Is There Room for Data Science in Today’s Schools?
We are witnessing a sizeable swell in the interest and media attention that Big Data is receiving. The recent case of Cambridge Analytica and the way companies are using social media data has hit the headlines and made us more aware of how data is impacting our experience.
We are now producing 2.3 trillion gigabytes of data every day.
So, we can fairly say that the industry is booming.
Enterprises need this data to be captured, processed, and analyzed to derive some actionable insights. This is done by the talented folks called as data analysts and data scientists. If these jobs sound exciting and fascinating to you as a career choice, here’s some good news:
Big Data jobs will grow by 4.4Million by 2024, as predicted by the US Bureau of Labor Statistics.
However, a report by the same organization reveals a massive shortage in the IT workforce by 2020. According to the report, there will be 1.4 million openings in data science but only 400,000 computer science graduates with the necessary skills to fill them.
That skills gap has widened over the last five years.
Making a case for data science in schools
The critical skills required for a successful career as a data scientist, include:
- The ability to clearly define problems and articulate questions that need answering.
- The ability to develop a deep understanding of data sources.
- The ability to create methods and tools to operate on these data and sources.
- The ability to stay relevant to the market by continually upskilling on the latest technologies, data types, and processing methods.
Since these skills are nowhere near our vicinity in the entire educational curriculum, it gets out of bounds for us to think about building a career in data science when the time comes.
Addressing these skills early on and creating a foundation in the formative years can be a game-changer for students with even a slight interest in data science and also for the industry.
A wide range of data science jobs is available in this digital day and age. Organizations are looking to extract benefits and leverage out of their data that is available publicly as well as from the internal analysis.
A few functions of a data scientist include data mining, storage, processing, cleaning, problem-solving, and more.
Big data is here to stay, and that translates into career longevity and the prospect of growing and expanding your career options. Big data and data science efforts span across industries, so it makes the field all the more fascinating.
Teaching introductory statistics at the college level can help enhance the grasp of data science later. The American Statistical Association has made updates in the training suggestions, to include the following:
- Teach statistical thinking
- Integrate real data with a purpose and a context
- Focus on conceptual understanding
- Use technology to explore concepts and analyze data
- Encourage active learning
- Use assessments to improve and evaluate learning
Training the Data Scientists of Tomorrow
Extracting four critical takeaways from a report by The National Academies of Sciences, Engineering, and Medicine:
- Teaching a blend of technical skills – Guy Lebanon, director of AI and ML at Amazon, said students need skills in software engineering, product sense, and machine learning to analyze data effectively. Lebanon added that data scientists could build tools and tests and then employ ML to optimize them. For instance, Southern Connecticut State University rolled out an internship program where students used IBM Watson Analytics to help a local business make better decisions.
- Boost critical thinking with data exploration– Students need to harness critical thinking during the exploration and data analysis process. The basic tenets of the data exploration process include asking questions, refining the questions as per the data, accessing the data, transforming the structure of the data, analyzing if the results will scale, reducing data dimensions, modeling and estimating data, diagnosing if the model fits the data, quantifying uncertainties, and conveying results.
- Data science curriculum must be interdisciplinary – Students in various disciplines might end up working as data scientists. But, often, these students aren’t taught about the technology of data collection. Therefore, data science programs must be interdisciplinary from the very beginning, teaching technical skills such as programming and critical thinking.
- Team projects to foster creativity – The report noted that hands-on projects within teams encourage creativity in the data scientists of the future. Collaborating on data problems allows for more creative thinking and opens up avenues where solutions might emerge. Additionally, using real-world data, students can gain valuable skills and knowledge for the workforce.
Meeting the Skills Gap in Data Science
According to Indeed, a well-known job site, there is an increase of 344% in demand for data scientists since 2013!
The Oceans of Data Institute has created an occupation profile which identifies the key skills needed to be a successful Big-Data-Enabled professional. This profile is created after gathering inputs from over 150 data professionals, including experts from Microsoft, Google, NASA, leading government and business organizations, and universities.
The profile is an excellent resource for educators and policymakers to identify the gap in specific skills and behaviors necessary to be cultivated in students from a young age in classrooms.
Here are a few roles that can be perfected in the students of today with the right teaching methodology:
- BI Specialist
- Business Intelligence Engineer
- Data Scientist
- Data Engineer
- Big Data Engineer
- Data Architect
- Data Visualization Developer
If we have to create the generation of students who think like data scientists, we need to introduce the right courses at the right levels in their education system.
You need to have an eye to be a data scientist.
But, guess what?
You can grow and develop the eye, too!