Data Science Career Guide

Data science is the process of building, cleaning, and structuring datasets to analyze and extract meaning. Often, this process involves writing algorithms designed to collect and analyze large amounts of data more efficiently.

Data scientists can be employed in a wide range of industries including healthcare, finance, retail, media, and manufacturing. Their work provides organizations with valuable insights that allow them to optimize important processes and improve the quality of products and services.

Because data science is valuable to so many types of organizations, employment in the field has been expanding significantly in recent years, and this trend is expected to continue. The Bureau of Labor Statistics projects that the employment of data scientists will grow 36% by 2031, much faster than the average rate for all occupations.

Additionally, it can be a highly lucrative career path, as the median pay for data scientists in 2021 was over $100,000 per year. This competitive pay and rapid job growth have made data science an increasingly desirable field.

If you’re thinking of pursuing a career in data science, it’s important to consider the different roles available within the field, and ensure that you understand the various educational, experiential, and skill-based requirements associated with each.

Fundamentals of Data Science

The first step toward finding a career in data science is to build a solid understanding of the field’s foundational skills and concepts. While different data science jobs will often require different skills and come with different day-to-day responsibilities, which can vary by industry, several hard and soft skills are essential to all types of data science professionals.

Soft Skills for Data Science

Soft skills can benefit data scientists when they’re conducting research and expressing the results of their analysis. Several non-technical “soft” skills are especially valuable in data science:

Adaptability: The technologies that data scientists must work with are constantly evolving, and their employer’s goals can often change on short notice. Adaptability helps data scientists quickly respond to emerging trends and acclimate themselves to the latest hardware and software programs being used in their industry.
Communication: To convey the value of their work, data scientists must be able to explain the connections between their statistical findings and their employers’ goals in a way that other types of professionals can understand. This often involves describing scientific processes in layperson’s terms and explaining the results of their research in an easily digestible way.
Curiosity: Data scientists are often asked to help their employers maximize the value of collected data. A strong sense of curiosity can encourage them to examine data more carefully and analyze it in new ways, which can help them to identify any previously undiscovered insights.
Storytelling: Storytelling skills help data scientists convey the results of their research and the logic behind their decision-making to their employers more compellingly and memorably.
Teamwork: Data scientists must interact with a variety of people as they work on projects, including managers, coworkers from different departments, and the other members of their own teams. Strong collaboration skills help them work with groups more efficiently and allow them to incorporate valuable feedback from different sources.

While they don’t directly relate to data analysis and research, developing these skills can help you set yourself apart to prospective employers and make yourself a more valuable asset in the workplace.

Technical Skills for Data Science

Finding a career in data science also requires you to have the right technical or ‘hard’ skills for the job. While some jobs in the field may require more specialized technical experience, certain types of hard skills are valuable for every type of data science professional:

Data visualization: Data visualization is the practice of presenting data in a visual format, such as a graph or map, to help people understand it. Data scientists use data visualization skills to create representations that more clearly communicate their findings to their managers and coworkers.
Data wrangling: Data wrangling is the process of reformatting data to make it easier to analyze. This sometimes involves merging multiple data sources into a single data set. This practice allows data scientists to comb through larger amounts of data for insights in less time.
Statistical analysis: Statistical analysis is one of the most fundamentally important parts of data science. Data scientists use statistics for several key aspects of data science, including drawing conclusions from data and applying quantified mathematical models to appropriate variables. Data scientists use statistical analysis skills to identify trends in large datasets more efficiently and interpret the results of their research more accurately.
Programming: Programming is also a fundamental aspect of most data science jobs. Data scientists must be able to code analytical models and algorithms that allow them to solve complex problems and automate key functions. In many cases, they must be proficient in more than one programming language.
Machine learning: Machine learning is a method of data analysis that utilizes automated analytical model building. This enables computers to learn and make predictions based on data. Data scientists use machine learning to identify trends and patterns in large datasets, which can help them gain valuable insights into their organization’s operations.

These technical skills are essential for fulfilling the day-to-day responsibilities of many data science jobs, and can help you establish a strong foundation for continued learning and professional development.

Programming Languages Used in Data Science

Data scientists use coding languages for a wide range of purposes, including wrangling and cleaning data, and designing machine learning algorithms. While different programming languages may be more valuable for certain data science roles, several languages are most commonly used across the field.

Python: For people who are new to coding, popular languages like Python and Java are often a good place to start. While both Python and Java have a wide range of applications, Python is especially valuable for data science purposes. Coders can use Python to draw symbols for data visualization, analyze large amounts of data, and automate machine learning tasks. Python also employs a relatively simple syntax that is similar to English, making it an ideal choice for those who are new to programming, in general.
Java: While it’s not suitable for as many data science applications as Python, Java is one of the most commonly used programming languages in the world. While it’s relatively simple to learn, data scientists can use Java for importing and exporting data, machine- and deep learning, data visualization, and text analytics.
SQL: Structured Query Language (SQL) also uses a simple syntax that integrates easily with Python, making it a hugely valuable tool for managing and analyzing large amounts of structured data. SQL enables coders to analyze and manipulate data within relational databases, as well as set up test environments for data. Because SQL is used in many database management systems, familiarity with this language is essential for many data science careers.
R: R is a more specialized language than Python or SQL, and is particularly useful for data mining and statistical analysis purposes. While it’s considered more difficult to learn than Python, R can enable users to analyze large amounts of data and communicate their findings more effectively. Commonly used for statistical functions, R is a highly valuable language for people in roles that involve data cleansing, data wrangling, and web scraping.
VBA: Visual basic for applications (VBA) is a highly practical language for beginners. VBA was developed by Microsoft and is used in the full suite of Microsoft Office products (Word, PowerPoint, Excel), making it a good stepping stone for people transitioning to data science from business or finance. While it can be highly useful for analyzing and modeling data in programs like Excel, VBA cannot work as a standalone program, making it less versatile than other languages like Python or SQL.

While these languages are extremely useful for data scientists to learn, this list is not exhaustive, and data science is a very diverse field. It’s important to remember that some more specialized data science roles may require proficiency in other languages.

Careers in Data Science

There are many potential career paths that you can take within the data science field. Many of these roles share similarities, but it’s still important to understand the necessary skills and day-to-day responsibilities associated with each. Comparing and contrasting the different aspects of these roles can help you determine the optimal career choice for you.

Data Scientist

While they’re commonly confused with data analysts, data scientists typically have much more diverse responsibilities that can include organizing, interpreting, and presenting data. As a result, they must possess a combination of skills including coding, communication, data mining, machine learning, and statistical analysis.

Data scientists often work with a wide range of tools to help them interpret large datasets more accurately; including programming languages such as R and Python. They can also utilize embedded analytics tools to create visuals that help them present their data in an accessible way.

Data Analyst

Data analysts are tasked with collecting, processing, and analyzing data to help solve specific problems and improve organizational decision-making. Their day-to-day responsibilities typically include converting data into user-friendly forms such as reports and presentations, which may involve analyzing large datasets or simply reformatting data to make it more accessible.

Data analysts often must have experience with one or more relevant programming languages such as R, Python, and SQL. Some valuable skills for data analysts include data transformation, data cleaning, and data visualization. Because the role of a data analyst is generally less demanding than that of a data scientist, this is an ideal position for less-experienced candidates looking to build their qualifications.

Data Engineer

The role of a data engineer primarily involves designing, building, and managing big data infrastructures. Data engineers differ from data scientists and other similar roles in that they mainly work with the systems and hardware that facilitate a company’s data activities, rather than analyzing the data itself. They may also be tasked with designing data warehousing solutions for companies.

Data engineers must have a strong background in software engineering, and be experienced in a wide range of programming languages including SQL, Apache Hive, Apache Pig, R, MATLAB, SAS, SPSS, Python, Java, and Ruby. This role often requires several years of professional experience in a related position, and therefore may not be suitable for less-qualified candidates.

Business Analyst

The role of a business analyst is less technically oriented than that of a data analyst or engineer, but candidates for this role are required to have strong knowledge of business processes. Business analysts often must work with both business and IT professionals to coordinate efforts to accomplish organizational goals.

Business analysts must possess communication, management, and mathematical skills. Basic data modeling and visualization skills are often very valuable for this position, as business analysts may be asked to present the results of data-driven research in a way that all their coworkers can understand. This is an ideal career choice for those with an interest in both business and technology.

Data Architect

Data architects are tasked with developing blueprints for data management systems to integrate, centralize, protect, and maintain data sources. Candidates for this position must have experience with warehouse solutions, systems development, and database architecture. They should be familiar with programming languages like SQL, XML, Hive, Pig, and Spark.

Data architects must be able to work with a variety of tools and programs designed to help streamline the data pipeline, including data integration tools like ELT staging platforms. This position often requires candidates to have several years of experience in a related role such as a data analyst.

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Marketing Analyst

Marketing analysts conduct research designed to help companies make more informed marketing decisions, including when, where, and how to market certain products or services. They use critical thinking, statistical analysis, and mathematical skills to study and interpret large datasets in search of marketing insights.

Candidates for this position should also possess strong communication and reporting skills, as they will frequently need to share their findings with managers and coworkers. Because marketing analysts generally don’t need to have the same technical and experiential qualifications as other data science professionals, this role can be an ideal starting point for less-experienced candidates.

Data Science Education

If you’re thinking of pursuing a data science career, it’s important to make sure your educational history lines up with your desired role. Due to the highly technical nature of data science work, you’ll generally need a formal education to find a job in this field. Many data science jobs require at least a bachelor’s degree in mathematics, statistics, computer science, or a related field. Some employers may even require master's or doctoral degrees for certain higher-level positions.

Degrees in Data Science

While several different educational paths incorporate elements of data science, it’s important to choose a degree program that will properly equip you for your desired career. The degree programs listed below are among the most valuable credentials for data science professionals:

Data science: Data science degree programs combine aspects of mathematics, statistics, and computer science. Data science majors take courses in algebra, calculus, geometry, statistics, and computer programming. Advanced coursework in data science covers topics like database systems, data mining and analytics, data structures and algorithms, data visualization, as well as machine learning.
Data analytics: Data analytics degree programs combine aspects of applied mathematics and statistics, computer engineering, information technology, as well as decision management/organizational science. Students in data analytics programs can also take courses in more specialized areas such as business finance and accounting, strategic marketing, and healthcare data analytics.
Computer science: Computer science degree programs prepare students for a variety of career areas including data science. Computer science majors take courses like basic programming, calculus, computational thinking, data management, statistics, and electronic design. These programs often cover topics like artificial intelligence (AI), machine learning, informational systems, and information management.

Scholarships for Data Science Degrees

While the cost of earning a college degree may deter some people from pursuing a data science career, there are a variety of data science scholarship and grant programs for both graduate and undergraduate students:

Bloomberg Data Science Ph.D. Fellowship: Program offered by Bloomberg that provides support and funding for Ph.D. candidates working in data science. Offers benefits like full tuition coverage, cost-of-living stipends, and mentorship programs.
ACM SIGHPC Computational & Data Science Fellowships: $15,000 per year fellowship intended for master’s and doctoral students who are conducting research with large-scale big data. Provided by the Association for Computing Machinery’s (ACM) Special Interest Group on High Performance Computing (SIGHPC).
Lilly Endowment Scholarship for Data Science: Scholarship offered to Saint Mary’s University graduate students who are pursuing a master’s in data science.
MinneAnalytics data science scholarships: Scholarship program provided by the Minnesota nonprofit group MinneAnalytics. Available to undergraduates who are pursuing a career in analytics.
Milliman Opportunity Scholarship Fund: Created to assist students who have been historically underrepresented in data science and related fields. Available to underrepresented students majoring in data science, data analytics, economics, statistics, mathematics, computer science, and programming.

In addition to the opportunities listed here, there are a wide variety of grants and scholarships available to students in data science and related fields.

Data Science Educational and Training Resources

On top of completing a college degree program, there are several other ways to further expand and develop your data science skills. These educational programs include data science certification courses, bootcamps, and training platforms.

Data Science Certifications

Completing a data science certification program can help you build your skills and stand out to potential employers. There are several different data science certification programs you can choose from:

Data Science Bootcamps

Data science bootcamps are short-term, immersive programs that can provide you with education about important areas of data science, including in-demand programming languages and popular data science technologies. There is a range of online resources where you can enroll in data science-related bootcamps:

Data Science Training Platforms

Even if you don’t feel like committing to a structured online program, there are still several ways to build your data science skills. Data science training platforms allow you to learn data science fundamentals at your own pace in a low-pressure environment. There are several popular data science training platforms you can consider:

Networking Resources for Data Scientists

Networking is hugely beneficial for building your career in any area. Building a strong network of data science professionals can help you identify job opportunities and gain valuable insights about the industry. While there are many different ways to network, working with mentors and attending industry meetups are two of the most effective ways to meet other data science professionals.

Mentors

Finding a data science mentor can provide you with the opportunity to expand your professional network and learn from an experienced peer in real-time. Your mentor may be a manager, coworker, or personal associate who works in the data science industry. Working with a mentor can help you improve your job performance and help you identify new opportunities for career advancement.

Conferences and Meetups

Data science conferences are an excellent way to meet potential mentors, identify job opportunities, or simply learn about the industry. Conferences may be in-person or virtual, and usually feature workshops, speakers, and networking events focused on different aspects of data science. There are several major data science conferences where you can meet people and learn more about the field:

Additional Data Science Resources

Courses, conferences, and mentorship programs are not the only resources for those learning data science. There are a variety of additional resources for data science news, knowledge, and training. Listed below are a few of the valuable resources for pursuing a career in data science.

The Open Source Data Science Masters: Website that offers books, tutorials, and study groups for a variety of data science-related subjects.
Data Science Weekly: Website that contains book recommendations, databases, and blogs focused on data science topics. Also offers a weekly newsletter featuring job postings and news articles about the latest advances in data science.
Simply Statistics: Website offering a large collection of articles about various aspects of data use.

The data industry has grown rapidly as more organizations recognize the value of data collection and analysis. As a result, job opportunities in the data science sector are expanding significantly. To take advantage of these opportunities, it’s critical to equip yourself with the right knowledge and skills.

Entering the career field of data science is a strong move, provided you acquire the right skills, education, networking strategies, and resources. While learning the fundamentals and developing a strong education and professional background may require an investment, the field offers lucrative and stable employment with plenty of opportunities for advancement. If you’d like to follow emerging topics and trends in data science, be sure to consult the resources above, as well as our blog, for updates.