How to Become a Data Scientist

You may be interested in becoming a Data Scientist, but have no idea how to start on this career path. Knowing the steps to become a Data Scientist helps you to map your career goals, determine the best way for you to learn the necessary skills to reach those goals, and provides you with actionable steps for pursuing a data science career. The following sections detail the work of a Data Scientist, the tools used for learning data science, steps in a Data Scientist career path, and more. 

What is a Data Scientist?

Data Scientists extract meaning from raw data to detect patterns and propose solutions that meet an organization’s needs, especially the needs to compete and grow. A Data Scientist’s responsibilities include finding valuable data from data sources, developing machine learning algorithms, improving data collection procedures, cleansing and validating data integrity to ensure accuracy, and detecting patterns and solutions based on data. Data Scientists build models based on data, create data visualizations that communicate patterns and findings to stakeholders, and automate collection processes. Because data plays a critical role in the success of any organization, Data Scientists can build careers in business, technology, finance, nonprofits, and many other industries. 

Those who wish to become a Data Scientist should develop the analytical, statistical, and programming skills needed to manage and interpret raw data. These skills include understanding statistics, machine learning, and reporting tools. Aspiring Data Scientists also benefit from understanding the programming languages R, SQL, Python, Java, and C++. 

Read more about what a Data Scientist does.

What Tools Do I Need To Get Started?

Data Scientists work with some of the latest technology including artificial intelligence and machine learning, as well as programming languages that constantly evolve. It is therefore essential that Data Scientists understand how to make the most of the tools that enable Data Scientists to gather, clean, sort, analyze, and make sense of data. Tools commonly used by Data Scientists include: 

• Python programming
• SQL 
• Data visualization tools

Python

Understanding Python programming is key to becoming a Data Scientist. Data Scientists use Python to gather data, automate tasks, improve data collection processes, create data visualizations, and more. Python is an open-source programming language, meaning that it is free for anyone to use. This means you can access the latest version of Python and Python libraries at no cost. Python also enjoys an active group of supporters who help one another learn best practices, make recommendations for improvements to the language, and more. While Python is considered one of the easier programming languages to learn, it helps to learn from an instructor who can provide step-by-step guidance, answer questions, and help you learn through hands-on activities. 

SQL

Standard Query Language (SQL) allows Data Scientists to explore databases and retrieve data from them. It also allows Data Scientists to filter, sort, and group data to identify information that helps an organization achieve its goals. There are both free and paid SQL tools available. 

Data Visualization Tools

Data visualizations make it easier for stakeholders to see patterns and understand the state of the data. Examples of data visualizations are graphs and charts. Data Scientists use the Python library Matplotlib and the software tool Tableau to create interactive dashboards and data visualizations. Knowing how to create a data visualization enables a Data Scientist to plan and present data stories, assemble data from various sources, manipulate data, enhance presentations, and more. Matplotlib is a free Python library. Tableau offers a free version of the software called Tableau Public.

Steps to Become a Data Scientist

If you are interested in starting a career as a Data Scientist, you will want to outline the steps needed to meet your goal. This includes outlining your career goals, identifying the right learning method, dedicating time to learning the necessary skills, creating a professional portfolio, networking with others in your field of interest, and starting your job search. 

Determine Your Goals and Learning Path

The first step toward starting a career as a Data Scientist is to identify your career goals and learning path. Is there a particular area of data science you wish to specialize in? What skills do you need to become a Data Scientist? Data Scientists must understand how to use data management and visualization tools, how to use machine learning to process data and forecast outcomes, and how to use R programming, Python, Java, and sometimes other programming languages such as C++. Identifying the skills necessary to reach your career goal will help you determine what learning method to pursue.

How do you go about learning data science skills? While earning a four-year degree related to data science is an option, it is a costly and time-consuming one. Most working professionals wishing to launch a new career as a Data Scientist learn data science skills through on-demand classes, in-person classes, or live online classes. On-demand classes such as those found on Skillshare and LinkedIn Learning provide the most flexibility since you can stop and start the course whenever you choose. The major drawback to the on-demand/self-paced learning method is the lack of access to an instructor if you have questions. In-person classes are those taught by an expert instructor in a physical learning location, allowing you to network with local professionals such as your classmates.

In-person learning works well for those who prefer face-to-face, and it provides access to all necessary equipment and software. In-person classes also incorporate hands-on assignments that help you retain what you learn, develop your skills, collaborate with others, and build a professional portfolio. The major drawback to in-person classes is the extra time and expense that commuting to the class requires. 

Live online learning offers many of the same benefits as in-person learning including real-time feedback from your instructor, the ability to collaborate with classmates, and the use of hands-on assignments. Live online classes have the benefit of letting you learn from anywhere. Virtual classes do, however, require that you have the necessary software and tools needed for you to learn. Remote learning may also mean that your classmates do not live in your local area, so the class may not provide the chance to network locally.

When deciding which learning method works best for you, keep both your budget and learning preferences in mind. If you want to start learning data science skills before committing to a paid learning method, you can explore free learning tools through Noble Desktop’s Data Science Learn Hub. 

When you’re ready to explore paid learning methods, you can compare different learning options, including local classes, using the Classes Near Me tool.

Learn Data Science Skills

Once you have decided on the learning path that fits your needs, it’s time to start learning the data science skills needed to launch your career as a Data Scientist. Learning these skills requires setting aside dedicated time. If you are taking an in-person or live online class, you will meet a scheduled time, but you may also wish to supplement what you learn in class to help you retain the information and further develop your skills. The skills required to become a Data Scientist include learning programming languages such as SQL, R, and Python, understanding mathematics including linear algebra, calculus, statistics, and probability, and understanding machine learning, natural language processing, and file management.

A critical part of developing data science skills is gaining hands-on experience. In-person and live online classes often include hands-on activities as part of the curriculum. To truly understand the work of a Data Scientist, you must put lessons into practice through hands-on assignments. Such projects also allow you to build a professional portfolio. 

Create a Professional Portfolio

Creating a professional portfolio allows you to show prospective employers the evidence of your hard work and data science skills. Portfolios can also help you to land part-time and freelance jobs as a Data Scientist if you decide to pursue that path or wish to gain additional experience before finding a full-time role. So how does one assemble a professional portfolio for Data Scientist jobs?

If you participate in hands-on activities through an in-person or live online class, you can save your work as part of your portfolio. You can also add other projects that showcase your work if you choose. Having a mentor review your portfolio will help to ensure you have everything you need to make a good impression during your job search. Live online classes and in-person classes that include portfolio reviews and mentorship with your tuition can therefore help you have the edge over the competition when you start your job search.

Make Industry Connections

Making industry connections can greatly benefit you when you start your job search, but networking doesn’t just serve your short-term goals. The industry connections you make can prove helpful throughout your entire career. You can learn new skills from mentors and others in the industry. You may learn about new opportunities through your connections as you work to advance your career. 

In-person data science classes allow you to network with locals while learning the necessary skills to become a Data Scientist. You also have a professional connection through your instructor/mentor. Live online classes also connect you to industry professionals, though not always in your local area. If you want to network beyond your class, identifying local data-related professional organizations can provide a means of connecting with others.

Review Your Resume and Start Your Job Search

The final step to finding a job as a Data Scientist is to review your resume and start your job search. Reviewing your resume with your mentor or another professional helps you to ensure your resume stands out to recruiters. You will want to highlight the skills key to the job you want, including any soft skills relevant to the position. Also, take advantage of any other job search assistance in your data science program.

Once you have a polished resume, you are ready to start your job search. You can find Data Scientist job postings on Glassdoor, LinkedIn, Indeed, and more. Setting up email alerts for relevant keywords and postings can help you stay on top of all available opportunities.

Data Scientist Career Path without a degree

Data Scientists begin their careers with entry-level positions. You can find an entry-level Data Scientist position at many different types of organizations including social media companies, multinational corporations, technology companies, government agencies, and nonprofits. Many entry-level positions require no previous experience, however, if you are targeting positions that seek previous experience, or wish to make your resume more competitive, you can explore Data Scientist internships. Internships provide hands-on experience, allow you to build your professional portfolio, add experience to your resume, give you industry connections, and provide you with references. 

As Data Scientists gain more experience, typically over several years, and build their skills, they can advance to mid-level and senior positions. Such positions may include management responsibilities and overseeing more complex tasks. Promotions to mid and senior-level positions also come with a salary increase.

Some Data Scientists take an alternative career path by working as a freelancer. You can learn more about freelancing as a Data Scientist and how to become a Data Scientist without a degree. 

Read more about the typical Data Scientist career path.

Why Become A Data Scientist?

Data science can provide a challenging, in-demand, and rewarding career that is relevant now and will remain so well into the future. Data Scientists: 

• Earn high salaries
• Work with the latest technology
• Use problem-solving skills and innovate solutions
• Work independently and collaboratively
• Long-term stability

The U.S. Bureau of Labor Statistics found that the median annual salary for Data Scientists is around $100,000. Entry-level Data Scientists make between $75,000 and $95,000 on average with more experienced Data Scientists earning well over $100,000. 

Data Scientists work with some of the latest technology, including artificial intelligence and machine learning. They also use Python programming and other programming languages. Because data science tools and programming languages constantly evolve, Data Scientists are constantly learning and sharpening their skills, which keeps the profession interesting and relevant. Demand for Data Scientists is expected to increase by 36% between 2021 and 2031, showing that this career makes for a rewarding option now and in the future. 

Data Scientists work both independently and collaboratively, so the job is neither too isolating nor overly draining due to constant meetings or team activities. They solve problems, think of ways to improve processes based on patterns in data, and make recommendations to stakeholders, all of which makes this a mentally stimulating career to pursue.

Read more about whetherData Scientist is a good career.

Learn the Skills to Become a Data Scientist at Noble Desktop

If you are looking to start a new career in data science, you might think the only way for you to become a Data Scientist is by enrolling in a four-year university or pursuing other costly and lengthy educational options. However, there are many alternative methods available to help you transition into a data science career, including data science bootcamps and certificate courses designed to help working professionals gain the skills needed to obtain an entry-level job as a Data Scientist. Exploring in-person and live online data science bootcamps and certificate programs can help you find the class that meets your career goals, budget, and schedule. The first step to finding the class that fits your needs is to understand the differences between in-person classes and live online classes. 

In-person data science classes meet in a traditional classroom setting at a physical location. In-person classes have the advantage of providing all necessary equipment, such as computers and software, and allowing students to network with local professionals such as your classmates and instructor. You also have the advantage of learning from an expert instructor face-to-face. The primary drawback to in-person courses is the extra time and money required to commute to the physical learning location. Live online data science classes offer many of the same benefits as in-person classes, including the ability to learn in real-time from an expert instructor. You can also collaborate with classmates, and you have the advantage of learning remotely. 

Noble Desktop offers several different in-person and live online data science classes that can help you start a career as a Data Scientist. The Python for Data Science Bootcamp teaches students foundational programming concepts and how to handle different data types, use conditional statements to control the flow of a program, use Scikit-Learn, Matplotlib, Numpy, Pandas, and other Python libraries and tools. Noble’s Data Science Certificate program and Data Analytics Certificate program provide a deep dive into the topics and skills essential to launching a career in data science or data analytics and offer one-on-one mentorship and job search assistance. All Noble Desktop classes provide students with hands-on experience, flexible financing options, setup assistance, a free retake, small class sizes, and real-time guidance from an expert instructor.

Learn more about Noble Desktop’s in-person and live online data science classes.

You can also learn more about data science careers and data science learning options with Noble’s free Data Science Learning Hub.