Before discussing the future of data science and big data, let’s consider a scenario. Imagine you’re the head chef at a reputed restaurant that sources ingredients from all over the world. Say, you’ve got baskets full of apricots from Turkey, Kiwi from New Zealand, almonds from California, and bananas from Brazil. Your job will be to decide how to prepare dishes that combine these different varieties the best. From finding out the nutritional value of these fruits to which flavors work well together and what additional ingredients should be added to make the unified dishes more delicious, you’ll have to derive value from these fruits. At the same time, you’ll need to consider how these dishes would influence the restaurant’s menu and reputation. 

This analogy would help you to understand how big data and data science work. Just like the different fruits, big data is a large volume of data sets – both structured and unstructured. Thus, big data refers to an immense volume and variety of information assets that are generated rapidly. Similar to the chef’s expertise, data science helps you to process such data to get useful insights for informed decision-making and process automation. The future of both these domains is bright though data science has a clear edge. Perhaps that’s why “how can I become a data scientist?” is a frequently asked question and reputed data science bootcamps in Silicon Valley attract participants galore. If you too have a plan to become a data scientist or work with big data, knowing what the future holds for them would help.  

The promising future of big data and data science

From financial services and retailers to media and telecommunications, big data is increasingly used for operational functions, compliance and customer analytics, and fraud prevention. According to IDC, by 2025, it’s expected that 75% of the global population or 6 billion users will be interacting with online data each day. This means in every 18 seconds, each of these connected users will be having one data interaction at the least. As businesses gain the capability to store such huge volumes of data, they’ll need to get them analyzed too to benefit from useful insights. And that’s where data science can help. For instance, if you’re sitting on a pile of data, you can use different types of sophisticated statistical and predictive analysis (think AI or machine learning) to make sense of the structured and unstructured data.  

According to the US Bureau of Labor Statistics, 11.5 million jobs in the field of data science and analytics are expected to open up by 2026. With a dearth of data scientists at present, the gap would widen in the future if adequate experienced professionals aren’t trained. This makes data science bootcamps in Silicon Valley an attractive prospect to jumpstart your career in data science. If you plan to become a data scientist but don’t want to slog through years at college, these bootcamps are your ideal bet. And you should look for specialization options instead of trying to be good at doing a little bit of everything. Thus, you should look for courses or data science bootcamps in Silicon Valley that focus on a particular field of AI, machine learning, etc. 

Final Words

With the rapidly growing demand for specialized data skills, the specific positions within data science are expected to be refined further. As everyone – from startups to multinational companies, starts leveraging data science, it would be interesting to see in which direction this domain grows in the future.

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To learn more about data science, click here and read our another article.