Our continued interactions with professionals working in the big analytics companies have made us realize that there is a need to streamline learning paths and offer more customized career solutions to professionals or students wanting to embark on a career in analytics or Big Data. Today, there’s no ‘one-size-fits-all’ data analyst. There’s data scientists, Big Data analysts or Machine Learning specialists. Designations could even be domain related – such as Retail analysts, or Financial analysts.
The learning story behind each of these designations is different, and usually contains a carefully curated combinations of courses that cater to the current requirements in the industry. In the field of analytics, generalists are giving way to specialists.
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Here are a couple of distinct job roles that have evolved in recent times:
Business Analyst – A business analyst is someone who combines analytics knowledge with strong domain expertise. Business analysts are valuable to businesses because they tend to focus on the role of analytics in the context of a business problem or an opportunity. The main focus for them is domain-specific experience rather than advanced technical knowledge. Technically, the typical business analyst should know some basic statistics, predictive modeling, Excel and a working knowledge of SAS, R or Python.
Data Scientist – A data scientist is someone with an extensive analytics background, who can wield multiple analytics tools like SAS, R, Python, SQL, VBA etc. with confidence. A data scientist is also well versed in advanced analytics techniques like multinomial regression, generalized linear modeling as well as text mining and natural language processing. What is not important though is domain experience, since data scientists will be expected to perform complicated analyses for clients from varying domains.
Machine Learning Specialist – As organizations generate ever more data, machine learning grows increasingly popular. This is because ML techniques can handle huge volumes of data, while generating rapid results. As data overwhelms organizations, ML techniques that can generate insights with little human interference, are proving indispensable. Machine Learning specialists will usually have competencies in neural networks, support vector machines, general boosting & bagging techniques and random forest algorithms among others. An example of a company that hires multiple teams of ML specialists is Facebook, who work on improving several Facebook features, such as the one that recommends the right people to connect with.
Big Data Specialist – With the explosion of information that can be analyzed, businesses have realized that there is a huge amount of data available to them outside of what their internal databases have managed to capture. This represents a gold mine for marketers looking for insights from their customers about the products or the company as a whole. This has led to a spectacular increase in the demand for Big Data specialists – people who can mine unstructured data of the kind found in social media channels, or with telecom companies. It is almost mandatory for Big Data specialists to have advanced skills in technologies like Hadoop, MapReduce, Hive, Pig, Sqoop, Spark etc.
Data Visualizer – The art of communicating analytics results to a non-technical audience in an easy-to-understand manner is a crucial (often underrated) skill for any data scientist. Many organizations now have resource people who specialize in this role. There are whole companies like Gramenar that have built their entire business models on “transforming your data into concise dashboards” and letting “visualizations guide you towards actionable decisions”. They are valued for their ability to generate vivid visualizations, precise dashboards and intelligent, comprehensible reports. A good data visualizer must be a data scientist, but with a heart of an artist. They must have visualization skills in Excel, R, Tableau etc.
All these roles are still new and continually evolving which means that some roles may still merge, or may have new designations that spin out from them. Which is why keeping a constant eye on how the industry scenario changes is critical for any learning development managers, analytics hirers and data science instructors.
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This article first appeared as part of Analytics India Jobs Study 2016: by AIM & Jigsaw Academy