What to Do with a Post Graduate Degree in Data Science
The field of data science continues to blossom into one of the most exciting disciplines in the corporate world. Today’s data scientists can leverage massive datasets to diagnose a business problem, apply algorithms to analyze the data, craft a solution, and finally make a business recommendation backed by the data. More than 95 percent of businesses today face some need to manage unstructured data, and it’s estimated that growing demand for data analytics and business intelligence services may boost revenues north of $200 billion in 2020.
In light of this burgeoning market, graduates, technologists, and IT professionals are all looking to new careers in data science. PG in Data Science can open fantastic new opportunities for learners, helping them make a significant contribution to a company’s business prospects. The following are four paths that a post-graduate degree in data science can offer.
1. Lead Your Company’s Data Monetization Effort
As with almost any business model, data science is most impactful when it has a significant financial impact on the business. Many companies have data analytics teams to help improve the way they manage their supply chains, optimize sales operations, and control costs. But currently, only one in 12 monetizing data to its fullest extent. That means they are spending on data scientists and data science tools, but they are not driving revenue to fund such efforts in the long term.
Monetizing data is the next big frontier for the data science field. A report by the Sloan School of Management at MIT recently highlighted some use cases of how companies are monetizing data science to optimize operations (internal-facing processes) and boost client services (external-facing processes). Areas these companies are focusing on include geo-targeting customers for retail and tourism, fraud detection for financial institutions, smart targeting and click-stream insights for digital advertisers, and Internet of Things (IoT) applications to drive revenue. More specifically, manufacturing company John Deere created a new source of revenue by giving farmers access to data analytical tools such as estimators for crop insurance and forecasts for yield and risk management.
2. Master and Market Insights-as-a-Service
Not every company, however, is fully staffed yet with an internal working environment of data scientists to generate the insights needed to improve processes, reduce costs, and monetize data. Many companies are now outsourcing their data intelligence needs, which is creating a growing segment called “insights as a service.” Currently, 66 percent of enterprises outsource between 11 percent and 75 percent of their business intelligence applications, and Forrester predicts that the insights-as-a-service market will double as insight subscriptions gain traction over time. One insights-as-a-service company created a decision-support model for ship operators to enable shipping fuel savings. And their advanced analytics-enabled mobile app provided financial and performance benefit analysis of ship coating choices to optimize investment decisions. Companies will be looking to insight-as-a-service to augment their internal data science capabilities.
|Data Science gives you broad exposure to key concepts and tools from Python to Machine Learning. Learn more in the PG in Data Science Program.|
3. Get a Seat at the Executive Table
The role of data science in the enterprise is becoming more and more vital to the point that today 57 percent of companies now have a formal title of Chief Data Officer (CDO) on their management teams. And more than 50 percent of those CDOs will report to the CEO this year, up from 40 percent the year before. Data science is the rising star that brings data scientists to the executive table. And 85 percent of respondents in a recent survey say that their firms have started programs to create data-driven cultures. Chief data officers and data scientists are charged with creating and supporting a data-driven culture, distributing data insights across different lines of business and finding new ways to innovate how data is utilized to drive better business processes, products, and services.
4. Position Yourself as the AI Specialist
Artificial intelligence (AI) and machine learning are now synonymous with data science, and they are supercharging the data science field. According to Indeed, the terms “AI” and “machine learning” were included in the job descriptions for about 75 percent of data scientist jobs. The demand for workers with AI skills has more than doubled in the last three years. Data scientists who specialize in AI and machine learning tools will enhance their competitive position and be able to lock down the more senior data science opportunities. Indeed also reports that the top two paying AI jobs were not AI-specific titles (such as AI engineer) but instead in data science: director of analytics and principal scientist. And what programming language is leading the way in AI and machine learning development? It’s Python. Python is the most popular language for AI and machine learning-based on-trend search results on Indeed.
The opportunities for newly minted data scientists are growing fast. And whether you’re looking to run and monetize a data science effort, create third-party insight services, or strive for a more senior data analytics position, there is a unique path to data science mastery just waiting to be followed.
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