Over the past few decades, technology has advanced at an increasing rate. The Industry has developed more and more techniques in order to capitalize on this improved computational capacity. The range of disciplines within the technological realm has expanded, leading to the development of a branch of analytics known as data science; a field which comes with its own theories, its own methods and its own jargon
In the 21st century, data science is currently one of the best careers to pursue. So what exactly does it entail? Data scientists gather, process, and conduct statistical analysis on data for businesses. Their role is to recognize problems and provide solutions. If you work in the field of data science, you will be using theories and techniques derived from many scientific fields, including: computer programming, data mining, data engineering, probability models, artificial intelligence, pattern recognition and learning, and statistics. Data science is utilized in many areas of research and technology, from economics to healthcare, from finance to robotics.
What Training Do you Need?
The three main areas of knowledge that a data scientist needs are computer literacy, math and science, and knowledge of a specialized area of business (such as finance or healthcare). More than half of the specialists in this field currently have at least a Master’s degree and around one fourth have PhDs. The most common areas of study are mathematics and statistics, a close second is computer science or engineering.
Technical Skills
As well as college degrees, comprehensive knowledge of a number of analytical tools such as SAS and/or R, Python coding, SQL Database/Coding, and unstructured data is preferred. There are many additional specialized courses you can take. For example, certification in a course such as Tableau Desktop 9 Qualified Associate Certification Training will provide some necessary skills like organizing and simplifying data, generating field types, mapping, statistics, and creating and using parameters.
Non-technical Skills
It’s not all about data crunching. Forging ahead as an analytics professional requires an insatiable intellectual curiosity. In perspective, you always have to be on the lookout for the next discovery. Curiosity should be one of your main motivating forces, which will, in turn, inspire creative ways to explore data. As well as being fully able to answer questions, you need to have the ability to ask the right ones.
Business acumen is another must. Without an in-depth understanding of the industry you’re immersed in, you won’t be able to figure out which problems your company’s trying to solve. This is as critical as being able to discover new ways for the business to leverage its data.
You’ll also need great communication skills to succeed as a data scientist. You must have the ability to clearly convey technical information to a non-technical team, such as the company’s marketing department. In doing so you will empower them to make appropriate decisions, armed with your insights.
Day-to-Day Tasks
Each company will have a slightly different take on the daily responsibilities of an analytics professional. They may include:
- Organizing and structuring research throughout the industry.
- Collating large volumes of data from a range of internal and external sources.
- Utilizing sophisticated data analytics programs, statistical analysis, and predictive modeling.
- Sifting relevant information from masses of data.
- Assessing data from multiple viewpoints to determine trends, opportunities or weaknesses.
- Resolving challenges with data-driven solutions.
- Devising lucrative changes within the company to improve current procedures.
Types of Analytics
Analytics is generally divided into three main categories.
Descriptive Analytics: Within this category, data is used to identify why certain factors led to a specific outcome. Descriptive analytics professionals rely on key performance indicators to highlight what is working and what is not. This is currently the most used form of analytics.
Predictive Analytics: Predictive analysts use data to indicate what may happen in the future by means of numerical scoring, quantitative modeling, and statistical tool predicting potential trends.
Prescriptive Analytics: Analytics professionals within this field, combine the two forms of analytics described above to assess how a company should proceed with its dealings, how to perform effective risk management, and how to minimize problems.
In spite of the financial and intellectual benefits, data science is not a field for everyone. The pathway there is not as simple as holding a couple of relevant degrees. You also need to have a natural bend towards quantitative and data analysis. If this is something you have a passion for, then you could go far.
Libby Stephens has worked as a graduate recruitment consultant for several years. She discusses some of the new career opportunities which are now available and becoming more and more mainstream as technology progresses.