Webinar Wrap Up: Breaking Into Data Science

Companies across the globe have always collected and analyzed data about their customers to provide better service and improve their ROI. The field of Data science involves extracting actionable knowledge from all of the data gathered from multiple sources. In today’s digital world, tremendous amounts of data are being collected continuously. To make this data useful requires innovative data processing methods and modern software. There is a high demand for skilled professionals in this growing industry who can create data-driven business solutions and analytics to help organizations achieve a competitive advantage. 

Anand Narayanan, Chief Product Officer of Simplilearn, and Ronald Van Loon, author, blogger, and an influential voice in the Big Data industry, sat down to record a fireside chat, discussing the current state of the data science job industry, different career path options, and how to find the right entry-level job in data science at the right organization.

We’ve collected some nuggets from this conversation to these valuable insights into the vast field of data science. You can watch the webinar or listen to the podcast using the links below. Or, you can keep reading to learn more about the career prospects in the field of data science

Watch the Data Science Webinar in the following video -

Check out the following podcast to listen to the Data Science Webinar -

What’s the driving force behind the demand for data science professionals, and what does top talent in this area look like according to leading businesses?

There is a huge need for skilled professionals worldwide who can see a business need, then create and deploy a data solution. Companies today prefer candidates with more specialized skills instead of professionals who are a “jack of all trades.” In other words, organizations are more keen to employ specialists than generalists. Data scientists should focus on one specific field and become an expert in the area like data labeling, machine learning, statistical modeling, parallel computing, and so on. The top talent in AI should have skills in AI applications, cloud computing, IoT, or Industrial Robotics. A data scientist is expected to be a combination of communicator, problem solver, mathematician, computer scientist, trend identifier, innovator. They must be able to work in a dynamic and innovative environment.

What does the job market look like for data scientists? Are there specific shortages and areas of need that might contribute to how a data scientist shapes their education and career path?

Data scientists are critical in transforming massive volumes of data into action for companies. They were in high demand in the past too but limited to large enterprises and digital natives until recently. Today almost all companies worldwide are investing in data science skills. A top job seeker site, Indeed, shows a 29 percent increase in demand for data scientists year over year and an increase of 344 percent compared to five years prior.

According to the LinkedIn Workforce Report, as of late 2018, every large U.S. city reported a shortage of data science skills. There is a gap of 151,717 people with data science skills, particularly acute in New York City (34,032 people), the San Francisco Bay Area (31,798 people), and Los Angeles (12,251 people). The U.S. Bureau of Labor Statistics estimates that there will be around 11.5 million jobs in data science and analytics by 2026.

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What do you see as far as data scientist degrees being put to actual use, and which ones are companies looking for most?

No doubt, data scientists need a strong educational background. If we look at the qualifications of currently working data scientists, 88 percent have a Master’s degree, and 46 percent hold a Ph.D. The degrees listed by data scientist job candidates on job site Dice indicate that 27 percent have a Master's degree, 10 percent have a doctorate, and 13 percent have a Bachelor's degree. It should also be noted here that the minimum qualification for most entry-level data science positions is a Bachelor’s degree in Data Science. But companies often look for degrees in other relevant areas like computer programming, computer science, or quantitative social science. Sound knowledge of programming languages is another in-demand skill for data scientists. However, gaining an advanced degree can always help you stand apart from other candidates.

You’ve interacted with a lot of leading companies across different industries. Can you tell us more about the types of data science careers that are offering a lot of room for business innovation and how this impacts career development?

Here are the most sought after data science job roles:

  1. BI Developer
  2. Applications Architect
  3. Database Administrator
  4. Data Architect
  5. Enterprise Architect
  6. Infrastructure Architect
  7. Data Scientist
  8. Data Engineer
  9. Data Analyst
  10. Machine Learning Scientist
  11. Machine Learning Engineer
  12. Statistician

The growth of your data science career path is ongoing and can happen during your career based on your skills, interests, and experience. You’ll continue to develop your advanced analytical skills as you practice and are immersed in real-world business scenarios. Upskilling can help you better understand and learn to identify and work around challenges in data science.

What are companies looking for in a data scientist? What types of skills are most in-demand and helpful?

Special Skills

Analytics, logical thinking, critical thinking, mathematics, project management, neural networks, deep learning, AI, NLP, ML, data engineering, creative problem solving, software programming, and engineering.

Tech Skills

Python, R, SQL, Spark, SAS, Java, Tableau, Hive, Tensorflow, C, C++, Excel, NoSQL, Azure, Linux.

Top 3 most common skills requested in LinkedIn data scientist job postings are Python, R, and SQL, closely followed by Jupyter Notebooks, Unix Shell/Awk, AWS, and Tensorflow. 

Other key skills

  1. Being able to work well and communicate as a team member
  2. Storytelling
    • Data scientists need to communicate data insights in understandable ways
    • They should be able to tell business users and non-data scientists about findings in a way they can comprehend
  3. Problem solving, adaptability, product understanding
  4. Curiosity to drive innovation
  5. Business acumen

How can data scientists make themselves more widely marketable in the industry?

It's essential to focus on the most critical skills and develop demonstrated skills in data analysis and machine learning. Develop sharp communication skills, especially when you are applying for a job. Proficiency in deep learning frameworks is also desirable. It is also highly recommended to focus on a popular programming language, like Python or R, or both. Tableau is also becoming very popular lately.

What specific areas of education are companies mentioning as being most beneficial in actual practice, and what types of training and certifications are attractive to prospective employers?

  1. Big Data 

    Companies are looking for skilled professionals in Big Data, Hadoop and Spark, and data mining experts who can distribute data from the large sets into AI algorithms and better understand the patterns by creating predictive trends and behavior.
  2. BI  & Data Science 

    Professional certifications in BI or data science indicate that you have the right skill sets that are needed to help companies perform application analysis or data modeling for centralized data warehousing.
  3. AI & Machine Learning

    These are the most popular data science certifications currently. Professional certifications in predictive analytics, deep learning, or natural language programming (NLP) will help a lot.
  4. Enterprise Cloud Platforms

    This provides far-reaching capabilities for the data scientist.

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How do data scientists find the right company to work for, and what should they expect as they start?

The bottom line is to build your skills gradually and apply for the role, even if you feel like you don't have everything checked out. Data analyst and junior data scientist are examples of entry-level jobs. While an MS degree in data science can be helpful, internship or project experience is a plus for such jobs. You can also opt for online courses and certifications in data science. Broaden your professional network and contact job recruiters specializing in data science and allied fields.

What can data scientists do to gain more experience and build up their portfolios?

You can check out previous data scientists' job postings and find out about the job description. Then you will have a better idea of how to present your skills. When evolving past entry-level jobs, data scientists can benefit from displaying their experience in the online portfolio. Work on your project and showcase your expertise in a specific area. Don't force yourself to work on a project or build your portfolio in an area that you're not passionate about.

Are you preparing for a career in Data Science? Take up answering this Data Science Practice Test and assess your knowledge.

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Conclusion

As McKinsey predicts, “by 2020; there will be 40,000 exabytes of data collected.” Data Science & Business Intelligence is a field that covers everything related to data cleansing and analysis. Know how to extract insights and gain information from data by looking into Simplilearn’s wide range of Data Science Certification and Business Intelligence certification courses. With Simplilearn’s Data Science course, you’ll get hands-on practice by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, and many more.

About the Author

Eshna VermaEshna Verma

Eshna writes on PMP, PRINCE2, ITIL, ITSM, & Ethical Hacking. She has done her Masters in Journalism and Mass Communication and is a Gold Medalist in the same. A voracious reader, she has penned several articles in leading national newspapers like TOI, HT, and The Telegraph. She loves travelling and photography.

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