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Is Data Science a Good Career Choice?
Data Science

Is Data Science a Good Career Choice?

4 minute read | July 8, 2020
Sakshi Gupta

Written by:
Sakshi Gupta

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Data science is a new buzzword in the tech world, promising high pay and great job growth. But what exactly is data science? Is it a good career choice?

Data scientists study where information comes from, how data fits together to tell meaningful stories, and what those patterns signify for business outcomes.

On a day-to-day basis, this means creating econometric and statistical models for projections, clustering, classification, simulations, and other purposes, predicting user behavior through rigorous data analysis and pattern/trend identification, and conveying insights through data summarization and visualization.

With 650% growth in jobs since 2012, the data science industry is on the rise, leading many people to wonder, “Is data science a good career for me?”

Is data science in demand?

When considering the switch to a new career field, people often want to know if it’s worth it to put in the extra time and effort studying, honing new skills, and preparing for interviews. Fortunately, the answer for many is a resounding yes!

Glassdoor labeled “data science” as the third most desired career in America, with a median data scientist salary of $108,000. The Bureau of Labor Statistics (BLS) lists the median salary for all US workers at $49,800, meaning data science salaries are over double the national average.

In 2019, LinkedIn ranked data science as the top most promising job in the US and reported a 56% increase in job openings.

According to a recent survey, COVID-19 has not slowed data science opportunities either: 50% of analytics and data science organizations have suffered no impacts (42.1%) or have actually grown in size (7.6%) during the pandemic.

The demand for data science links back to more companies ramping up big data, Internet of Things (IoT), and cybersecurity efforts and small-to-medium-sized enterprises also expanding their data analytics capabilities.

Is data science hard?

Data science is definitely intellectually rigorous and can have a steep learning curve. It may be coined by Harvard Business Review as the sexiest job of the 21st century but it’s not all glamorous—there is a lot of time spent cleaning the data, importing large datasets, building databases, and maintaining dashboards. To thrive as a data scientist, you should enjoy quantitative fields and be passionate about helping companies make more data-driven decisions.

According to LinkedIn, SQL is the most commonly requested skill in data science jobs, with Hadoop and Spark also rising in popularity. You will probably need to learn a programming language like R, SAS, or Python; brush up on mathematics with a focus on statistics and probability, linear algebra, and multivariate calculus; and learn some data visualization tools like Tableau. It is recommended to learn coding from scratch, as even a parameter change can disrupt results and there’s little margin for error. As you progress as a data scientist, you may specialize in machine learning algorithms, deep learning, and natural language processing, among other related fields that deal with big data and unstructured data.

Successful data scientists should also be well-rounded with soft skills, like interpersonal skills, communication skills, teamwork, and storytelling. Often, these skills cannot be taught in a textbook and only be developed on the job by collaborating with stakeholders across business, product, and tech teams.

Each step of the data science process can be challenging. First, companies need to acquire the right data from different internal and external sources (e.g. credit card transactions, weather data, order history, competitor intelligence) and make sure it is structured in a legible format. Once the data is queryable, you then have to build complex models and algorithms to extract meaningful insights and convey them in a way that answers key business questions and influences stakeholders.

This article provides a solid overview of the top in-demand data science skills in 2021.

Although data science can require advanced domain knowledge and many job qualifications do mention post-graduate education, you don’t need a master’s degree to break in as an entry-level data scientist. There are many online courses, workshops, bootcamps, and books available, so it is definitely possible to acquire technical skills on your own (link to data science bootcamp article), especially if you come from a software engineering background or hold a bachelor’s degree in another STEM field.

Those interested in data manipulation and computation may also want to explore the work of data analysts, data architects, business intelligence analysts, machine learning engineers, and similar job titles when searching for opportunities.

Get To Know Other Data Science Students

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How many hours do data scientists work?

Most full-time jobs require 40 hours or more of work, but many freelance positions allow you to set your own hours and schedule. If you go over team expectations beforehand, it is definitely possible to maintain work-life balance as a data scientist.

Is it hard to find a job as a data scientist?

The supply of data science postings grew by 31% over the past few years, while data science job searches only rose by 14% over the same period. This high demand has led to a shortage of over 150,000 data science professionals.

Given the supply and demand imbalance and the skyrocketing growth of the field, the data science market is one of the hottest for job seekers, with many companies continuing to hire under uncertain market conditions.

Data science expertise is highly sought-after because it leads to tangible and measurable business outcomes. As stated in Harvard Business Review, “Companies in the top third of their industry in the use of data-driven decision making were, on average, 5% more productive and 6% more profitable than their competitors.”

A data science career path is not for the faint of heart, but sure to satiate the curiosity of those who value continuous learning and a growth mindset.

Companies are no longer just collecting data. They’re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists – the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.

In this article, we’ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you. 

Is Data Science A Good Career?

Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It’s an interdisciplinary field that keeps the work varied and interesting.

10 Data Science Careers To Consider

Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.

Data Scientist

Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research – all depending on the field they operate in.

They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. 

General Requirements

A data scientist’s career starts with a solid mathematical foundation, whether it’s interpreting the results of an A/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. 

Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.

Average Salary

The average salary of a data scientist in the US is $156,363 per year.

Data Analyst

A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.

A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.

General Requirements

To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps to acquire these skills. 

For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.

Average Salary

The average base salary of a data analyst is $76,892 per year.

Business Analyst

Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. 

A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.

General Requirements

Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.

Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.

Average Salary

A business analyst can earn an average of $84,435 per year.

Database Administrator

The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. 

A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.

General Requirements

Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. 

Average Salary

Database administrators earn an average salary of $77,391 annually.

Data Engineer

Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).

General Requirements

Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. 

Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.

Average Salary

Data engineers earn a whooping average salary of $125,180 per year.

Database Architect

A database architect’s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. 

Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.

General Requirements

If you’re aiming to excel as a database architect but don’t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don’t forget programming languages – SQL, Python, or Java. 

If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard.

Average Salary

Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.

Machine Learning Engineer

A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. 

General Requirements

As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.

Average Salary

Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.

Quantitative Analyst

Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. 

They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.

General Requirements

This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. 

Average Salary

A quantitative analyst earns an average of $173,307 per year.

Data Mining Specialist

A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.

General Requirements

If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. 

Average Salary

Data mining specialists earn $109,023 per year.

Data Visualisation Engineer

Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data’s context. 

General Requirements

Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.

Average Salary

The average annual pay of a data visualization engineer is $103,031.

Resources To Find Data Science Jobs

The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.

Job Boards

When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. 

Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.

Online Communities

Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.

Network And LinkedIn

Don’t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.

FAQs about Data Science Careers

We answer your most frequently asked questions.

Do I Need A Degree For Data Science?

A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.

Does Data Science Need Coding?

Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.

Is Data Science A Lot Of Math?

It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.

What Skills Do You Need To Land an Entry-Level Data Science Position?

To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you’re acing problem-solving, critical thinking, and effective communication.

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!

About Sakshi Gupta

Sakshi is a Managing Editor at Springboard. She is a technology enthusiast who loves to read and write about emerging tech. She is a content marketer with experience in the Indian and US markets.