How to become a data scientist in 6 steps
Whether you want to track the spread of a virus, determine how a rescission could affect construction, or identify potential hot spots for political unrest, data is key. Learn how to become a data scientist with edX.
What is a data scientist?
According to the U.S. Bureau of Labor Statistics, a data scientist is a professional who uses their expertise in mathematics, statistics, and computer science to understand patterns, identify trends, and extract insights from large and complex sets of data. They apply various techniques, algorithms, and programming skills to analyze and interpret data in order to solve complex problems, develop predictions, and make data-driven decisions. Common tasks and responsibilities of data scientists include: data collection and cleaning; data exploration and visualization; statistical analysis and modeling; model evaluation and optimization; and communication of results. They strive to make complex findings understandable and actionable.
Data scientist vs. data analyst
Data scientists and data analysts can have many overlapping skills and responsibilities, depending on their employer. However, there are general distinctions between the two roles:
Data scientists are primarily focused on solving complex problems using data-driven approaches. They work on exploratory and predictive analysis, statistical modeling, machine learning, and developing algorithms to extract insights and make predictions, often supporting more research-oriented projects.
Data analysts can be more focused on analyzing structured data to develop insights and support decision-making. They may work with existing data sets; perform descriptive analysis; create reports and visualizations; and help answer specific business questions, often involved in operational and business intelligence tasks. They typically have a broader understanding of various data analysis techniques, but their work may not involve the same level of complexity as data scientists.Their skill set may also be more focused on data manipulation and analysis rather than advanced modeling.
Steps to become a data scientist
Step 1: Pursue an undergraduate data science degree
BLS notes that becoming a data scientist requires a solid foundation in many different branches of mathematics including linear algebra, calculus, and statistics. While you may be able to build your knowledge in individual courses, data scientists typically hold a bachelor’s degree in fields such as mathematics, statistics, business, and engineering. You also have the option of pursuing a focused bachelor's degree in data science, which will cover not only math and statistics, but also data-oriented programming languages, statistical software, database software, and more.
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Step 2: Enhance your data science skills
Earning an undergraduate degree can equip you with a comprehensive set of skills that can prepare you for a data science career. However, if you choose to pursue an undergraduate degree in more general fields such as business or mathematics, you may need to build additional competencies. To pursue a job in data science, it's important to have proficiency in the following technical and transferable skills:
Programming languages: Python, R, SQL
Mathematics: Linear algebra, calculus, statistics, probability
Data analysis and visualization: Tableau, Power BI
Organization and attention to detail
Web scraping: BeautifulSoup, Scrapy
Data mining and warehousing
Machine learning and AI
Big data: Hadoop, Spark
Step 3: Get a data science certification
Data science certifications can be a valuable means of demonstrating your skills and knowledge to potential employers. However, they are optional unless specifically requested. There are many different software developer certifications available, so be certain to pursue certifications in a specialization that aligns with your professional goals.
Certifications include, but are not limited to:
Demonstrates knowledge of:
AWS data lakes and analytics services
Domains of analytics
Google Cloud technologies
Data curation, advanced analytics, and AI and machine learning
Microsoft products, including Azure
Note: The list above is intended to be a helpful starting point for exploration and research. It is not meant to be a comprehensive compilation or to provide definitive criteria for certifications, which can vary by region.
Step 4: Earn a master’s degree in data science
While a master’s degree in data science is not required to enter the field, it can also be a valuable asset. For candidates who studied a different subject in their undergraduate degree program and are hoping to transition into the field, a master’s program can cover topics such as machine learning, data mining, statistical analysis, and data visualization. It can also advance your programming skills in languages such as Python and R. Some employers may specifically seek out candidates with master’s degrees for higher level positions in leadership and management.
Step 5: Excel in data science tools
Data scientists use a variety of tools to complete different tasks. The specific tools that a data scientist needs may vary depending on their area of focus, the industry in which they work, and the data they are using. According to BLS, some of the tools data scientists can use include, but are not limited to:
Web scraping tools help collect data from websites. This data can be used for market research, product development, customer analysis, and more.
Machine learning tools are used to build models that can learn from data and make predictions. These models can be used for fraud detection, spam filtering, product recommendations, and more.
Data visualization tools can create graphs, charts, and other visuals that help data scientists understand and communicate their findings. These visuals can be used to present data to stakeholders, inform decision-making, and identify trends.
Business intelligence tools help collect, store, and analyze data from a variety of sources. This data can be used to track performance, identify problems, and make improvements.
Instant metrics tools can get real-time insights into data so information can be used to make decisions quickly and effectively.
Insights tools can help extract meaning from data to identify trends, solve problems, and make recommendations.
Continuous learning and adapting to new tools and technologies are also crucial for staying up to date in this rapidly evolving field. If you are looking to learn a new tool quickly, explore the different courses offered through edX.
Step 6: Start your data science career
When applying for jobs in the data science field, it's important to approach the process strategically. Start by clearly defining your career objectives, including the type of data science role you are interested in, the industry you prefer, and the skills you want to leverage or develop further. This clarity will help you focus your job search and tailor your applications. You can then start the job search process, completing the following steps:
Polish your résumé: Create a compelling and concise résumé that highlights your relevant skills, experience, and education. Emphasize your data science expertise, including programming languages, statistical analysis, machine learning, and any specific projects or achievements.
Build a strong online presence: Establish an online presence that showcases your data science skills and accomplishments. Consider creating a professional website or a portfolio of projects. Include project descriptions, code samples, visualizations, and any outcomes or impact achieved. Also, ensure your LinkedIn profile is up to date and engage in relevant communities such as data science forums or open-source projects.
Network: Leverage your professional and personal network to explore opportunities in the data science field. Attend industry events, conferences, and meetups to connect with professionals in the field. Networking can lead to job referrals or insider information about job openings.
Research companies and positions: Identify companies that align with your interests and career goals. Research their data science teams, projects, and the technologies they use. Tailor your applications and cover letters to highlight your skills and how they align with the company's needs.
Prepare for interviews: Study and review key data science concepts, algorithms, and methodologies. Be prepared to demonstrate your technical skills through coding exercises, data analysis challenges, and problem-solving tasks. Additionally, be ready to discuss your past projects, your approach to problem-solving, and your ability to communicate complex concepts.
Apply strategically: Customize your applications to each position you apply for. Tailor your résumé, cover letter, and any additional materials to showcase how your skills and experience align with the specific job requirements.
Throughout the application process, remember to stay informed about the latest developments, tools, and techniques used in the field. Engage in ongoing learning, which demonstrates your commitment to professional growth.
You should also remember that the job search process in the data science field may take time, so be persistent, patient, and open to feedback. Continuous learning, building your skills, and expanding your network will increase your chances of finding the right opportunity.
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Frequently asked questions
Data scientists collect, analyze, and interpret data to solve problems and make informed decisions. They use a variety of statistical and machine learning techniques to extract meaning from data, and they communicate their findings to stakeholders in a clear and concise way. Data scientists can be found in different industries. A data scientist in healthcare might analyze patient records to identify trends in treatments, while a data scientist in retail might use data to optimize product placement and pricing.
To become a data scientist, you’ll need a strong foundation in mathematics and statistics, as well as a combination of technical and transferable skills. Technical skills include proficiency in programming languages; data analysis and visualization; web scraping, data mining and warehousing; machine learning and AI, and big data. Transferable skills include analytical thinking, communication, organization, problem-solving, collaboration, self-learning, business acumen, creativity, and ethics.
The amount of time it takes to become a data scientist depends on several factors, including your current level of experience and knowledge; the type of data science role you are looking for; and the amount of time you are willing to devote to studying and practicing data science. Data science jobs typically require a bachelor’s degree in data science or a related field which can take about four years. Employers may require a master’s degree for certain roles.
To become a data scientist without a degree, you need to prove your skills and experience. This can be done by building a strong portfolio, networking with other professionals in the field, and staying up to date on the latest data science trends. Data science boot camps provide intensive, hands-on experience that can help you fill in any gaps and develop the skills and knowledge you need. Additionally, completing a certificate program in data science or analytics can show your commitment to the field and help you stand out.
Becoming a data scientist requires dedication and hard work. You should have a good understanding of mathematics and statistics and be comfortable with learning programming languages and software applications. Additionally, you should be familiar with data visualization tools and be able to discuss your findings in a clear and concise manner.
Data science is a career that allows you to combine analytical, problem-solving, creative, and programming skills. Data scientists work in a variety of industries and use their skills to collect, clean, analyze, and visualize data to solve real-world problems. As a data scientist, you can have the opportunity to work with data from different sources, help organizations make better decisions, and stay current with the latest trends and techniques.
Last updated: July 2023