Data science is a unique, constantly evolving career path that is important to highlight as technology develops. A data scientist is an individual who employs a wide range of analytic techniques to draw information from relevant business data. Many businesses use data scientists to develop business plans and strategies to capitalize on the information from the data. This information can include customer data, sales patterns, user interaction, and many more profound and complicated elements. Unsurprisingly, such information is key to the success of many modern organizations, as it can be employed to improve marketing, further target the right demographics, and otherwise discover how they can improve on current best practices.
Data-Driven Towards Success
Data science uses diverse techniques to transform raw data into actionable insights. These techniques include data mining, analytics, data engineering, statistics, machine learning, and technical visualization techniques. A data scientist uses these techniques to significant effect, taking the information and presenting it to their organization so that it can then be used to better the company. A fully trained data scientist's strength lies in being able to take in the data and in presenting their findings to help their company make effective business decisions.
The Significance of Data Science
As we've already noted, data science has a crucial role in modern business success; however, it goes even further than what we've discussed thus far. The information that data scientists can ascertain through data wrangling is extensive, including information that can transform into marketing plans, but it goes beyond sales. For example, adequately reviewed data can identify problems within a given system, including fraud, system failures, and any apparent slowdowns or pain points within a system. Similarly, data may help identify weaknesses that could leave a company vulnerable to cyber attacks.
Efficiency Through the Power of Data
In general, data can help improve the efficiency of a business overall. As already noted, information from data can form better business plans. Still, again this only touches on the depth of improved efficiency that a data scientist can provide to a business. Numerous systems can be thoroughly optimized, including inventory systems, product distribution, and customer service in general. Notably, having this kind of impact on the effectiveness of a business is a large part of the job satisfaction that data scientists enjoy. They feel as if they are having a real effect on the company they work for and know that they are helping to improve the well-being of every person in the industry.
Data Engineering for Endless Industries
While the corporate world is often the primary focus of data science, as it has the most overall consumers and, therefore, the most data to pull from, many other industries can make ample use of the tools employed by data scientists. For example, government agencies need to understand the population they serve, so they regularly collect data on local populations to determine how they can improve their work. Academia is always looking for ways to improve, including identifying new ways to help better the student experience. The healthcare industry takes in a mass of diagnostic information, including the demographics they serve and common conditions they treat, and can build better forms of treatment with the right data sets.
What Language Is Used for Data Science?
Data science can employ several programming languages, though the most common is Python. Therefore, it is typical for data scientists to fully understand Python and its uses in programming, including coding best practices and assorted python-based tools. Such tools include Matplotlib and Seaborn, among other vital elements that data scientists learn to use throughout their careers. This industry standard also means that data scientists must have at least a basic understanding of many other computing techniques.
How Much Math is Involved In Data Science?
Similarly, it should be no surprise that mathematical skills are a must for anyone interested in a career in data. While a lot of the math involved is done automatically, with many computing systems able to crunch the numbers and provide hard data straight to the user, there are a lot of statistics involved in most forms of data science. Therefore, knowing how to take a potentially complex series of numbers and reduce them down to data that can be presented to stakeholders is one of the most important hard skills for a data scientist, including employing similarly complex mathematics.
What Skills Do Data Scientists Need?
Professionals in data-driven positions have a combination of several hard and soft skills. The first technical skill that a data scientist needs is the ability to analyze large chunks of data, taking that information and then sharing it with their leaders and stakeholders. Behind that skill set is the ability to use and potentially build technology designed to better consume and manage data. This may include AI tools, data visualization technology, and programs designed for statistical analysis.
A data scientist must be fully prepared to collect or mine data, build predictive models around that data, mathematically analyze material, and potentially build algorithms or code. This means using the programming languages and mathematics already mentioned, but it can sometimes go deeper into building applications or developing new platforms for their business. In addition to these hard skills, a data scientist is strongly encouraged to possess many soft skills, such as critical thinking, a love for public speaking or presentations, and generally working well with others.
Why Pursue a Career In Data Science?
While there are many reasons to consider a career in data science, the lucrative nature of the industry is arguably the most significant reason. According to Glassdoor, the current average annual salary for a data scientist is around $120,000, making it a great career move for many individuals. When you add how fulfilling the work often is, a career in data science is the correct career path for curious, number-minded individuals from all walks of life; especially those with any history in programming, statistics, or other industries that make any use of more primitive data sets.
Become a Data Scientist
In the modern day, it is surprisingly easy to begin your journey towards a career in data science. Through online organizations such as the University of South Florida, you can participate in a comprehensive data science bootcamp that will allow you to gain all the skills you need to join the industry. You can readily sign up for a USF data science bootcamp here, where you will learn everything you need to begin walking down the career path towards a fulfilling data scientist role.