Explore the Fundamentals of Data Analytics: Types and Tools

Data Analytics
Data analytics is a fascinating topic these days. Machine learning, data visualization, and storytelling skills are becoming essential in virtually any professional field. The necessity to acquire data fluency is not a thing for data scientists and analysts only. For most people interested in using analytics, learning how to code in a programming language is an intimidating barrier to break and—sadly—the first reason for abandoning their intent.
The term data analytics usually denotes those processes and techniques used to extract value from data. Sometimes, the same term indicates the tools used to make this transformation happen. In any case, data analytics represents how we can transform crude data into something more actionable and valuable. We can recognize three different types of data analytics, each one carrying its own set of peculiarities and possible applications: descriptive, predictive, and prescriptive analytics.

For amateurs, data analytics can be best learned with a more hands-on approach from a course by IBM.

Descriptive analytics

These methodologies describe past data to make it digestible and usable as required by the business need. They answer the generic question "what happened?" by leveraging summary statistics (like average, median, and variance) and simple transformations and aggregations (like indices, counts, and sums), ultimately displaying the results through tables and visuals.

Predictive analytics

Predictive analytics focuses on answering the natural follow-up questions that you have after learning what happened in the past, such as: "why did it happen?" and "what will happen now?". The simplest examples of predictive analytics are diagnostic tools: they enrich the more traditional descriptive reports with a model-based inference of possible causes behind what we see in data. Using basic methods like correlation analysis, control charting, and tests of statistical significance, we can find anomaly detection or business alerts such as a brand share going down. 

Prescriptive analytics

Prescriptive analytics transforms data into a recommended course of action by answering the ultimate question every business manager has: "what should be done?". If descriptive and predictive analytics produces insights and informs us about our business, prescriptive analytics is undoubtedly more assertive and direct. Examples are

• recommendation systems,
• systematic optimization such as automated trading (a rising trend in FinTech), and
• programmatic advertising (the real-time buying of digital media through automatic bids).

Who is involved in data analytics? 
We can recognize four roles concerning data analytics in companies: business users, business analysts, data scientists, and data engineers. 

Data Analytics Tools 

• Spreadsheets: Although their analytics ability is quite limited, spreadsheet applications are omnipresent because of their ease of use and extended portability that facilitates sharing data with colleagues.
• For creating advanced data visualizations and interactive dashboards, tools like Microsoft Power BI, QlikView/Qlik Sense, Tableau, and TIBCO Spotfire let you implement user-friendly data apps to democratize data and make it accessible to the masses.
• Low-code analytics: These tools enable you to build advanced analytics workflows without writing code rapidly. Their "secret" is the workflow-based user interface: non-data-focused knowledge workers (who are looking for ways to automate their time-consuming, regular data reporting.

Knowledge of tools is important, and so is a hands-on approach to data. We at careervira recommend it.