You want a job in data science, but you do not have any job experience in that field. The good news is that it’s possible, and you don’t even need a college degree. However, there are some things you need to know and steps you might want to take to land the job.
Here’s how to start a data science career with no experience:
- Build advanced math skills.
- Learn suitable programming languages.
- Acquire data skills.
- Develop an online presence.
- Create a relevant experience.
- Develop interpersonal skills.
- Work toward a data scientist job.
- Network. Network. Network.
- Find the right position.
Pursuing a career as a data scientist is a worthwhile endeavor. More companies want a better understanding of their data to cut costs or forecast trends, so data scientists are in high demand. If you work on the items on the list, you will be on your way to becoming a data scientist.
1. Build Advanced Math Skills
When you work with data, you are working with a lot of numbers, so it makes sense that knowing advanced math is very important.
Typically, the list of data scientist math requirements includes calculus, statistics, and linear algebra. However, you need to be most familiar with statistics. For calculus and linear algebra, understanding the principles is adequate.
Most likely, a computer will use linear algebra and calculus when executing the calculations. As long as you understand the fundamentals, you do not have to relearn your high school math classes.
2. Learn a Suitable Programming Language
To clean and analyze data, you need to know some programming. The most popular language is Python, but other common languages include R, Java, and C++.
Make a list of companies where you would like to work, then find out which programming languages they use by researching their job postings on their website or job boards. If you do not have a good understanding of any of these languages, the good news is we collected a list of sites where you can learn programming for free.
Additionally, there we also gathered a list bootcamps that offer job guarantees that will teach what employers are looking for.
3. Acquire Data Skills
Data scientists must be experts at data management, due to the following reasons:
- They analyze data to identify and predict trends.
- They execute “what-if” scenarios and solve problems.
- They collect large amounts of data, perform data cleaning, and organize it for the best normalization using different techniques.
Estimates indicate a data scientist spends at least 80% of the job conducting data cleaning, also called data cleansing or scrubbing, which is crucial for the reliability of the analysis. If the data is not correctly organized, missing data handled, and redundancies and irrelevances mitigated, you will not be able to trust the results.
Data manipulation and data visualization are vital in doing proper analysis. The software they use includes SQL, Tableau, and Power BI.
Structured Query Language (SQL), pronounced “sequel,” is a 4th-generation language developed in the early 1970s by IBM. Data scientists use SQL to communicate with databases to store, retrieve, and manipulate the data it contains.
The data scientists provide analysis or modeling results to executive management and other stakeholders, and an attractive and understandable visualization is necessary.
Tableau and Power BI are used to provide the analysis results to executive management and other stakeholders.
4. Develop an Online Presence
Many employers research a candidate online before deciding to interview them. You want to make sure personal websites, social media profiles, and online portfolios are up to date.
Ensure they only have the information that shows you in the best light.
Update your LinkedIn profile and make sure you do the following:
- Make your profile image look professional.
- Update your past work experience.
- Optimize your summary section with the proper keywords to make it easy for recruiters to find you.
A strong presence online will help you in applying for jobs and getting interviews, which also might result in getting freelance job offers.
5. Create Relevant Experience
Hiring managers determine if you can do the job by reviewing what you did at previous jobs. But showing you have the expertise does not have to come from a typical job. There are other ways to create a work experience.
Internships and Freelancing
Internships and freelance work are things you can add to your resume to show prospective employers you know what you are doing.
Internships are a way to get your foot in the door and can often lead to a full-time job offer. Many of the larger companies have internship programs, so search on social media for current internship openings.
Another way to gain expertise is doing freelance work, and there are many websites where you register for free. Some popular freelance websites include:
- Coding Ninjas
While these are great to see what’s available, you may not want to use them as a full-time gig-finder, as they can command up to 25% or more of your income once you land a job. A better way to find a freelance job online is to either go directly to the hiring website or use job finders like Indeed.
After you create your freelance profile, peruse jobs posted by different clients. When you see a posting that interests you, submit a bid, or apply and make a case for why they should hire you.
Some clients have small budgets, so they may be willing to take a chance on someone without a lot of experience.
Create a Data Science Portfolio
Research to find some real-world situations you find interesting, then use your data scientist knowledge to practice data modeling and data visualization. Select different types of projects to showcase a variety of skills.
When your data science project is complete, tell its story. Provide answers to questions like “Why did you select this project?”, “How did you go about completing it?” and “What did you find from your analysis?”.
Make sure you explain your project summary is easily understood.
Create a website to host your project or use GitHub Pages and include the links on your job applications.
6. Develop Interpersonal Skills
Virtually every employee needs to have good interpersonal skills. Having these skills means you can communicate effectively, have a positive attitude, show empathy, problem solve, and collaborate, to name a few.
If you think interpersonal skills are not that important, let’s talk about why they are beneficial.
Data scientists’ projects involve improving efficiencies, increasing revenue, reducing costs, and enriching the user experience. Therefore, it is very beneficial to understand the business and how it works.
To be a valuable resource, you need to understand its goals and the direction to reach them.
In a sense, the data scientist works for the people requesting the data modeling projects. The requestors are people at various management levels, working in different departments across the company.
Interacting with many different people and departments may not be as simple as one would think. People’s personalities and abilities are vastly different from one another, so interacting with a variety of people is where you need effective communication.
You need to make sure the information you provide is understandable.
If it isn’t, you should not complain or think the other person’s inabilities are causing the issue. Have a positive attitude and show some empathy and make changes to improve the situation.
Communication includes being a good listener. You need to understand the project requirements. If you misunderstand a crucial detail, the work you have done may not have the desired result.
Collaboration often saves time and gets large projects completed quicker and more efficiently. Another benefit of collaborating or working on a team is the many opportunities to learn from coworkers.
7. Work Toward a Data Scientist Job
If you find it difficult to get a data scientist job, try getting a related position first, such as a data analyst position. Like data scientists, data analysts collect and clean datasets. However, knowing a programming language is not required.
Since it does not have as many job requirements, it pays less than a data scientist. So getting in at an entry-level may be more accessible.
Another option is a statistical assistant. They work with statisticians to compile data to use in statistical research. They may interview subject participants and enter their responses.
If you are employed, it might be possible to transition into data science. Talk with your managers and the data science managers, find out if it would be a possibility, and what the requirements are to start the process.
8. Network. Network. Network.
You have probably heard the saying, “It isn’t what you know but who you know.” It means it is not enough to be knowledgeable, as you need to know the right people to get where you want to be.
Networking is essential to finding the right people.
Searching job boards and applying for positions is easy and does not require much time. So you might only get out of it what you put into it. In other words, it may only provide little results.
Employers receive hundreds, maybe thousands, of applications from their job postings. When they receive an application, it usually goes through an applicant tracking system, where it gets scanned and searched for specific keywords.
A computer is probably the one deciding to tag your application for a possible interview.
If you know someone at the company who can put in a good word for you, your chances of getting an interview improves. Build your network to improve the odds of knowing the right person.
Let as many people know you are looking for a job as possible. Contact recruiters directly, as they are known to use LinkedIn to search for candidates. This is another reason to make sure your LinkedIn profile is up to date and accurate, as you can use LinkedIn to find them too.
Talk to your friends, colleagues, and family members and let them know you are looking for a data scientist position. Attend careers fairs and recruiting events to connect with other job seekers and hiring managers, and recruiters.
Set up or attend virtual chats to get to know other data scientists. You might find out about job postings, and it creates an avenue to ask for referrals.
9. Find the Right Position
Applying for every data scientist job posting you find may not be the way to get hired.
Many companies are hiring for this position, and they can be very different. Just like recruiters and hiring managers decide to hire you, you also need to research the company to determine if it would be a good fit for you.
Read job descriptions carefully.
A data scientist may mean different things to different companies, so make sure you have the required qualifications. You should know the difference between a data scientist, data analyst, data engineer, and machine learning engineer.
Do you want to work for a large company or a small company, maybe a start-up?
Larger companies usually have specialized data science teams, and they often provide additional training. And there are always more experienced coworkers to offer opportunities to learn.
More prominent companies, like Facebook, PayPal, and Uber, are likely to have internship programs and will hire newly graduated candidates.
Conversely, smaller employers may provide more autonomy and allow you to be creative. They might be working on the data infrastructure and need your input to determine the best platform requirements.
Before applying, and certainly before accepting a position, you should research the company.
It will not do you any good to get a data scientist position if it turns out not to be what you were expecting.
Resume and Application
When you find the right job, make sure to tailor your resume and cover letter to the job description. Add a summary section to your resume explaining why you are shifting careers, and list the courses you have taken and the programming languages you know.
Describe any projects you have completed and share your online portfolio. Show you understand business problems and can analyze them from the standpoint of a typical business problem.
Your resume should focus on your data science experience, as well as other relevant experiences. For example, interdisciplinary skills are transferable to all jobs, so convey how you used or developed those skills in your previous jobs.
If you want to be a data scientist but do not have prior experience, there are ways you can make it happen. It will take work and dedication, but do not give up. The most important thing is to start. Make a plan and get out there.