Webinar Wrap Up: Breaking Into Data Science

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 and 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 keener to employ specialists than generalists. Data scientists should focus on one specific field and become experts in data labeling, machine learning, statistical modeling, parallel computing, etc. 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, and 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 until recently, they were limited to large enterprises and digital natives. 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 there will be around 11.5 million jobs in data science and analytics by 2026.

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 the 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 that the minimum qualification for most entry-level data science positions is a Bachelor’s degree in Data Science. However, 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 Offer 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

Your data science career path can grow throughout 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.

The 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 data analysis and machine learning skills. 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, 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 large sets into AI algorithms and better understand 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 to help companies perform application analysis or data modeling for centralized data warehousing.

3. AI & Machine Learning

These are the most popular AI and ML certifications currently. Professional certifications in predictive analytics, deep learning, or natural language programming (NLP) will greatly help.

4. Enterprise Cloud Platforms

This provides data scientists with far-reaching capabilities.

How Do Data Scientists Find the Right Company to Work For, and What Should They Expect as They Start?

The bottom line is to gradually build your skills and apply for the role, even if you don't have everything checked out. Data analysts and junior data scientists are examples of entry-level jobs. While an MS in data science can be helpful, internship or project experience is a plus for such employment. You can also opt for a Data Science program. 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 learn about the job description. Then, you will better understand how to present your skills. Data scientists can benefit from displaying their experience in the online portfolio when evolving past entry-level jobs. 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 you're not passionate about.

Conclusion

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 and Business Analytics certification courses. With Simplilearn’s Data Science course, you’ll get hands-on practice by implementing various real-life, industry-based projects in healthcare, retail, insurance, and many more.