What is a data science course?

Data science is a vast domain involving artificial intelligence, business acumen, mathematics, machine learning, and statistics. Each of these fields has a contribution to make in extracting meaningful information from the mountains of data available to most organisations – actionable insights that can be used to streamline or even reimagine logistics, marketing, products, health services, education, and many other aspects of business, government, and modern life.

This article explores the nature of data science, aspects of data science courses, and how they help you develop the expertise necessary to succeed in this field.

THIS ARTICLE COVERS:

• What is data science?
• Why study data science? 
• Application of data science
• Careers in data science
• What would you study in data science?
• Study choices for a career in data science
• What do employers want?
• Deciding what you need to master

What is data science?

Data science is one of the fastest-growing fields of study in the world, contributing to the success of many organisations. It is used to sift through masses of data to find otherwise unseen patterns, anomalies, and insights to support decision-making and predict future outcomes. It may also detect patterns in seemingly unstructured or unconnected data.

It involves four main activities: data collection and storage, processing, analysis, and communication. 

Big data, machine learning, and business intelligence are subsets of data science. “Big data” refers to massive data sets, usually relating to human behaviour; “machine learning” involves adapting computer systems through statistical modelling and using algorithms to draw inferences from data patterns. “Business intelligence” may harness both in analysing data to deliver information organisations can act on for improvement, efficiency, and gain.

Why study data science? 

Data science’s ability to leverage multiple datasets to make meaningful predictions and uncover insights organisations can act on for profit has become a driving force in many industries, from finance to healthcare. It has the potential to revolutionise the way we conduct business and manage our data. It holds many opportunities, not least in business intelligence, predictive modelling, and data interpretation. Consequently, the demand for data scientists has rocketed globally.

The proliferation in the volume of real-time data generated by smart devices is helping to drive demand for data scientists. The US Bureau of Labour Statistics predicts that demand for data scientists – one of the fastest-growing employment categories – will grow 36% by 2031. And that’s just in the US. The trend is evident around the globe.

Application of data science

Data science is used in many sectors. Here’s a handful of applications in daily use that – as recently as a decade ago – barely existed in the realm of science fiction, let alone the real world:

• In health sciences: Therapeutic customisation of treatment through genetics and genomics research, tumour detection, drug discovery, virtual medical bots, image analysis, and predictive modelling for diagnosis;
• In search engines: To speed up internet search;
• In transport: To cut accident rates by controlling traffic lights, driverless cars, and rerouting traffic away from dangerous situations;
• In finance: To predict customer lifetime value and stock market moves, understand suspicious activity, and identify unusual transactions; 
• In e-commerce: Providing personalised shopping experiences on e-commerce websites. Examples: Amazon, Flipkart, Walmart, and Netflix, to name a few.

Careers in data science

The field of data science evolves constantly. Data science professionals are in high demand in every sector from healthcare to finance – and it’s a lucrative field if you have the aptitude and analytical skill.

The top jobs in data science are:

• Statistician;
• Data scientist;
• Data engineer;
• Big data analyst;
• HR analytics professional;
• Machine learning engineer;
• Business analytics professional;
• Marketing analytics professional;
• Business intelligence professional.

Top data science employers include:

• Paytm;
• Oracle;
• PayPal;
• JP Morgan;
• Airtel X Labs;
• Mercedes- Benz ;
• Boston Consulting Group.

What would you study in data science?

Your data science course syllabus should cover all aspects of the data science lifecycle to plan, execute and manage a data science project. Each stage of the life cycle incorporates many tasks:

Capture: This stage requires gathering raw structured and unstructured data and involves data acquisition, data entry, and data extraction.

Maintenance: Here raw data is prepared and sorted for processing. This involves data cleansing, staging, architecture and warehousing.

Processing: This involves data mining, classification, modelling, and summarisation in preparation for analysis. 

Analysis: Data scientists examine the patterns, ranges, and bias the data displays to determine its validity and usefulness in predictive analysis. This involves confirmatory, predictive, regression, text mining, and qualitative research. 

Communication: In the final stage of the data science life cycle, analysts set out their findings in easy-to-interpret formats, such as graphs with explanatory notes, in business intelligence reports. This requires – among other things – data visualisation, making it easy for readers to understand the rationale underpinning the recommendations flowing from analysis, and should, in turn, aid decision-making.

Study choices for a career in data science

You could enrol for a degree in data science, a vocational programme, or a variety of short courses. A degree, from a bachelor’s to a master’s in data science, will give you a well-rounded education, imparting management and other skills besides technical ones. 

A vocational programme is likely to provide you with technical skills. 

Short courses will teach you about aspects of data science or particular technologies.

Which data science course to enrol in depends on your career objectives, resources, and the time you have for completion. 

A degree programme can take several years to complete and may be expensive. On the other hand, a data science online course and diploma programme or short course can provide focused training in a specific aspect of data science and take less time to complete, though it might only cover some of what you need in depth. 

Many data scientists find short courses invaluable when they need to explore something outside their usual frame of reference or learn new technology, and they often use them to upskill on a just-in-time basis. However, while short data science courses can be more flexible and cost-effective than accredited qualifications, they require self-discipline and effort.

What do employers want?

Companies that employ data scientists generally care more about your skills and experience than your qualifications. That said, a reputable institution’s degree or diploma carries more weight in some companies. 

It’s important to have hands-on experience and relevant projects to demonstrate your abilities to potential employers, regardless of your qualifications. And most employers require job candidates, as part of the selection process, to complete tests in which they demonstrate their skills.

Deciding what you need to master

Data science is one of the most sought-after skill sets in the job market. It’s a potent tool for finding insights, uncovering trends, and informing decisions – and lucrative to boot. Ultimately, it’s essential to consider your career objectives and resources when choosing your path to becoming a data scientist.
Register at Digital Regenesys for information on its data science course syllabus, entry requirements, and the technical skills you need to be a data scientist, data analyst, or engineer.