It’s common to read blog posts about the importance of data-driven research and business practices in today’s society, but they rarely address who provides and analyzes this information. Is the CEO receiving this information from various sources, finding ways to model it, and spotting patterns?
Not quite. Data by itself is useful but inefficient. It needs to be organized, interrogated, and analyzed by a data scientist.
In this guide, we’ll cover the fundamentals of data science, why you should consider becoming a data scientist, and how to get started on this career path. We’ll detail salaries and prospects for a data scientist career and give you an idea of the places you could end up as a successful data science professional.
Table of Contents
What Does a Data Scientist Do?
Data science is an emerging profession in the field of big data and analytics. Data scientists help to develop insights from raw data by performing statistical analysis and visualization.
A data scientist’s responsibility is to collect, analyze, and interpret data sets. They are expected to have extensive knowledge of statistics, machine learning, and programming languages. Data scientists may also do some of the following:
- Data modeling, collecting, exploring, processing, structuring and analyzing data
- Communicating insights to stakeholders
- Creating predictive models that anticipate future outcomes
Why Become a Data Scientist?
Data science is an ever-growing field because the internet is an ever-growing resource for data. Businesses and academic experts are constantly finding new uses for this information and investing massive amounts into utilizing its benefits. So, there’s a high demand for professionals that can work with this data.
Meanwhile, the biggest companies in the business (Facebook, Google, Amazon) are always looking for ways to use their datasets to benefit their sales partners and improve their services. This means that they pay a lot for data science professionals who can help achieve these goals.
With the big data market set to surpass $100bn by 2023, there’s never been a more exciting time to work in data science. Below we’ll cover some of the most exciting reasons to become a data scientist today.
High Demand
Demand for data scientists is always high because the world is becoming more data-driven. We have been living in an information society for quite some time. The advent of social media, smartphones, and increased integration between online platforms means that businesses increasingly turn to data when making their decisions. It’s the best resource available, so how could they ignore it?
The thing is that understanding data is very different from possessing it. This is why businesses offer data science careers with high salaries: the data scientist is the key to not only understanding the information but making its possible applications intelligible.
Great Compensation & Outlook
According to the BLS, the median wage for a data scientist or mathematical scientist is around $98,000 per year. This rises to a median of $165,000 for the top 10% of professionals, so you know that the big companies are willing to pay big money for someone at the top of their game.
While exact figures aren’t available from the BLS regarding the growth prospects of data science professionals, its outlook for computer and information research scientists is 15% growth from 2019-2029. This is way above the national average of 4%. Given that there is some overlap between data science and information science, it’s also worth noting that the BLS gives the median wage for this occupation at around $127,000 per year.
We can gather from these statistics that the salary and job growth outlook for this role is exceptional. The fact that pay scales sharply upwards at the top of the profession also indicates that you have excellent prospects for advancement.
Creativity Flourishes in Data Science
Make no mistake, this is a creative industry. Analyzing and modeling data isn’t a matter of following a set of predetermined processes. You have to ask questions of the information at your disposal if it’s going to give you any answers, which requires a creative mind.
Fun example: remember the scene in Lord of the Rings where they’re stuck outside the gates of Moria? Gandalf asks the gate numerous “questions”, trying to get the answer he desires. Eventually, Frodo realizes that they’re asking the wrong type of question. The instructions are a riddle, and when he asks the right question, the gate “answers”, providing the desired result. This is what a data scientist does every day: trying to find the right questions to get an answer that is useful and actionable.
It can take a long time to find the right questions, but this makes a data scientist career a perfect choice for creative people with inquisitive minds.
Everybody (Should) Want to Be a Data Scientist
Glassdoor listed “data scientist” as the #2 best job in America in 2021. It’s an impressive statistic, but even more impressive when you consider it’s a rare drop in form. “Data Scientist” was named by Glassdoor as the best job in America for 5 years straight from 2016-2020. We can safely say that it’s a positive outlook. It says a lot about how much people in this industry enjoy their jobs.
As we know, the internet isn’t going anywhere, which means that businesses will continue to need someone to make this information work for them. The excellent salaries, strong demand for data science jobs, and high regard for this profession mean this job will continue as one of the best opportunities for people interested in both numbers and creativity.
How to Become a Data Scientist?
So we’ve covered your prospects. It’s exciting stuff. But hold onto your hat. Before you commit to a data science career, you need to know if you can handle the training.
Data science is an intensely technical and demanding discipline. It requires strong skills in math, computer science, statistics, and also workplace skills like the ability to clearly present information.
Education Pathways To Become a Data Scientist
Below we’ll cover the best education pathways to become a data scientist and what you’ll need to have a long-lasting career in the field. These pathways include data science college degrees, data science job training programs, which includes both certificate programs and bootcamps, and self-study data science courses.
Undergraduate Degree
Several undergraduate degrees will equip you for careers in this area. Other than an undergraduate degree in data science, math is foremost among these, but you could also consider:
- Applied mathematics
- Statistics
- Computer science
- Engineering
- Physics or related degrees like meteorology and astrophysics
Any degree program that includes a heavy emphasis on working with data will set you up well for data science. This is why programs in subjects like meteorology and astrophysics are also valued: so much of the work in these disciplines is based on interpreting data and asking the right questions. These undergraduate paths can also give you the chance to specialize if you’re interested in a certain field.
Master’s Degree
Many successful data science professionals have a master’s degree. Data science is the preferred specialization by most employers, but a master’s degree in many math fields will also serve you well.
A master’s degree in a computer science-related discipline combined with an undergraduate degree in math is a strong choice. You may also transition into a master’s degree in data science from an area like management or accounting if you acquire certain foundation qualifications (see below), which can be a great choice.
Certificate Programs in Data Science
Certificate programs are an excellent and affordable way to learn how you work in this industry. They typically last several months and will cover key concepts like:
- How to run data pipelines
- Designing experiments and asking questions
- Building strong recommendation systems
They may also involve working on a project designed to give you direct insight into the life of a professional in this field. Certificate programs are an excellent option if you’re looking to start building your understanding or transitioning from a related discipline as preparation for a degree in data science.
Data Science Bootcamps
Data Science Bootcamps are engineered to help you find a job as a data scientist. They teach you the fundamentals of life as a successful data scientist and can be a great, affordable alternative to a full master’s degree for graduates looking to move to careers in this area.
Bootcamps typically include more 1-to-1 support than certificate programs, making them great resources for anyone with a thirst for knowledge. They may also teach associated skills like programming and software engineering.
Self-Study
Self-taught computer science professionals are becoming more common. With the right resources, you can teach yourself the skills you’ll need to become a successful data scientist.
It’s an affordable way to transition to this exciting career path if you’re already in full-time work and comes without as much time pressure as degrees or other programs. Remember that if you can demonstrate creativity and expertise in an interview, a prospective employer may listen to your words more than the shiny qualifications on your CV.
Skills You Need to Become a Data Scientist
To excel in a data science career, there are technical skills that data scientists must master. Proficiency in programming languages like Python, R, and SQL is essential for data manipulation and analysis. A deep understanding of machine learning algorithms and techniques is also required, as well as the ability to effectively use data visualization tools such as Tableau and Power BI.
Programming. Data scientists are expected to be fluent in at least one programming language. The language you need to know depends on what each employer prefers. However, general-purpose languages like Python and R are applicable to data science. There are add-on packages you can use to gain statistical analysis and machine learning skills, which are essential for this career.
Soft skills, although sometimes overlooked, play a significant role in a data scientist’s success. Critical thinking and problem-solving abilities are necessary to analyze and interpret complex data sets. Strong communication and presentation skills are vital for conveying insights and recommendations to non-technical stakeholders. Moreover, adaptability and a willingness to learn new technologies are key traits for staying relevant in this fast-paced field.
Creativity. As we’ve stressed, you’re working in a creative industry. You need to practice interrogating datasets and learning how they respond to different questions. This means thinking outside the box and finding ways to square your company’s goals with the dataset provided.
Communication. One thing you should be prepared for as a data scientist is presenting information to people with high expectations and no real understanding of analysis. You’ll have to explain why your queries are relevant, how the findings are actionable, and why you didn’t make certain choices. It can be frustrating, but learning how to present and communicate clearly gets you through roadblocks with your team.
Patience. You’re going to spend a lot of time fiddling around with complex modeling systems and troubleshooting. You need to retain a calm, inquisitive approach and make full use of your computer science expertise to fix issues.
In addition to formal education and skills, earning relevant certifications can further bolster your data science credentials. Examples of such certifications include the IBM Data Science Professional Certificate and the Microsoft Certified: Azure Data Scientist Associate. These programs provide structured learning paths and demonstrate your commitment to professional growth in data science.
Data Scientist Career Growth Path
Curious about what a career trajectory looks like in this field? Here are the typical responsibilities of professionals in this field at different levels.
Entry-Level Positions
At entry-level, your job will be very research-intensive. You’ll be using analysis tools to extract conclusions from raw datasets and write reports of results.
Mid-Level Positions
At the mid-level, you may have the opportunity to design experiments and provide an analysis of the results. You’ll have junior scientists working for you and will learn to direct them in analyzing information using creative inquiries.
Senior-Level Positions
In a senior role, you’ll be in charge of acquiring and analyzing datasets and providing actionable results for your company. You’ll probably have a say in data acquisition, and you’ll determine how your team approaches a project. You’ll be responsible for choosing datasets and asking the right questions.
Gaining Experience and Building a Data Science Portfolio
Acquiring practical experience is crucial for breaking into the data science field. Internships and entry-level positions provide valuable opportunities for hands-on learning and skill development. Job boards, company websites, and networking events are excellent resources for finding relevant opportunities in data science.
Personal projects and Kaggle competitions offer another avenue for honing your skills and showcasing your abilities. By working on diverse, real-world problems, you can deepen your understanding of data science concepts and techniques, while also demonstrating your expertise to potential employers.
Creating a strong data science portfolio is essential for standing out in the job market. A well-crafted portfolio should include a variety of projects, code samples, and visualizations that demonstrate your proficiency in various data science tools and methodologies. When presenting your portfolio to potential employers, ensure that it is organized, visually appealing, and effectively communicates the impact of your work.
Networking and professional development
Building a professional network is vital for career growth in the data science field. Attending industry conferences and meetups allows you to connect with like-minded professionals and learn from their experiences. Additionally, joining online data science communities and forums can provide access to valuable resources and discussions with experts from around the world.
Continuing education and staying up-to-date with industry trends are essential for staying competitive in the job market. Enroll in online courses and webinars to expand your skillset and knowledge. Regularly read industry publications and blogs to keep abreast of the latest developments and best practices in data science.
Mentorship from experienced data scientists can significantly accelerate your career advancement. To find a mentor, attend networking events, join online communities, and leverage your existing connections. A mentor can offer invaluable guidance, support, and insights that can help you navigate the challenges and opportunities in the data science field.
Job Search Strategies for Aspiring Data Scientists
When searching for data science positions, it is essential to tailor your resume and cover letter to highlight your relevant skills, education, and experience. Customize your application materials for each job opportunity to demonstrate your fit for the specific role and company. This targeted approach can increase your chances of landing an interview.
Preparing for data science interviews is crucial for showcasing your technical skills and knowledge. Familiarize yourself with common interview questions for data scientist positions and practice explaining complex concepts in simple terms. Demonstrating your ability to effectively communicate your expertise during interviews can significantly improve your chances of securing a job offer.
Utilizing job search platforms and resources can streamline your search for data science positions. Online job boards and websites specifically for data science roles can be valuable tools for finding opportunities that align with your skills and interests. Additionally, leveraging your professional network can help you uncover job openings that may not be advertised through traditional channels.
What Companies Hire Data Scientists?
Below are some top companies hiring data scientists.
- Google. Who wouldn’t want to become a data scientist at Google? The world’s most famous algorithm lives off of information. The chance to work on this behemoth is something that anyone in this field would jump at. The salary’s not too shabby, either: median pay for this role is around $169,000 per year.
- Amazon. Amazon has revolutionized retail with its user-information-based approach. Its marketing algorithms are among the best in the world, and as a data scientist working for Amazon, you could continue to change the game with your expertise. This role is also very well-compensated, with entry-level professionals earning about $129,000 per year total, and senior staff earning around $220,000 including bonuses.
- JPMorgan Chase. FinTech is a booming industry and has a massive need for people who can work with financial data and draw actionable conclusions. Working as a data science professional for JPMorgan Chase is a lucrative career. Your salary prospects begin at around $111,000 and scale upwards to $185,000 for senior positions.
Interview Questions – Examples
- Tell me about your experience in your previous role as a data scientist
- When you’re analyzing data, what are some of the most useful tools that you use?
- What would you do to improve the quality of the data you analyze?
- What is the difference between exploratory and confirmatory research?
- Does it matter if a dataset is labeled or unlabelled when conducting analysis?
- How do you define machine learning?
- What are some things you have done in the past to work around or fix data problems?
- What do you think is the most innovative data science idea that you’ve come across?
For more tips on how to ace this interview, check out our data scientist interview question guide.
Conclusion
The path to success in this field isn’t always easy. You have to do a lot of studying, for one thing. However, this career offers plenty of opportunities for those that love asking questions, thinking creatively, and chasing knowledge.
Once, you’ve completed your studies, the prospects for advancement are huge. Ask any data scientist, and they’ll tell you that the job is incredibly rewarding. And in an age where so many people feel like they’re ruled by data, it’s so much fun to turn the tables!
