Unlocking the Juice in Data Analytics and Science.

Preamble

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Near and long-term job demand concerns will spark the emergence of new programs in data analytics and science. The use of data to drive key performance and business decisions leads to innovative applicable ideas like deep learning which allows for the accuracy in data prediction and effective decision making at management level. The most important thing here is that statisticians, skilled data scientist and business analyst will be key to business analysis, unlocking to endless possibilities of big data. Data science is a relatively new term but same cannot be said of data science jobs and responsibilities which now cuts across several aspects of company operations and dimensions.

The sky is no longer the limit

a. Increasing Specialization of Roles

At the moment, a large number of companies manage data science and analytics teams with many large established teams, while data science roles are starting to become even more specialized with focus on hiring specialist with concentration on fewer task rather general task all at once. The current downside will further see diminished roles in manual task and possibly loss of jobs that are less automated to the demand of today’s technicalities — efficiency is the buzzword here. The US bureau of Labor Statistics predicted that about 11.5 million job openings will be available in 2026, hence the need to meet the predicted supply, given that there are fewer qualified data analyst and even fewer data scientist by skill-to-demand needs. I do not expect data science to replace biological intelligence but to reinforce the knowledge that will enhance how we think and act.

Many companies used to must that all their data science hires had master’s degree and/or PhD with a deep background in mathematics, statistics and computer science. Now we see lots of professionals with backgrounds in engineering, economics down to physics and chemistry and the juicy actuarial science. Reports by KDnuggets opens up the need to get a more advanced education to develop the depth of knowledge required to function as a data analytics and scientist.

b. Blending of Predictive Analytics & Data Science for businesses

Understanding how businesses function is fundamental to a better application of data analytics and science tools to solutions. Now, almost all fields from government to personnel to security and to start-ups requires experts and this is largely expected to impact the industry in the next 10 years. In the recent past, we have seen people pick up skills in computer coding and transition to data science, giving focus on coding and unstructured data, creating an environment for bigger and larger teams with less expensive need for data science tools. The continuous popularity of data analytics and science title also makes it possible for data analytics and science teams to be a home for data engineers and modelers — providing a corporate payment scale.

The general responsibilities is quite deep and multifaceted with diverse bias towards major areas of applied science and analytics tools, modelling and re-modelling of data components for analytics with the use of analytical software and programming languages like R, Scala, Python and SQL and on the business analytics and visualisation side, software like IBM SPSS, Cognos, Tableau and SaS will be handful. The job of a data scientist extends to running a partial set of the data to establish accuracy — training data analytics, which will then revised and tested again using the full dataset. At that point, the data analytics work begins in earnest. A data scientist builds an analytical model, using predictive modelling tools or other analytics software and programming languages such as Python, Scala, R and SQL.

For instance, the design and application of small molecular drugs in health care and bridges in construction differs empirically. Imagine testing small molecular drugs by using mice, this allows you to train your model with specific identification as to how the drug works without damages to human life, different from bridges which will demand some sort of monitoring of the cost and schedule of the construction. The discovery of America by Columbus is synonymous to the entrants or newness of data science and it is expected that just as America grew, similar is to be seen in the various skills and application that satisfied the title and functions of a data scientist.

A sample of heart signals or electrocardiogram (ECG), the number of beds for patients needing, the number of results from blood test can be captured in cross sectional pattern attention data for predicting the movements in commodities market or a sample of university admission data can be presented in a time series allowing the building up of analytics to get a feedback loop and more insights. We can even predict trends in cafeteria visited by people on Facebook, then we can analyse the underlying influence of the patrons and the profit run of the cafeterias; this represents a paradigm shift where we can move from intuition to data-driven decisions. Peter Drucker says “you cannot manage what you cannot measure”. The most essential element in driving insights out of data remains the human factor which makes the data analyst and scientist a demigod among all. The key to turning data into actionable insights lies in the incapability of machines or tools — which is curiosity to find the right information, empathy that allows you to connect with people, creativity which is the key to invent and solve problems, communication to pass information and leadership that allows to manage teams. These are set of skills that no machine can match, hence the everlasting strength in biological intelligence.

However, the wider challenge is to retain the original structure of the data while making it available for public use void of privacy laws which may pose a constraint to user optimization. The point of pulling dataset together into a database for use is primarily to ask questions — most of which are demand specific. There are a lot of challenges in getting data that is usable — you have to understand the data and the instrument that was used to measure the data and most essentially how to combine the measures from one source or field with another measure source of field — this is a limiting factor to accuracy of data and reliability of results for insights and prediction.

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To make the learning and development data analytics and scientist more exciting — attention must be carefully focused on projects rather than bit parts of task. This will mirror live company cases and thus entail putting together 20 or more dataset for use as exemplified by Tableau server data sources. Python open data sources, quantmod for R, Hadoop packtub and more generic to specific range of sources like World health Organisation (WHO), Mckinsey data house, Airbnb, Glassdoor and Power BI Microsoft data sources.

Written by

A Data Scientist with an eye for simplicity in writing and perfection. The success of any enterprise depends on the key strategies and the ability to stay ahead

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