Career In Data Analytics And Emerging Opportunities

Q. What is data analytics?

Simply put, data analytics is the process of deriving and understanding information (insights) from data. Ideally, the derived information is actionable and usable in data-driven business decision-making. For e.g. in retail, a successful analytics project will provide previously unknown information to the retailer and/or suppliers which can be used to make decisions which benefit the customer while driving increased value for the business. 

Types of analytics include Descriptive Analytics (Looking at data to get a picture of what happened and usually presented through dashboards and static reports),  Diagnostic Analytics (Identifying data patterns and explaining why something happened), Predictive Analytics (Forecasting and understanding the future i.e. what will happen), Prescriptive Analytics (Using data to recommend the next best action, what to do about our findings), Optimization Analytics (Using data and sophisticated algorithms to understand what is the best possible outcome).

Q. Emerging careers opportunities in data analytics

Data-driven decisions provide a clear competitive advantage across diverse sectors right from banking to technology, retail, manufacturing, healthcare etc. As more and more data is collected and new big data technologies emerge, there is a growing demand for different kinds of data analytics professionals. Some of the hot careers are:

Analytics Business Consultants, who query the databases, extract insights, test hypotheses and present findings to the business in compelling ways.

Data Engineers, software engineers who design, implement and maintain data architectures, receiving data from disparate sources and ensuring that end users have reliable, efficient access to high-quality data.

Data Scientists, who use statistics, machine learning and programming to aggregate, cleanse data, perform exploratory analytics as well as build predictive solutions and data visualization tools. Their end goal is to solve business problems by turning data into actionable insights.

Data Science Engineers (AKA Machine Learning Engineers), software engineering professionals who work with data scientists to automate solutions such as predictive models. They are fluent enough in software engineering as well as data science to produce efficient products.

Q. What one needs as an aptitude/attitude to make a career in data analytics?

In addition to having a strong background in quantitative fields (Statistics/ Mathematics/Engineering etc.) and programming, a good analyst can quickly translate a business problem into an analytical problem. They are scientifically curious, with strong problem-solving and logical thinking skills. Good communication skills are also vital i.e.an ability to build and tell impactful stories from data, usually disseminating this through charts, models, visualizations.

Q. What are the new ways to analyze data? What about the future technologies to process data faster? 

Declining data storage costs coupled with increasing processing speeds have led to a global explosion of data volumes. There have been huge advancements in big data technologies, and tools and techniques of data analytics. Simple spreadsheet analytics have given way to massively parallel processing to the level of streaming and real-time analytics helping make instantaneous business decisions at the speed of data generation.  Structured data analytics are now complemented with unstructured data analytics such as image and video with relative ease. The algorithmic techniques have advanced to AI techniques such as Deep Learning and Reinforcement Learning, helping drive up massive improvements in learning and prediction accuracies. 

Q. What is the difference between a data analyst and a data scientist? How their careers distinguish and upcoming trends in respective careers?

This is a question subject to much debate because organizations tend to define these career tracks differently. Both jobs involve working with data, deriving insights and telling stories about the data. They both require analytical and curious mindsets. In general, Analysts work on structured databases through SQL queries whereas Data Scientists work on a wider variety of unstructured, structured, semi-structured datasets often accessing them through data lakes. Analysts focus more on exploratory and descriptive analytics while Data Scientists spend more time on predictive analytics. Data Scientists are more academic/research oriented and will have more advanced programming skills across a variety of languages. 

Data Scientists also have deep expertise in advanced statistics with a larger toolkit of algorithms including machine learning. They generally write more complex algorithms and models which can be automated and built into products compared to Analysts who generally produce outputs in form of presentations/charts/visualizations. 

The major trend is an expected convergence and/or increasing overlap in the roles of the two career groups. Both will be expected to work with an increasing variety and volume of data, more real-time/streaming analytics, more sophisticated algorithms and automated systems while at the same time having the domain knowledge so that they can seek out previously unknown information nuggets from the data.

Q. Can we turn a data analyst into a data scientist? How?

Yes, this is possible, and many analysts have already embarked on and successfully completed this transition. It requires a dedicated focus to learn many more skills. Most of the folks starting off as analysts will already have the domain expertise as well as the story-telling skills, so the two main areas to upskill would be Algorithms and Programming languages.

These require extensive training and academic curiosity due to a large number of analytical techniques/algorithms available as well as the variety of programming languages such as R, Python, Java. It also requires a good understanding of big data technologies such as Apache Spark. 

Key must-haves include Supervised machine learning techniques, Unsupervised machine learning, Time series, Natural language processing, Outlier detection, Computer vision, Recommendation engines, Survival analysis, Reinforcement learning and Deep learning as well as data visualization skills.

These can be achieved through the numerous Massive Open Online Courses (MOOCs), classroom training or formal Data Science degree courses now being offered by many universities. It also helps analysts interested in this field to join communities and forums such as Kaggle and Data Science Central where they can hone their skills through hands-on exercises and competitions.