How to Become a Data Scientist

Becoming a data scientist in today’s economy ensures your skills will remain in high demand, providing job security in an ever-evolving tech landscape. Data science careers are financially rewarding and globally sought-after, which means you’ll have the flexibility to work in various regions and industries around the world. 

If you want to learn how to become a data scientist, the following guide is the perfect place to find all the information you need. We discuss everything from data scientist requirements to what a data scientist does on a daily basis. You’ll also find an in-depth guide on how to get a job in data science, regardless of your previous work history or education. 

What Does a Data Scientist Do?

A data scientist collects, analyzes, and interprets data to extract key insights, inform decision-making, and solve problems across various industries. Some data scientists play the essential role of creating new machine learning algorithms and models to help computer programs make better predictions. We cover what data scientists do on a daily basis in the breakdown below. 

Data Collection 

Data collection is the process of acquiring data from various sources, such as news sites, surveys, social media, and even website cookies. Data scientists must collect data generated from millions of users all over the world. It’s a significant part of what data scientists do and requires diligent work.

Data Cleaning

To have a career in data science, you will also need the skills to scrub through raw data and categorize it accordingly. This means looking for inconsistencies and missing data to make sure there are no errors in their analysis. This requires strong attention to detail and technical skills to ensure the data scientist has clean data sets to work from.

Data Analysis

After the data is cleaned, the data scientist begins inspecting the information through data analysis. Data analysis is the process of inspecting data closely to find key patterns and trends. This is one of the more technical tasks you’ll tackle in a data scientist position. 

There is a great deal of statistical analysis and visualizations done in this stage to make data more digestible. Data analysis highlights the points in need of further analysis. Visualization tools allow data scientists to note the outliers and explore these further. Data scientist job descriptions usually ask for experience with visualization tools like Tableau and Excel. 

Data Modeling

Another essential aspect of most data science roles is data modeling. Data modeling is the process of analyzing and organizing how data should be updated, collected, and stored. 

There are various ways to build a model. It could either be through machine learning techniques or statistical modeling. Only after the modeling can a data scientist start deriving insights from it.

Data Interpretation

Data interpretation is the derivation of insights from a data model, and it is a key component of what data scientists do. Data scientists must present these insights in a way that stakeholders can understand.

This is when one has to go back to the drawing board. How do the insights answer the questions posed at the beginning of the project? What’s the best way of communicating these insights? For example, a data scientist could use information about user preferences to recommend products to a user on an ecommerce site.

What Are the Requirements to Become a Data Scientist?

Becoming a data scientist often requires a Bachelor’s Degree in Statistics, Computer Science, Mathematics, or a related field. However, this is not always the case for entry-level positions. Other data scientist requirements include experience with machine learning models, analysis tools, various database systems, and data visualization technology. 

The most common data scientist qualifications include:

• Bachelor’s degree. Getting a bachelor’s degree is the most straightforward way of starting a data science career, though some employers will be willing to hire data science bootcamp graduates or self-taught applicants with impressive work experience. For advanced roles, you might need a master’s degree or PhD.  

• Professional experience. Relevant work experience, such as internships or previous roles in data analysis, data engineering, or related fields, can greatly enhance your prospects. Real-world experience is an important data scientist requirement, so make sure you have a portfolio that demonstrates your practical skills.

• Strong quantitative skills. Proficiency in mathematics, including linear algebra, calculus, and probability, is essential for understanding and developing data science algorithms. Your statistical knowledge is also critical for data analysis, hypothesis testing, and model building. 

• Technical ability. Data science careers require experience with a wide range of technical skills, including programming languages, data manipulation tools, and machine learning models. 

How to Become a Data Scientist: Step-by-Step

Becoming a data scientist is a very achievable goal if you have the right amount of passion and are willing to master the right skills. There is more than one way to enter this field, so the process of how to become a data scientist looks a little different for everyone. The detailed breakdown below will lay out the seven key steps you should follow to launch a data science career.

Step 1: Earn a Data Science Degree or Equivalent

Articles upon articles have pitted college degrees against bootcamp certifications. In the past, pursuing a computer science degree was the main path toward becoming a data scientist. These days, employers are far more focused on applicants’ abilities than their educational credentials, so many people choose to attend data science bootcamps instead. 

Coding bootcamps are intensive programs designed to equip you with the skills you need to break into a specific field in the tech industry. The allure of the bootcamp model lies in its short, immersive, and affordable structure. Premier data science bootcamps offer career support post-program to help you immediately secure a job and thrive in it. 

Step 2: Build Your Data Science Portfolio

To succeed in a career in data science, you’ll need to put what you learn into practice. Few employers hire data scientists without real-world experience. So, finding ways to build some projects to add to your data science portfolio is important. If you attend a bootcamp instead of a data scientist degree program, you may already have a workable portfolio when you graduate. 

Your data science portfolio acts as proof of your data science skills. It displays the quality of your work as well as how you work. Check out our guide on data science projects for beginners. There, you’ll encounter some basic data science projects with links to relevant datasets. Once you’ve accomplished these, your next step is to launch your project to the world. Read about how to launch your portfolio project online through Career Karma.

Step 3: Earn Data Science Certificates 

Certifications and certificates help you verify your skills and experience within the industry and will be invaluable once you start on your resume. Online courses offer certificates that prove your skills with specific processes and data science technologies. You can also sign up for certification exams if you want to be certified as a data scientist. 

Certified data scientists can hold many distinctions, such as Certified Analytics Professional, Data Science Council of America Senior Data Scientist, and Cloudera Certified Associate: Data Analyst. You can learn more about how to become a certified data scientist later on in this article.

Step 4: Gain Data Science Experience Through Internship Programs

Getting a data science internship is another way to boost your profile and gain notable data science experience. Scour popular job websites, such as Indeed and LinkedIn, for great internship opportunities. If you already have a company in mind, check its website for any internships. You can also apply for entry-level jobs at this stage if you feel ready. 

Remember that you’re not the only one on the lookout for data science experience, so use all the resources at your disposal. Do you know someone in the company that you can use as a referral? Does your school have some kind of partnership with a data science company? Most coding bootcamps have employer partnerships that you can leverage for an internship opportunity.

Step 5: Finalize Your Data Science Resume

Hiring managers glean their first impression of you through your resume. A neat-looking CV that represents your skills well is essential. A good data scientist resume should be free from errors and showcase your experience, education, and skills. Overall, your data scientist resume is a great place to showcase your strong background in the field. 

You should make sure to list all degrees, certificates, and certifications that you have earned. In addition, you can highlight the data science teams you have worked on as well as database systems you have experience with. 

Step 6: Prepare for Your Job Interview

While your portfolio may look good, hiring managers will want to make sure that you’re a well-rounded candidate. This means you will need to exhibit an understanding of data science beyond the projects you’ve completed. A technical data science interview allows them to test your knowledge.

To ace your data science interview, you should be prepared to answer a variety of technical questions. To prepare, brush up on common data science terminology, models, theories, and more, before your scheduled data science interview. You want to walk in ready to impress the interviewer. 

Step 7: Apply For Jobs

After developing skills, building a data science portfolio, and gaining experience, it is time for the most obvious part of how to become a data scientist. It is time to apply for data scientist jobs. Thankfully, there are many data scientist jobs out there today, as the field is one of the fastest growing in the tech industry. 

At this stage, you will need to answer important questions such as whether you want to work as a data analyst or data engineer and what industry you want to work in. When you have a clear vision of your professional goals and desired data scientist career path, you are ready to apply for data scientist jobs. 

How Long Does It Take to Become a Data Scientist?

You can become a data scientist in eight to 12 months if you study intensively or participate in a data science apprenticeship or internship. It can take three to five years to learn data science comprehensively or to prepare for a more advanced career. 

You will find that it takes longer to become a data scientist if you don’t have any background in programming, statistics, or mathematics. In general, the length of time you spend studying depends on the learning path you choose. A bachelor’s degree will take around four years to finish, whereas you can complete a data science bootcamp in less than one year.  

Essential Skills for a Data Scientist

If you want to know how to become a data scientist, learning about the essential skills is a great place to start. A data scientist career path requires professionals to not only have a specific educational background but also specific skills and theoretical knowledge. Here are the most important tools and technologies you’ll find listed in most data scientist job requirements. 

Data Scientist Hard Skills

• Programming languages. Data scientists use coding languages like Python and R for data analysis, data visualization, and machine learning. You will likely need experience with Python libraries, including NumPy, Pandas, and Scikit-Learn. 

• Statistical analysis. Statistical techniques like regression analysis, hypothesis testing, and Bayesian statistics are essential data science skills because they allow you to make data-driven decisions. 

• Machine learning. Experience with machine learning algorithms is only becoming more essential as we increasingly rely on artificial intelligence. You should be familiar with supervised learning algorithms, unsupervised learning techniques, and deep learning neural networks. 

• Data visualization. Whether you’re a junior data scientist or a senior data engineer, visualizing data is a critical skill for data science. This is because you need to be able to communicate your insights to others without your specialized knowledge. Tableau and Matplotlib are two of the most common data visualization tools. 

• Data manipulation and cleaning. This process involves data wrangling, which usually refers to cleaning and preprocessing data until it is in a suitable format for analysis. SQL, used for database querying and management, is another data science required skill. 

Data Scientist Soft Skills

• Excellent communication skills. You’ll need to communicate with others to share your findings in any data scientist job. To do these things effectively, you must know how to communicate your insights depending on your audience. As such, communication is one of the most important data science soft skills. 

• Strong business mindset. Data science is all about solving business problems through data. You’ll have to approach problems as if you were an executive. This means having a strong understanding of particular business operations and strategies to give actionable insights. 

• Critical Thinking. Being able to think critically is one of the most essential data science soft skills when evaluating data and finding insights in large datasets. This entails tackling problems from different angles and perspectives.

Data Scientist Career Paths

Starting a career in data science can mean anything from working in database management to artificial intelligence, as there are numerous opportunities in this field. Additional specialties in data science include machine learning, data visualization, business intelligence, and data engineering. Below, you can learn more about some of the options you have when it comes to a data scientist career path. 

Data Analyst

Many people begin their data science career path by applying for data science internships or working in an entry-level position like data analyst. Data analysts spend most of their days analyzing data and writing recommendations. They work with existing data and provide a summary of sorts that details the company’s performance. 

The real-world experience you get in this data scientist career will help build your advanced analytics skills and coding abilities. Networking effectively and building a professional portfolio of projects during your early career can accelerate your data science career progression in the long run. 

Data Scientist

This is perhaps the most obvious job title in the data science field. Data scientists work in every part of the data process, from collecting to organizing, analyzing, and interpreting. Data scientists must provide insights to help organizations come up with strategies to meet their goals. If you want to become a data scientist, you will need a bachelor’s degree or several years of real-world experience. 

Data Engineer

Data engineers work on building data infrastructure and dataset processes that data scientists use for analysis. Think of them as software engineers and data scientists combined. You need data science and software engineering skills to thrive in this data science career path. You must be fluent in programming languages such as Python, R, Java, and Scala.

Big Data Scientist 

A big data scientist specializes in handling and extracting insights from vast and complex datasets, often characterized by high volume, velocity, and variety. They possess the skills to design and implement scalable data processing solutions and use advanced analytical techniques to uncover valuable patterns and trends within this data. 

Machine Learning Engineer

A machine learning engineer is one of the most advanced and lucrative positions on the data scientist career path. These experts specialize in developing and deploying machine learning models and algorithms for specific applications. They focus on designing and training models that can make predictions, classify data, or automate decision-making processes. 

Data Scientist Salary and Job Outlook

The average data scientist salary is $103,500, according to the Bureau of Labor Statistics (BLS). This is a field that is especially well-compensated. Glassdoor reports that even entry-level data scientist salaries can be as high as $127,505 and the average senior data science salary is $203,865. As with other jobs, data scientist salaries are not set in stone. Their earnings may vary by company, location, and experience. 

One of the best aspects of working in the field is the data scientist job outlook. According to BLS, the data science field will grow 35 percent from 2022 to 2032. This is significantly higher than the 3 percent average for all occupations. This projected job outlook for data scientists speaks to the impressive growth opportunities you will experience as you progress in your career. 

Data Scientist Education Requirements: Data Science Courses, Bootcamps, and Training

There is no singular data scientist education path that you must take to work in this sector. If you are wondering how to learn data science when you don’t have the time or resources to gain a data science degree, you’ve come to the right place. Below, we detail various data scientist education options that will set you up for success in the job market. 

Data Science Bootcamps

Below are the bootcamps that offer the best immersive data science programs. As we have discussed, data science bootcamps are career-focused. Students often receive hands-on experience and work on real-world projects in their courses. You will have access to professional career services to make breaking into data science significantly easier.

Some of the best data science bootcamps include:

• Flatiron School. A consistently top-ranking bootcamp, Flatiron School advances a holistic learning approach that boasts a curated community and passionate instructors. Flatiron School’s data science bootcamp curriculum gravitates toward developing your fluency in Python and skills in machine learning. 

• NYC Data Science Academy. This bootcamp was specifically created to provide data science training. Lessons are delivered through remote live instruction and pre-recorded videos. Students build statistical models while learning about linear regression, data classification, and visualization, and study both Python and R. 

• General Assembly. General Assembly is another top professional training school. Its programs come in online or on-campus formats. Its Data Science Immersive program is a 12-week data science bootcamp that focuses on data science essentials, including Python programming, exploratory data analysis, data modeling, and machine learning.

• Byte Academy. Byte Academy offers a comprehensive curriculum that hones your knowledge in data science fundamentals. The data science bootcamp is divided into five modules, namely Python, SQL, mathematics, data visualization, and machine learning. What makes Byte Academy’s programs unique is their AI tutor, Aiza, created to guide students as they learn.

Is Data Science a Good Career?

Yes, data science is a good career because people in this field have specialized skills that are in demand in nearly every industry, which leads to very competitive salaries, career flexibility, and stability. The Bureau of Labor Statistics predicts that there will be 59,400 additional data science jobs every year for the next decade. 

Luckily, there are several ways to see if becoming a data scientist is the career path for you. Multiple courses, books, and bootcamps are all designed to help you go from a beginner to an experienced data scientist. Make sure to look into any of the programs mentioned above and learn more about how to become a data scientist.