How to start thinking like a data scientist
- 1. How to Start Thinking Like a Data Scientist. Debashish Jana
- 2. What Do Data Scientist Do? As chief datahandlers and strategists, they are tasked with transforming volumes of data into actionable insights, enabling the organization to strengthen customer relationships, improve service delivery and drive new opportunities.
- 3. What tasks consume most of Their time? Data preparation tends to be one of my most time-intensive activities. It’s extremely critical, but an onerous task. It also leaves less time to actually analyze the data and deliver new insights to decision makers.
- 4. Complex data types yield large volumes of information to be analyzed. But it’s not the amount of data per se that consumes the most time; it’s getting it in the right format, augmenting it and figuring out what information might be missing. It’s an ongoing process that they have to perform again and again.
- 5. What are some of the challenges they face on a day-to-day basis? One of the biggest challenges as a data scientist is applying the domain expertise to solve a problem. They have a plethora of algorithms and techniques to get value from data, but they need solutions to help them apply those to applications—to connect the dots from the statistics to the business opportunity.
- 6. One of the biggest obstacles to analytical productivity is refining and formatting the data required for high-quality analytics. The lack of a universal or standardized programming language specifically geared to the data science domain doesn’t make it any easier. Even with the best tools today, there is really not a good way around cleaning up the data manually. It’s a continuous cycle of collecting and cleaning data and trying to figure out if it will yield significantly relevant insights
- 7. What skills are the most valuable for data scientists? Data scientists need expertise within multiple disciplines. They need to be good at databases. They need some knowledge of software engineering. They need to know some machine learning. And they need to know some statistics.
- 8. Data scientists are inquisitive. They are continuously exploring, asking questions, doing what-if analyses questioning existing assumptions and processes.
- 10. Data scientists need a good blend of domain knowledge and a blend of business expertise. They need to be extremely inquisitive and relentless at figuring out how to solve a particular problem. That means digging into different approaches and alternatives.
- 11. A skilled data scientist explores and examines data from multiple disparate sources.They simply do not collect and report on data, but also look at it from many angles, determine what it means and then recommend ways to apply the findings
- 12. Data scientists often become the liaison between IT and C-level executives. Therefore, they need to be able to speak both languages and understand the hierarchy of data; they can’t just be the data expert.
- 13. What has been your experience in working across teams to drive an end result? Data science is a sharing-oriented discipline. In their line experience, they have found that if “I have a question”, there’s always someone out there with an answer—whether it’s inside or outside the organization.
- 14. One thing that becomes increasingly important with big data is keeping track of how analytics applications are performing. The data scientists have provided a lot of value by building up metrics that enable them to measure what’s going on in an application when we push a code change.
- 15. Advice from data scientist to companies that are trying to get value from their data? Building out a data scientist role or data science team will foster collaboration among the organization and provide ‘champions of data’ who can derive maximum business value from the organization’s data.
- 16. Embracing the big data challenge
- 17. Why and how are these insights relevant to a manager in India? According to a recent India Jobs study, data science is the fastest- growing field in India. As every company turns into a technology company, organizations in industries such as manufacturing, healthcare and retail are all looking to strengthen their data teams. India is emerging as one of the world’s largest data science capitals, with companies like Mercedes-Benz, Walmart, PayPal and AIG setting up data science centers in the country. Research from the Everest Group shows that India holds between 35 percent and 50 percent of the global analytics services market.
- 18. The number of analytics jobs nearly doubled from April 2016 to April 2017, and there are almost 50,000 analytics and data jobs currently open in the country. Hiring companies are looking for people skilled in tools such as R, Python and Hadoop.