaggle often has data science competitions. These often involve scenarios where actual, real-life business problems are addressed by working with unwieldy, real-world data. Not only are these competitions educational and provide much-needed experience, but many participants and their rankings in this competition often get noticed by employers seeking data science professionals.

Conclusion

The secret is out, and the mad dash is on to leverage data science techniques for competitive advantage. If you’re in the market for a data science job, these are the skills that will help you enter data science market and garner you a job offer.