How to become a data scientist? Complete guide
Explore top professional organizations for data scientists, including ADSA, AMS, AMIA, ASA, and INFORMS, offering networking, certifications, and career growth opportunities.
Today, there is an abundance of data, and its volume continues to expand with advancements in technologies like artificial intelligence, machine learning, and the Internet of Things. This surge in big data is driving a rapidly growing global data science platform market, which is now valued at $95 billion.
The field of data science is expanding and is instrumental in transforming businesses worldwide. Data science positions offer lucrative salaries—often exceeding $100,000 annually—and there are numerous job opportunities across the country, including roles at innovative and forward-looking companies. However, these positions demand advanced technical skills and often require specialized knowledge. Whether you are seriously considering a career in big data or just curious about the field, read on to discover how to become a data scientist.
Understanding a data scientist
Rooted in statistics and computer science, data scientists employ various tools to gather, analyze, and store data. Their main role is to simplify complex information and make it understandable.
Data scientists work with a diverse set of software, including application server software, scientific software, presentation software, and development environments. They excel in data visualization, trend tracking, problem prediction, and helping organizations tackle critical issues. Their work often leads to cost savings and innovation, with many tasks being performed almost in real time.
Data analyst vs. data scientist
Although their roles overlap, data scientists and data analysts have distinct responsibilities. Data scientists handle and process extensive data sets using data-centric programming languages and visualization tools.
In contrast, data analysts focus more on examining large data sets to extract actionable insights for decision makers. Their role is primarily centered around analysis, whereas data scientists integrate more complex data manipulation and visualization.
How to pursue a career as a data scientist
Earn a bachelor’s degree
Most data scientist roles require a four-year bachelor’s degree in a relevant field such as IT, computer science, statistics, or mathematics. Some institutions even offer associate degrees in data science.
Pursue a master’s degree
Certain data science positions necessitate a master’s degree, which provides advanced specialization and demonstrates your ability to undertake independent research projects. Earning a master’s degree also indicates your commitment to the field and your capability to complete complex tasks.
Obtain certifications
Various certifications are available to prepare you for specialized roles and to showcase your dedication to a specific career path in data science. These certifications can validate your proficiency with certain software or professional functions.
Join a professional organization
Professional organizations offer valuable educational resources, career opportunities, and networking with other data science professionals. Many of these organizations cater to data scientists across different industries, roles, and regions.
Gain job experience
Given the diversity of data science roles, employers have varying expectations. Relevant work experience can be crucial, and in some cases, can substitute for formal education. It's important to gain experience tailored to your chosen industry to meet specific job requirements.
Data scientist salary and job outlook
As advancements in AI, cloud computing, and machine learning continue to evolve, the outlook for data science is highly promising. Data scientists typically earn six-figure salaries, with a median annual income of $108,020 for data analysts. The field is rapidly expanding, with the U.S. Bureau of Labor Statistics projecting a 35% growth in job opportunities from 2022 to 2032.
Key industries employing data scientists include computer systems design, insurance, corporate management, scientific and technical consulting, and research and development. These sectors are driving demand for skilled professionals who can harness data to drive innovation and efficiency.
Data scientist specializations
Data science professionals specialize in various areas such as consulting, maintenance, deployment, integration, and both professional and managed services. They operate in diverse deployment environments, including both cloud-based and on-premises systems. Their expertise spans numerous business functions, including sales, logistics, marketing, customer support, and accounting.
Data scientists are integral to a wide range of industries. They work in sectors like banking, insurance, telecommunications, retail, media, healthcare, manufacturing, government, defense, energy, logistics, transportation, IT, and e-commerce. This broad applicability highlights the versatility and increasing relevance of data science across different fields and industries.
Certifications for data scientists
Amazon Web Services (AWS) certified data analytics specialty
AWS offers a range of certifications to validate your technical skills and cloud expertise. While foundational-level courses require no prior experience, professional, associate, and specialty-level certifications have varying experience requirements. Certification exams cost between $100 and $300, with no initial experience needed.
Certified Analytics Professional
The Certified Analytics Professional credential, provided by the Institute for Operations Research and the Management Sciences (INFORMS), assesses both technical and soft skills essential for data science roles. Training covers seven domains of analytics. The certification requires two years of analytics experience for those with a master’s degree, four years for those with a bachelor's degree, and eight years for those without a degree. The exam costs $440 for INFORMS members and $640 for nonmembers, with an additional $55 application fee.
Google data analytics certificate
This certificate covers data processing, visualization, and analysis using R programming. Offered through Coursera, the course costs $49 per month after a seven-day free trial and lasts between 11 to 36 hours. No prior experience is necessary.
Google cloud professional data engineer
This certification demonstrates your ability to collect, transform, and publish data. The exam tests knowledge of data processing systems, storage, and analysis, lasting two hours. Registration costs $200, and three years of professional experience is recommended.
CompTIA certifications
The Computing Technology Industry Association (CompTIA) provides certifications ranging from entry-level to expert, validating standards for IT professionals. Their credentials are recognized internationally and cover various aspects of IT.
Data Science Council of America (DASCA)
DASCA sets standards for data science and big data, offering certifications for big data analysts, big data engineers, and data scientists.
International Institute of Business Analysis™ (IIBA®)
The IIBA, with over 30,000 members globally, focuses on business analysis. It provides educational programs, career resources, and certification exam details, supporting professionals in adapting to emerging technologies.
Microsoft certified
Microsoft certifications validate applied skills and career advancement in data science. The Azure Data Scientist Associate certification demonstrates proficiency in training and managing machine learning models through Azure Machine Learning. Courses typically last from one to three and a half hours, with assessments taking about two hours. Proctored exams cost $165.
