For many people, the word “scientist” conjures up images of a white-coated, slightly disheveled man working in a lab, maybe bearing a close resemblance to Christopher Lloyd in Back to the Future. The reality is that scientists come in a wide variety of guises. And they work in an even wider range of disciplines, from meteorologists to ecologists to astronomers.
One type of scientist you may see increasingly referred to in our digital age is a data scientist. Just what is a data scientist? Is the term as self-explanatory as it sounds? More importantly, are careers in data worth considering? Does it offer good prospects, both in terms of monetary rewards and career progression? Read on to find out.
Want More Career-focused News? Subscribe to Build Your Career Newsletter Today!
What is a data scientist?
As the name suggests, data scientists work with data. More precisely, they usually work with large sets of data, collecting them and then analyzing them. That data can be both structured and unstructured. You could view data scientists as combining
three distinct disciplines. These are statistics, mathematics, and computer sciences.
Their primary role is working with data from companies. And that role can include analysis of data, processing of data, and using data to create models for forecasting future “events.” This allows organizations to create actionable plans from the results calculated by their data scientists.
People who follow careers in data look for trends and patterns in any datasets they’re studying. They’ll usually have knowledge and experience of the industry they’re working in, such as e-commerce. In many cases, they’ll be presented with challenges faced by the business and be asked to use their knowledge and contextual understanding to discover solutions to those challenges.
Much of the data they work with will be unorganized and unstructured. And they’ll draw data from a wide range of relevant sources that can include social media, emails, website visits, and smart devices. Their initial task is to structure that data so it’s easier for them to analyze and identify if there are any patterns to that data. Let’s look at the most common functions a data scientist may be asked to undertake:
- Extract and collect data from various sources, then sort it into sets that are easy to analyze.
- Solve set challenges and/or problems facing a business using the available data.
- Understand the use of different programming languages in relation to data frames. For example, the use of pandas in relation to Python as outlined in this Databricks article on a pandas dataframe.
- Be able to communicate their findings to management and other relevant stakeholders in an understandable format.
- Have knowledge and experience of different analytical tools and techniques.
- Have the ability to notice and focus on patterns and trends in data sets so as to provide an actionable plan.
- Develop new algorithms and tools in order to identify patterns and to more easily analyze relevant data.
- Be able to use the data provided for predictive analytics that can forecast future trends, performance, events, and so on.
- To use data analytics to recommend changes to various aspects of operations, strategies, and processes.
- Be able to use various programming languages and programs to collect and analyze the data and use machine learning and other aspects. So, you should be easily able to answer questions like, “Is TensorFlow a programming language?”.
- Help develop, innovate, and streamline current modeling standards, data collection techniques, and data reporting and analysis.
Careers in data: 6 reasons why you should consider one
1. Money, money, money
Let’s be honest here; when most people are considering a career, one of the first things they look at is the salaries that are on offer. One thing to remember is that data scientists are often the lynchpin in an organization’s decision-making process. Their analysis and interpretations of data sets can lead to multi-million dollar decisions. This is both in terms of expenditure and revenue.
With such an important but often overlooked role, you expect remuneration to reflect that importance. According to Glassdoor, the average salary for data scientists is an attractive $155,728 per year, ranging from $100,000 to $178,667.
Of course, you would expect to earn less at entry-level, and the average salary at that level is around $68,054 per year.
Another monetary factor to consider is add-ons. These can include bonuses, profit sharing, and, in some cases, commission. These will vary greatly according to your experience, the sector you work in, and the pay structure offered by your employer. Bonuses can range from $3,000 to $20,000, while profit sharing can range from $1,000 to $25,000. So careers in data can prove to be a lucrative choice.
2. Increasing demand
There’s nothing more frustrating than deciding on a career path, undergoing all the necessary training and studying, then finding out there are few vacancies in your chosen field. We live in an increasingly data-driven world, and most organizations now consider data scientists as a fundamental role in growth and successful operations. That means that the demand for data scientists will consistently grow.
In fact, in many parts of the world, companies are finding it hard to find applicants to fill these roles. For example, India has over 97,000 data scientist vacancies that they struggle to fill. And according to the US Bureau of Labor Statistics, the number of roles needing data science skills is forecast to grow by 27.9% by 2026. Job security can be a crucial factor when choosing a career path, and it certainly looks like data scientists have that security in droves.
Increasing demand for careers in data also means fantastic opportunities for career progression. This means that even when you join a business or other organization, you will quickly see an increase in both salary and seniority, even if it takes moving to another vacancy to achieve that promotion.
3. Educational opportunities
As well as potential salaries, people interested in careers in data will wonder how they become one. One thing to note is that data scientists don’t only operate in the business world. They’re also in high demand for governmental posts at every level, including federal, state, and even city. Of course, as with many career paths, salaries tend to be lower in the public sector than in the private sector.
So, how do you become a data scientist? There are several routes if careers in data interest you. More and more educational institutions realize the importance and demand for data scientists. So they’re adding courses to their curricula. There are also certificates in data science that you can do as part of a wider computer science degree.
One thing to remember is that it’s not just about your ability to read and analyze data. You also have to be a good communicator, effective at teamwork, assume leadership roles, and work with other departments/teams within an organization, including marketing, customer service, and operations. As you will be accessing data from all these different departments, you need to have interpersonal skills.
4. Value to the business
Everyone likes to have their role valued and recognized for what they contribute to their employer. While your initial impression of data scientists may be of people working in darkened rooms in front of computer screens, they’re, in fact, an integral and essential part of any organization. The patterns and trends they identify from data analysis can inform a business about crucial decisions affecting how the organization operates.
That importance exists regardless of the sector the data scientist operates in. From government to healthcare, e-commerce to manufacturing, the results from a data scientist’s work allow the management to make informed decisions based on that actionable data. When you consider the costs or revenue that can result from decisions based on those analyses, you can begin to understand how much value companies place on their data scientists.
In fact, you could say that data scientists are crucial to a company’s decision-making process. Those decisions, and any formulated strategies, depend totally on data scientists correctly interpreting and analyzing the data they have access to. Without them, a large part of any decision-making would be based on guesswork and unprofessional interpretation of data.
5. A constantly evolving sector
Another reason a career in data is a good choice is that it’s constantly changing and evolving. That means you will never grow bored with your job as there will always be changes, new techniques, and new tools to use. While we may think of tangible assets, such as gold or oil, as being the most valuable assets in the world, there’s increasing value put on intangible assets such as data.
Though we may not be able to give data a market value in the same way as oil or gold, it’s invaluable to businesses, governments, and other organizations. From e-commerce companies, such as Amazon, to streaming services, such as Netflix, there’s increasing use of data analytics to predict customers’ buying and taste preferences.
Data analytics is moving fast and is no longer just about numbers. With the ever-increasing use of AI, machine learning, and automation, more data is being harvested than ever before, which will continue to grow.
These are very much two-way streets where data scientists can help improve the performance of these automated tools and systems while the systems themselves can “self-learn” from the data they generate. This can be of particular use in software testing.
6. Make a real difference
For many people, their job is not just about a good salary or job security. People like to feel that they make a difference, whether from their work alone or as part of a team effort. Careers in data offer the ability to make a real difference, no matter what sector you work in. That difference can be achieved whether you’re working in a small e-commerce business or a major tech giant.
The analyses you provide may move a company to make major operational decisions. For example, you may identify that there are a number of processes within your organization that would work better if they were automated rather than manual. That analysis, leading to an actionable decision, could save your company millions of dollars over the coming years.
Your work doesn’t always have to have financial benefits. Your analysis of data could also produce environmental benefits. From creating accurate climate change models to helping organizations reduce CO2 emissions, the work you do could have a profound effect on our environment; positive change can be a major benefit of careers in data.
There are also healthcare opportunities. Your modeling can help develop new treatments and processes.
The takeaway
Careers in data may be one of the best choices someone can make when considering their future career path. However, it’s not a job that everyone can do. You need to have a high degree of mathematical ability and be able to identify patterns and trends when you analyze any data sets provided to you. Having good knowledge of computing is also hugely helpful. This is because much of your work will utilize computers and specialist tools and programs.
If you tick those boxes, then you could be a future data scientist. Whether looking for lucrative salaries, good opportunities for promotion and career progression or having a real desire to make a difference in the world, careers in data offer all the different avenues you can imagine. Your work could improve public transportation or add value and efficiency to a call center system.
Data is an increasingly used resource at every level of our world today. Choosing to be a data scientist offers multiple opportunities to people with the right skills. It may not immediately sound like the most exciting profession, but the reality is that it can offer as much excitement as you need. Careers in data are increasingly in demand and may be the perfect choice for you.
About the Writer
Pohan Lin is the Senior Web Marketing and Localizations Manager at Databricks, a global Data and AI provider connecting the features of data warehouses and data lakes to create lakehouse architecture. With over 18 years of experience in web marketing, online SaaS business, and e-commerce growth. Pohan is passionate about innovation and is dedicated to communicating the significant impact data has in marketing, through themes like Elasticsearch topics by DataBricks.
