5 Highly advanced Analytics Domains within Data Science Course

With the tremendous growth in the fields of data science and deep learning, we are seeing an exorbitant level of investments coming into the analytics market. Business analyst groups are now aware of analytics platforms that can deliver actionable insights based on pertinent data, enabling them to make smarter decisions in a faster manner. All the talk on data management, actionable insights, and predictive intelligence based decision making hasn’t started suddenly. It took years of hard work by top certified professionals from leading data science courses who understood the need to have smoother data analytics streams to serve businesses better.

If you are in a business analytics course, we recommend you to follow these top domains within the industry. There are 3 reasons to do so:

  • High pay per project
  • The superior adoption rate of analytics compared to traditional markets
  • Projections on revenue generation and market share in the next 2-3 years

Due to a positive outlook toward the data science course, we shortlisted the top analytics fields you could specialize in now. These are:

  • Retail Analytics
  • Recruitment Analytics
  • Augmented Analytics
  • Fraud Analytics
  • Healthcare Analytics

What is Retail Analytics?

As the name suggests, retail analytics pertains to the use of smart data to understand the buyer’s persona, identify the top buyer’s groups, and design marketing and advertising campaigns to engage, interact and personalize with buyers. It has been found that companies that use retail analytics in their marketing channels have 500% greater chances of converting a prospect into a potential buyer, and then go a step ahead to sustain engagement that results in customer loyalty.

What is Recruitment Analytics?

The analytics pertaining to key metrics associated with the hiring and recruiting process is called recruitment analytics. It is a very important component in the People Analytics Cloud and Software industry.

Data has sparked a revolution in the field of recruitment and hiring. A large number of organizations are adopting AI techniques to reach a larger group of employable candidates and retain talent in a stiff competitive space. Recruitment analytics is used by hiring managers, outsourcing channels, and competitors looking to tap into the candidate pool available in the hiring industry. With maturing HR industry, we can see expect to see a more dynamic shift in recruitment analytics based on AI, Predictive analytics, candidate experience, and cloud-based talent management.

If you are seriously pursuing a business analytics course in the HR domain, here are the top destinations for you to make a mark.

  • Remote Interviewing / Candidate experience management, using video collaboration tools such as Zoom, Google Meet, Skype, Cisco Webex, or Ring Central
  • Diversity and inclusion management (DEI)
  • Contractual and contingent recruitment process management
  • Social media branding and communication analytics

In recruitment analytics, you would come across key metrics such as cost per hire, time to fill / vacancy closure rate, offer acceptance rate, quality of hire, and so on. With Business analytics dashboards designed specifically for recruitment processes, you have a solid reason to be part of one of the fastest growing tech domains in the world.

What is Augmented Analytics?

In IT, we need an augmented approach to predict and swiftly act against any critical operational failure. In the recent, we witnessed how an IT issue took down the whole infrastructure at Facebook, causing it losses worth billions of dollars. In order to prevent such incidents, we need augmented analytics.

Many business analysis groups like to use the words augmented and predictive analytics interchangeably. We should avoid it. Predictive intelligence is based on historical data analysis, while augmented analysis is a Machine learning based semi-supervised technique that allows the software to identify critical anomalies and deviations, thereby preparing the system against a type of outage. You can call augmented analysis an extension of human-level supervision applied to machine intelligence capable of learning and understanding on its own.

Fraud analytics is better understood from the context of digital banking, insurance, mortgage, and traditional credit management. We all know that financial transactions are so much dependent on KYC data and customer information that puts banks, and customers at such high risks. Data fraud or theft can occur within the supply chain (financial fraud or credit card defaults) or could be triggered by external agents such as ransomware groups.

Lastly, Healthcare analytics is a universe in its own right. It covers the entire spectrum of patient experience management, AI-based genome study, and telemedicine industry developments. We will cover healthcare analytics in depth separately.


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