How to Become a Data Scientist in 2023


5 Steps to Start Your Career

Learn how to become a Data Scientist and discover everything you need to know about launching your career, including the skills you need and how to learn them.

Five Steps to Becoming a Data Scientist

If you want to pursue a career as a Data Scientist, there are five main steps you’ll need to take:

1. Learn the essential data analysis skills.
2. Gain a qualification that demonstrates these skills.
3. Practice your Data Scientist skills on a variety of projects.
4. Create a portfolio of your work.
5. Apply for entry-level Data Scientist jobs.

In this post, we’ll look at each of these points in more detail, giving you everything you need to know to get started on your journey to becoming a Data Scientist. You can find out about the top analytics careers in a separate post.

Why Start a Career as a Data Scientist?

In recent years, there has been an increase in people searching for how to become a Data Scientist. The role has become increasingly popular, which comes as no surprise with the massive amount of data we create in the modern world.

Companies in all sectors need specialists who can harness data, analyze it, extract meaningful data-driven insights from it, and use those insights to help them to solve key business problems. As a result, there are several reasons why you might consider a career as a Data Scientist:

• The demand for roles. The job market for Data Scientists booming, and it’s not showing signs of slowing down anytime soon. Data from the US Bureau of Labor Statistics suggests the number of jobs for analysts is expected to grow by 23% between 2021 and 2031. If you’re looking for a future-proof career, this could be the one.
• The salary. Data Scientist roles are typically well-paid, with average salaries in the US around $73,632.
• Job satisfaction. Data Scientists are generally happy with their jobs, according to PayScale data, with an average of 3.9 out of 5 stars.
How to Become A Data Scientist

Below, we’ve outlined how to become a Data Scientist from scratch. Much of this assumes that you’re coming to the profession fresh, although much will be relevant to those with some of the fundamentals already.

In terms of how long it takes to become an analyst, that very much depends on the individual. Those with little existing knowledge and experience can master the skills within a few months. For others, it will take several years of study.

However, essentially, you’ll need several ingredients to thrive in the industry, including:

A natural curiosity about data `

• Key programming languages and skills
• A portfolio of relevant projects
• A properly written resume
• Compelling profiles on LinkedIn and similar websites
• Networking with other data specialists

Step 1: Learn The Essential Data Analysis Skills

Start with the basics of Data Scientist. The popular belief is that to start learning data analysis, one has to be good at mathematics, statistics, or programming. While it's true that a background in these fields provides a solid technical basis, it doesn't mean that a career in data analysis is unapproachable for people from other educational and professional backgrounds.

Learning to analyze data will require some intensive study, dedication, and a great deal of practice. You have to maintain a certain level of optimism even when you are stuck, exhausted, discouraged, or can't see any progress.

An aspiring Data Scientist should be creative and curious about data, have an exploratory mindset, be able to think analytically, be able to work both independently or in a team and be willing to dedicate the necessary time and effort. These are all qualities that will help ensure your success.

You’ll want to start by mastering some basic data analysis skills, such as the fundamentals of statistics, such as including measures of center and spread, probability distributions, and hypothesis testing.

You can also start with things like data analysis in spreadsheets to get a feel of collecting and wrangling, and learn how exploratory data analysis in Power BI and other tools works.

Choose a programming language to learn.

Data Scientists will usually work with several programming languages, which means there is no wrong or right choice. Essentially, you’ll need to master SQL for querying and manipulating databases, but you’ll then need to choose between R and Python for your next programming language.

You can find a comparison of Python vs R for data analysis in a separate post. You can also learn to become a Data Scientist with R or Python with our tracks.

Essentially, at this point, you’ll be learning how to import, clean, manipulate, and visualize data with your preferred programming language. You’ll discover some of the libraries that can help you with various tasks and improve your programming skills.

Step 2: Get Qualified in Data Analysis

Most employers will want to see evidence of your qualifications in data analysis. There are various routes to this, and much of it depends on your current level of education and familiarity with the subject matter.

For example, several universities offer bachelor's and master's degrees in data analytics, but undertaking this path will require a substantial investment of both time and money: you’ll need to devote 2-4 years to full-time study, and it could cost anywhere from $30,000-$200,000.

Additionally, if you enroll in a bachelor's degree program, you’ll be required to fulfill course requirements outside of data analytics. A degree isn’t necessary to become a Data Scientist, but it can certainly help.

Learning online gives you the flexibility to learn from wherever you are in the world, provided you have a computer and internet access. What’s more, learning through an online program is dramatically less expensive than learning at a university: for example, you can complete our Data Scientist Career Track and Data Scientist certification in less than a year at a fraction of the cost of university study.

An ideal online self-study program includes an exhaustive and well-balanced curriculum that covers the most important data analysis topics and techniques – along with plenty of opportunities to practice them.

So, if you’re looking for how to become a Data Scientist with no experience, you’re learning path will look something like this:

Step 3: Practice Your Data Scientist Skills

Once you’ve mastered some of the essential skills, you’ll need to start developing them on your own. There are a variety of ways you can do so:

Work on individual projects

You will have abundant opportunities to put your new skills to work by doing various exercises and completing the data analysis projects suggested by your curriculum. Practicing your skills and solving mock or real-world problems will give you a solid basis for your future work experience.

At this stage, having access to some real, clean datasets and preselected ideas to explore will help maintain your interest in learning and avoid the distractions of additional searching or brainstorming. You can look at our range of Data Scientist projects to help you put your skills to the test.

However, the time will come for you to prepare yourself for real-world work experience as a Data Scientist, and you’ll need to proceed with more advanced studies: In order to best “sell” yourself, you’ll want to showcase your ability to work and research independently to a potential employer.

Hence, you will need to undertake individual projects where everything will be your responsibility: selecting the topic, fetching the necessary data, contemplating the direction of your research, designing the project structure, making, and checking hypotheses, effectively communicating your findings, and laying out the way forward.

As a result, individual projects usually take much more time than guided ones, but they will help you to stand out from the crowd when applying for a job. Use free datasets for data analysis projects.

As soon as you come up with a good topic to develop in your project, your next step is to find the relevant data to explore. For this purpose, there are numerous online repositories offering a variety of free datasets. As well as AITP projects, you can use:

• AITP Workspace – An online integrated development environment (IDE) with available datasets for writing code, analyzing data, and practicing your skills.

• Kaggle – The most popular website that stores thousands of free datasets on various topics, both real-world and synthetic.

• UCI Machine Learning Repository – Contains open-source datasets. Most of which are clean, well-structured, and well-documented.

• FiveThirtyEight – Here you will find interactive data-driven articles on different mainstream topics, as well as the datasets used for these articles.

• Google Dataset Search – A keyword-based search engine, just like normal Google search. It stores more than 25 million free public datasets.

Step 4: Create A Data Scientist Portfolio of Projects

By this point, you should be well on your way to becoming a Data Scientist. However, to get in front of potential employers, you’ll need to have a portfolio of your work. You can check out our full guide on building a Data Scientist portfolio for some inspiration.

When you first come to the job market as an entry-level Data Scientist, it's understandable if your initial portfolio of projects contains mostly guided capstone projects from your online boot camp or data-related university work. At this stage, it is also perfectly fine and expected to have many disparate boot camp projects on different studied concepts, showing a variety of tools and techniques.

However, you want to use your portfolio to make your passions and interests shine through. Tell a story about how your skills have developed and where your expertise are. You want to demonstrate both your technical and soft skills and design it to appeal to your readers.

Ideally, you want to avoid cookie-cutter projects that everyone has, and instead, focus on the projects you’ve completed that are most interesting to you. The more you develop your portfolio, the more you can remove the broad-scope, common projects.

When you feel that you are ready to explore a specific business area of your choice, you may start focusing on gaining domain knowledge and making individual projects related to that sphere.

You can keep your portfolio of data analysis projects on AITP Workspace, GitHub, or Kaggle free of charge. They are not the only free platforms for hosting such portfolios, but these two are widely popular and are the best choice for an entry-level data specialist as they ensure good visibility for your projects. You might also want to consider creating a personal website.

Step 5: Start Applying for Entry-Level Data Scientist Jobs

Once you’re confident in your abilities and portfolio, it’s time to think about how to get a job as a Data Scientist. We've got some tips below:

Be sure you have the required skills.

Before starting your job-hunting process, you may want to make a quick revision of your Data Scientist skills and compare it with the requirements for this role in the modern market.

A good place to start is to look at the descriptions of several job positions for Data Scientist and write down the skills that are currently most in demand. You can check out our Data Scientist job description template for some inspiration.

To get a job as a Data Scientist, here are the basic technical skills that companies usually expect to see:

• Python or R (especially their specialized libraries for data analysis)


• The command line

• Statistics

• Data cleaning and wrangling

• Data analysis

• Data visualization

• Web scraping

• Debugging

• Data storytelling

• Dealing with unstructured data

If the list above looks overwhelming to you, don't feel discouraged; you likely won't need all these skills for every Data Scientist job. Usually, each company looks for a different set of skills in a suitable candidate.

The best way to find out the specific requirements of a certain employer is to read the corresponding job description. If, at this point, you feel that you lack some crucial skills, consider upskilling.

Finally, don’t forget that there are also some important Data Scientist soft skills:

• Analytical thinking

• Multitasking

• Curiosity

• Creativity

• Communication skills

• Flexibility

• Ability to work both independently and in a team

• Decision making

• Business domain knowledge

• Create a professional Data Scientist resume

Now it's time to write your resume. At first glance, it may seem to be an easy task. However it’s worth dedicating some time and effort to building a compelling and professional-looking resume that can capture the attention of recruiters. You may find the following article helpful: Tips to build your data scientist resume.

While this article talks mostly about creating a resume for a data scientist role, most tips from here are applicable to any other data-related profession. Let's briefly outline the most important suggestions from the article:

• Fit your resume on one page.

• Select an appropriate resume template. You can create it from scratch or use an online resume builder with a variety of resume templates. You may wish to consider the following resources: Resume, Zety, Resume Builder, Canva, CakeResume, VisualCV, ResumeCoach.

• Create your master resume. This can be a long-form, very detailed version of your resume with many pages and a lot of bullet points. Here you can include all of your work experience (even previous unrelated work experience if you are a career-changer), studies, projects, technical and soft skills, and other relevant info. You can use this version of your resume as a basis for applications for any Data Scientist’s job position.

• Customize your Data Scientist resume to each job description you apply for. Read the job description carefully, figure out the requirements that the company is looking for in a candidate, and incorporate/highlight the necessary skills and keywords in your resume. In addition, you can explore the company's website (its mission, values, and products), and refer to it to make your resume reflect that you are the perfect fit.

• Be concise but informative.

• Use plain but efficient language.

• Check for errors and typos.

• Consider including the following sections:

• Contact Information

• Objective

• Work Experience

• Projects

• Skills

• Education

The order of appearance of the last four sections depends on your real relevant experience and, hence, on what you want to showcase first. Develop your online profiles.

Your LinkedIn/Kaggle/Medium/GitHub or any other relevant professional profile should be in line with your Data Scientist resume or even represent your resume in miniature. The main goal here is to let your readers know that you are a Data Scientist, even if you don't have real work experience yet in this sphere.

In other words, you must promote yourself and create a unique personal brand to enter the competitive labor market of data analysis.

Below are some useful tips:

• Keep your professional profile and portfolio of projects updated.

• In the headline, write Data Scientist instead of your current profession, if you are a career-changer. Avoid adding the word aspiring to your headline.

• Include your photo and maybe a data-related cover picture.

• Provide a professional way to contact you.

• Include any relevant licenses, certifications, skills, accomplishments, recommendations, and cross-links to your other professional profiles.

Some of the suggestions for writing an efficient resume are also applicable here. Be concise but informative, use plain but efficient language, check for errors and typos, double-check your contact details, and avoid including your skill level.

Network with other data professionals

Since you are trying to enter a completely new sphere, you need to start growing your professional network in the data world. Creating a compelling LinkedIn, GitHub, or similar profile is a great first step.

However, you can be even more proactive by joining various data communities or groups, participating in online and live meetup events and conferences, following and connecting with the right people on social networks, commenting on social media data-related content, and publishing articles on data analysis topics.

Surround yourself with data professionals, and the AITP, community of data enthusiasts can give help and support and broaden your contacts in the data world.

Applying for Data Scientist job openings

Finally, you’ve learned how to become a Data Scientist, and it’s time to start applying for your first job. There are plenty of resources out there for finding work, with general job portals such as

LinkedIn, Indeed, Google for Jobs, SimplyHired, AngelList, and Hired all having listings.

For more data-oriented job boards, check out AITP Jobs to find and land your dream roles in data. Plus, keep in mind that if you obtain your Data Scientist Certification through AITP, you’ll receive job-search support that’s tailored to your individual needs from our career services team.

Once you've secured an interview, you'll need to prepare for some Data Scientist interview questions, details of which you can find in a separate article.

Reach out to potential employers directly.

In parallel with this job searching approach, you can try a less conventional, more time-consuming, but also more efficient method: reaching out to companies of interest directly.

To take this route, first find their official website, explore their home and career pages, and find their contact details. Read about their mission and values, their services and products, and anything else that’s relevant. Try to figure out how you could be an ideal candidate for this employer.

Once you are more informed about what their business looks like, you can send them an email with your data analysis resume customized exactly for that company and demonstrating that you are a perfect fit for them. This is indeed a safe way to stand out from the crowd in the eyes of that employer.

Keep good records and adjust your strategy along the way.

When sending your resume, whether to various job portals or directly to an organization, keep a record of the resume versions you sent and the corresponding company names and job descriptions.

Don't be discouraged if you don't manage to find a Data Scientist job immediately. It is absolutely normal if your job search process takes some time and remember that rejections are an inevitable hurdle for most. Your failures shouldn't frustrate you or make you lose hope.

Keep applying for new job positions and continue sharpening your technical skills. Try to analyze what could be improved in your resume, your portfolio, or your job-hunting process and make alterations accordingly.

In the case of rejections, always ask for feedback and, if you get it, try to make the most of this information by reinforcing your strengths and working on your weak points. If you follow all the suggestions from this article, then landing your first job as a Data Scientist is just a matter of time, persistence, and hard work.

Become a Data Scientist with AITP,

By now, you should know how to become a Data Scientist and what you need to do to make your goal a reality. In this post, we discussed:

• The prerequisites you need to have to start learning data analysis.

• how to select the optimal qualification.

• Why individual projects are so important for your portfolio and where to search for datasets.

• Other projects that can be added to your portfolio.

• The technical and soft skills companies usually look for in Data Scientist.

• The nuances and tricks for creating an outstanding resume.

• The importance of your online presence, visibility, and interaction with data specialists.

• Where and how to search for a job and how to keep optimistic and confident whilst applying.

With all this information in hand, it's time for you to go ahead and start learning today. Become an AITP Certified Data Scientist

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