Future of Data Science according to Data Specialists


Data is growing and is everywhere. As quoted by Daniel Keys Moran, “You can have data without information, but you cannot have information without data.” Some data we use, some we consume, and some we share. No matter the data is online or offline, it is used to create valuable insights and outcomes that provide value to the user.

This data led to the birth of big data, data science, data analysis, data management, and a lot more data terminologies. ‘Data Science’ was coined in the year 2001 and it gained a lot of popularity in the year 2010. As data was growing at an unprecedented rate, the need to manage and analyze the data was increasing as well. Right from the year 2010 to 2021, the popularity of data science has grown and it is here to stay in the future.

I would like to hereby summarize and decode the key insights from a few data experts who have been in this industry for more than 10+ years. These key insights include the following:

  1. Challenges and path towards data science.

  2. Key skills required to become a data scientist.

  3. Future of data science as a skill.

Excerpts from a senior data science delivery manager at Microsoft, Olga Ivina

Talking about the future of data science, here are some key excerpts from Olga Ivina, a senior data science delivery manager from Microsoft, who thinks that data is increasing day-by-day and it is important to have a focus on responsible and ethical AI as well. She was the one who was fascinated by the working of math and data analysis. She initially worked on predictive models in her first role as a data analyst and later worked on churn prediction in the telecom sector.

She highlights the fact that one needs to be curious, thoughtful, and always wanting to seek answers out of the data. The challenge towards data science is that the field is exhaustive and a lot of practice is required to master the skill. One needs to have a lot of passion and a growth mindset to build a good career in the field. It’s important to self educate, experiment and always try learning from past mistakes. Talking about the future of the field, she highlights the importance of natural language analytics & generation and computer vision. The breakthrough and research in these fields are amazing. The interview by Olga Ivina highlights all these important facts.

Brief insights from none other than the Host of The Ravit Show, Ravit Jain

Ravit Jain, Host of The Ravit Show and community manager at Packt also believes that data is the future and has tremendous growth in the field. He got inclined to the data world by reviewing technology publications by different authors in Packt. Data science is everywhere and it will be so as per his opinion about the field. Through his show, he tries to give back to the community, by sharing different experiences and learnings that help people in the data world grow. The motive of his show is to ask questions and get easy answers from the top experts working already in the data science field.

The top recommendations that Ravit provides to get into the data industry are to start networking understand the niche and talk to as many people as you can. Keep on trying your best to always improve & grow and don’t ever give up. He foresees a bright future in the data industry and the two main people he looks up to are Kate Strachnyi and Thom Ives. Ravit gives great insights about data and it’s importance in his interview.

Juhi Mittal foresees the future of data science as growing

A few other top recommendations for the data science field comes from Juhi Mittal, an ex applied scientist at Amazon who is currently working as a Quantitative Researcher at J.P. Morgan. She has great experience working with companies like Google, and her in-depth knowledge in the field of artificial intelligence. She loved the classes on probability and statistics classes, which made her inclined towards the world of data. She further took multiple courses on artificial intelligence and machine learning and gained good exposure.

One of the best pieces of advice she gives in her interview in AI Time Journal is that patience is the key to becoming a data scientist. She also explains that modelling is only one part of the data scientist job, instead, it’s important to follow proper machine learning pipelines and workflow that help in creating scalable ML models. Juhi highlights the fact on how getting real-time data and information is a major challenge and why the knowledge of data structure is important to order such kind of issues. With her immense experience in the industry, she explains that data science is not just a technology, but it is an art to convince the stakeholders and generate better data outcomes for them.

Three top suggestions by Juhi Mittal, include curiosity, patience, and tons of hard work to secure a career in the field of data science. According to her, the data science ocean is vast and deep and to swim well, one needs to focus on the basics which shall build a lasting foundation that will allow one to reach great heights in the field. When learning data science should remain on more and more hands-on experience, the focus helps build the skills and expertise. Her practical experience and industry knowledge predicts that the future will comprise tons of AI-based applications that will make it faster and better. You can check on Juhi’s recent interview and understand how data is growing.

The Future of Data Science

Hearing from the top minds in this field, it is easily known that data science is here to stay for years. There is an immense amount of development in the field of natural language processing, computer vision, deep learning, and other areas of artificial intelligence that will make use of data in different ways. To build a career in the field, one needs to have a lot of patience (as it’s a long process), curiosity (understand the ‘why’ of everything), and tons of hard work and practice.

The chairman of LinkedIn, Jeff Weiner quotes that “Data really powers everything that we do.” Data is powerful and with the growth in technology, the intensity of data is growing. It is important to understand the power of data and how it can be useful to generate better outcomes.

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