You’ve probably heard about data science and its soaring popularity in recent years. Becoming a data scientist presents you with unique opportunities not available in many other career paths.
Data scientists use data from various sources to build complex algorithms and models that solve problems and answer critical questions for major organizations. If you’re curious, result-oriented, and astute at decision-making, you have what it takes to become a data scientist.
Still, you may wonder: What does it take to become a data scientist? What if you’ve never stepped foot into a computer science class? Can you still become a data scientist? Whether you’ve always known you want to work with data or are just starting to come around to the idea, we’ve outlined the critical components you can put in place to get your data science career off to a stellar start. Read on for a comprehensive overview of the steps necessary to obtain an education and land a great job in data science.
What Does a Data Scientist Do?
In the past 15 years, the demand for data scientists has grown across the United States and is projected to continue. The U.S. Bureau of Labor Statistics (BLS) projects that the employment of data scientists is expected to grow by a massive 35% between 2022 and 2032, which is faster than average for all occupations.
With the high demand and prestige associated with tech jobs, you may wonder what role data scientists play in the workplace.
Data scientists extract, interpret, and analyze massive amounts of information an organization generates across multiple sources. They use data mining, statistical tools, artificial intelligence, and machine learning to develop models to help an organization forecast or plan for future events.
Additionally, data scientists can perform other roles in the workplace, including:
- Conducting research, finding challenges, identifying patterns and trends in data, and developing ways to resolve them.
- Building algorithms and tools that collect, merge, test, and extract data to help management, teams, and customers make better decisions.
- Assessing the effectiveness of data collection techniques and sources and developing ways to improve them.
- Creating reports and datasets that clearly show how customers, other businesses, and clients interact with your organization.
- Staying updated on technological news, trends, techniques, and technologies.
- Communicate coherently with teams and management and enhance collaboration between the tech department and others.
What are Some of the Most Popular Specialties in Data Science?
Data science is an expansive profession, and data scientists can fit multiple roles across different industries. There are plenty of different paths and specialties you can encounter. Here are three popular ones:
Data Scientists
The traditional role of a certified data scientist is to identify and assess questions that need answers in an organization, find relevant data, and model potential solutions. Besides requiring stellar technical and analytical skills, data scientists must possess strong business acumen and communication skills.
Data scientists use their expertise to mine, collect, and clean unstructured data. They also analyze and synthesize results using complex algorithms, machine learning, AI, and other tools. Additionally, they generate reports and communicate findings to key stakeholders, who use that information for high-level decision-making.
Data Engineers
Unlike data scientists, data engineers focus solely on how to manage massive amounts of fast-changing data. They develop, manage, optimize, and deploy infrastructure that transmits and transforms data to data scientists for further querying and analysis.
Data Analysts
Data analysts are the bridge between business analysts and certified data scientists. Once analysts have questions that need answers, they analyze and organize data generated by data scientists for answers key stakeholders need for better high-level business strategies. They also translate the data derived from technical analysis into actionable items and communicate them to different stakeholders coherently.
Is Becoming a Data Scientist Right for Me?
Data Science can be a rewarding and fulfilling career path if you are a creative individual with high technical expertise, critical thinking, and problem-solving skills. If you have these characteristics and love using data models, AI, and machine learning, data science may be the right path.
Here are five indicators you can use to gauge your fit for a career in data science:
- You have strong programming skills in R, Python, SQL, and any other data-oriented language
- You enjoy working with numbers, tables, statistics, and probability to make sense of datasets.
- You enjoy solving unstructured problems and developing new models that lead to solutions.
- You love using data to drive decision-making in organizations and businesses.
- You enjoy using machine learning and AI tools to solve complex data mining and analysis problems.
How to Become a Data Scientist: Step-by-Step
There are plenty of ways to become a professional in data science, but here is a general outline of how to become a certified data scientist.
Step 1: Earn a Bachelor’s Degree in Data Science
To have a successful career as a data scientist, you’ll need to start with a bachelor’s degree in data science, statistics, or computer science, either online or through a traditional program. Although you can still become a certified data scientist without an undergraduate education, employers favor academic credentials when choosing suitable job candidates. A bachelor’s degree can be earned in four years of full-time study, but it’s not uncommon to take five or six years to complete a degree program. Some employers may require a master’s or doctorate for career and salary advancement.
A data science degree provides you with an ideal platform to develop your knowledge and skills in:
- Data Analytics
- Programming
- Data visualization
- Database management
- Statistics and probability
Step 2: Sharpen Your Relevant Skills
To become a competitive data scientist, you’ll develop your existing skills and learn new ones. If you already possess a strong background in programming, data analytics, or a related specialty, you may be able to become a data scientist without a bachelor’s degree.
You can also specialize in a single subfield of data science, becoming a master or authority.
Here are some skills you can develop and improve:
Programming
Once you become a certified data scientist, you’ll spend considerable time using various programming languages to build and develop tools and data models for your tasks.
For instance, you may need to build a database to store all the data collected from various sources in your organization. Improving your SQL programming knowledge can help you build, manage, and deploy databases better.
You can also learn and polish additional programming languages. Here are the top languages used by data scientists all over the world:
- Java
- C/ C++/ C#
- Python
- R
Machine Learning
Machine learning and deep learning are two modern tools every data scientist needs to have under their belt. By enhancing your expertise in machine and deep learning, you can persistently improve the quality of data you collect, which increases the accuracy of your models and your success at forecasting.
Data Visualization
Part of being a data scientist requires you to generate and use charts, graphs, and illustrations that help stakeholders understand complex data and make actionable decisions that impact the direction and strategy of a business.
For better presentation, you can enhance your capacity to use data visualization tools like Microsoft Excel, Power BI, and Tableau.
Communication
As a data scientist, you’ll make complex data accessible to everyday users, and this may include generating reports for key stakeholders in other departments. Developing your communication skills can help you interact, share ideas, and persuade decision-makers to adopt your recommendations.
Step 3: Consider Pursuing Additional Certifications or Attending Bootcamps
If you already have a bachelor’s degree in data or computer science, you can scale your education by obtaining a postgraduate qualification such as a master’s or PhD. These qualifications can help you advance as a certified data scientist, earning you higher-level supervisory and management positions.
If you prefer to learn data science using a different path, you can attend data science boot camps virtually or in person. Bootcamps are intensive and immersive experiences that teach you programming, machine learning, AI, or other skills while helping you improve over a few weeks or months.
Boot camps are valuable to people looking to advance their knowledge or switch to data science from other careers. Boot camps can help you assess your suitability as a data scientist. You can use that period to determine if data science is an ideal career path without committing thousands of dollars to a degree program.
Step 4: Complete an Internship
Internships are an excellent way to enter the job marketplace and build a career as a data scientist. They can fast-track your path to working with data and expand your knowledge and skills as you earn experience in an actual workplace.
Internships allow you to exercise the knowledge and skills acquired in school, boot camps, and other additional programs. In an internship program, your employer places you in a data analytics position under a supervisor and a team.
You’ll learn data science while on the job, applying your knowledge and skills to solve challenges. Additionally, you learn how to communicate, collaborate, and work with colleagues to achieve the success of the organization you work for.
Internships typically range from a few months to a few years. No matter the length of your internship program, seeing it to completion is the key if you want to be well-prepared for the job market.
Step 5: Apply for Jobs
Once your internship program is complete, you get the chance to move into full-time employment. Your company may absorb you as permanent staff if you were exceptional during your internship period. Otherwise, you can search for entry-level positions for data scientists, engineers, and analysts.
When applying for data science jobs, consider these factors:
- Ensure you read and understand the requirements of each position you apply to. Apply only to relevant jobs and organizations needing someone with your skillset and expertise.
- Search for entry-level jobs on reliable job boards, social media platforms, and company websites. Some examples of excellent sources include indeed.com and LinkedIn.
- Join a private or public community of data scientists. You learn about new and open data science positions across industries and locations through these networks.
- Format your resume so application tracking software can parse and identify you as a top candidate.
- Build a portfolio to showcase your work and contributions to joint projects with other data scientists.
Once you successfully apply to a position and a company brings you on, you may demonstrate your skills as a certified data scientist and work your way up the ladder.
Data Science Specializations to Consider
Data science is an expansive profession with plenty of subfields and opportunities. Although you may opt to be a general data scientist, specialization has benefits.
For instance, increasing your expertise as a healthcare data scientist guarantees you better opportunities for growth in the healthcare industry, which is different from being a data scientist in another profession.
Specialization eases your pursuit of a fulfilling career in data science in an industry you’re passionate about. It also helps you fit better into the workplace and offer actionable solutions to businesses and organizations across your chosen industry.
How to Become a Healthcare Data Scientist
If you want to become a certified data scientist in healthcare, a medical background is required to succeed. You may need expertise in pathology, epidemiology, or pediatrics to offer your services effectively in these fields.
Healthcare data scientists collect, clean, and analyze patient data to help hospitals and medical facilities make better medical decisions and improve patient care. They can also use machine learning models and other tools to predict future medical events and suggest solutions to manage or mitigate them.
Like a data scientist without a specialization, you’ll need technical expertise in programming, data collection, statistics, and machine learning models to become a healthcare data scientist.
How to Become a Full-Stack Data Scientist
A full-stack data scientist collects data, handles feature engineering, builds models, and applies these steps to make correct predictions from the data collected efficiently.
Much like the standard certified data scientist, a full-stack data scientist must have the right skills for the job, including programming, machine learning, AI, and statistics. However, they may also need to possess a broader knowledge of computer science, using the skills required to build functional data models for better decision-making.
How to Become a Financial Data Scientist
To become a financial data scientist, you’ll need the skills and expertise to collect and interpret financial data to help businesses grow, manage risks, expand, and plan for the future.
Additionally, you should have a background in finance, data collection, statistics, programming, and machine learning models, all essential tools many businesses employ in the modern workplace. An extensive background in business and finance may help you understand, build, and manage appropriate data models for your organization and recommend crucial action steps for major stakeholders.
How to Become a Clinical Data Scientist
Much like a healthcare data scientist, a clinical data scientist should have a solid medical background or experience as a clinician. This gives you the core skills needed to create clinical data models and help solve medical challenges for institutions and patients in all aspects of clinical research.
How to Become a Big Data Scientist
Big data involves extracting valuable insights from large, diverse, and rapidly generated data sets to inform decision-making and uncover patterns that traditional data processing methods may struggle to handle.
A big data scientist focuses on analyzing, collecting, cleaning, and interpreting vast and complex data sets characterized by volume, velocity, and variety, especially for companies that generate massive amounts of data within short periods. To become a big data scientist, you’ll need the skills of a standard data scientist but with a focus on big data.
