How Will Evolution of Data Science Jobs Look Like in Future?

Data science is an infinitely dynamic field of evolving technologies. A number of aspects of data science are changing constantly and is expected to change even more vibrantly in the next few decades. With the ongoing advancements in this field, professionals are able to predict the clearer and deeper prospects of data science. The current trends that mark the futuristic projection of data science in businesses are – •   The complex data science algorithms are getting subsumed in packages and the orders of magnitude faster to apply using technologies can be done with great quality and less technical or statistical knowledge. •   The companies who have already seen some success in leveraging data science will continue to adopt ML, AI and other technologies in certain ways that would influence their business performance for better. •   Budding data scientists and tech-students are getting exposed to the latest trends of software engineering, statistics, and other related disciplines. It is also necessary for graduating students to stay competitive to survive in the job market with advancing technology. These trends project that with the emergence of newly trained workforce, the data scientists will be able to transfer their much of the work to those who are sufficient for coding, stats, and new age technologies. The new labor force will be able to employ robust packages and technologies in order to build ML models. Although many academic programs are yet not fully caught up with the current requirement of the industry the timeline of advancement shows that in the next 5 years the situation will not be the same. The industry will welcome graduates with low entry barriers who will be capable of leveraging machine learning without any expertise which would extremely cost-effective for the businesses. The evolution of data science jobs over the years will be in two genres – one would involve highly research-oriented work and second would include some business use cases having strong ROI to support heavy investments in algorithms. The first would precipitate a relatively deep understanding and application of machine learning across a variety of use cases. Whereas the second path will more likely hang with the business-management trends. With the mushrooming of data science in the coming decades, the value of the combination of underlying techniques and business problems will be increased in a significant way. Additionally, the technically trained professionals with some basic knowledge of business and management underpinned with great communication skills will prove to be the best asset to bet capital on.