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.
Looking forward to a career in Data Science? Check out the Data Science certification now.
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 -
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 keener 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.
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.
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.
Here are the most sought after data science job roles:
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.
Analytics, logical thinking, critical thinking, mathematics, project management, neural networks, deep learning, AI, NLP, ML, data engineering, creative problem solving, software programming, and engineering.
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.
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.
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.
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.
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 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 the domains of healthcare, retail, insurance, and many more.
Name | Date | Place | |
---|---|---|---|
Data Science with R Programming | 6 Feb -7 Mar 2021, Weekend batch | Your City | View Details |
Data Science with R Programming | 12 Feb -13 Mar 2021, Weekdays batch | New York City | View Details |
Data Science with R Programming | 15 Feb -3 Mar 2021, Weekdays batch | Atlanta | View Details |
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.
Data Science with R Programming
*Lifetime access to high-quality, self-paced e-learning content.
Explore CategoryData Science Career Guide: A comprehensive playbook to becoming a Data Scientist
Top Data Science Books for an Aspiring Data Scientist
How to Become a Data Scientist?
Data Science Interview Guide
A Day in the Life of a Data Scientist
How to Build a Career in Data Science?