How to Master Data Science Without a Degree in 2020

Data scientist has been hailed as the "sexiest job of the 21st century." With an average salary of over $120,000 in the U.S., it‘s no wonder that so many are flocking to this burgeoning field.

However, there‘s a common misconception that to break into data science, you need at least a master‘s or Ph.D. in a quantitative discipline. The truth is, while having an advanced degree can certainly help, it‘s not a hard requirement to launch a successful career in data science.

In this post, we‘ll lay out a roadmap for becoming a data scientist without pursuing another degree. With the right combination of self-education, projects, networking, and persistence, you can absolutely achieve your goal of working in this exciting field.

Acquire the Necessary Skills and Knowledge

The first step is to learn the core skills that every data scientist needs in their toolkit:

Programming: Python and R have become the de facto programming languages in the data science community. Aim to gain a working knowledge of at least one of these languages, including libraries like NumPy, pandas, matplotlib for Python or dplyr, ggplot2, caret for R. SQL is also a must for working with relational databases.

Math and Statistics: Data science is fundamentally about deriving insights from data, so you‘ll need a solid grasp of concepts like probability, statistics, linear algebra, and calculus. You don‘t need a super advanced math background, but taking some online courses or reading introductory textbooks will help you understand the underlying theory.

Machine Learning: Machine learning is a key component of data science, enabling you to build predictive models to solve business problems. Some of the most important algorithms and techniques to learn include linear and logistic regression, decision trees, random forests, support vector machines, K-means clustering, and neural networks. Start with a course like Andrew Ng‘s Machine Learning on Coursera.

Data Wrangling and Visualization: Raw data is messy. Data scientists spend a lot of their time cleaning, transforming, and visualizing data to make it suitable for analysis. Tools like Excel, SQL, Tableau, and Python libraries like NumPy, pandas, and matplotlib are commonly used for these tasks. Practicing "data munging" on real-world datasets is the best way to hone these skills.

Some of my favorite resources for learning data science skills include:

• DataCamp and Dataquest for interactive coding courses
• Coursera and EdX for online courses from top universities
• Kaggle for hands-on data science competitions and tutorials
• Fast.ai for deep learning
• "Python for Data Analysis" and "Hands-On Machine Learning with Scikit-Learn and TensorFlow" books

The key is to not just passively consume content but to practice implementing your skills on real datasets. Kaggle and the UCI Machine Learning Repository are great places to find datasets to work with.

Gain Practical Experience Through Projects

Learning concepts and theory is important, but the best way to cement your knowledge and prove to employers that you can do the job is through hands-on projects. Hiring managers want to see what you‘ve actually built – a GitHub full of data science projects is more impressive than a generic resume.

Some ways to gain practical data science experience:

Competitions: Kaggle runs data science competitions where you can compete against other data scientists to solve real-world problems. Having a few Kaggle competition entries is a great way to demonstrate your skills, and some Kagglers have even been directly hired by companies like Google and Microsoft.

Independent Projects: Come up with your own data science projects to work on from end-to-end. This could be analyzing your own data, scraping data from the web, or building an interactive data visualization. Document your project and share it on GitHub – have a few impressive projects you can discuss in-depth with employers.

Open Source: Contributing to open source data science projects and libraries is a fantastic way to collaborate with other data scientists and build up your portfolio. Some beginner-friendly places to contribute include scikit-learn, NumPy, pandas, and matplotlib.

Hackathons and Datathons: Participating in in-person or online data science hackathons and datathons lets you team up with others to tackle a data science problem under time pressure. It‘s a great way to learn, network, and experience what working as a data scientist is like.

Internships: If you can dedicate a few months to full-time work, pursuing a data science internship is an excellent way to gain real-world experience. Many companies have internship programs specifically geared towards aspiring data scientists. You‘ll get to work on a substantial project and learn industry best practices from a data science team.

In summary, doing lots of hands-on projects is essential for cementing your data science knowledge and demonstrating your abilities to potential employers. Make a project plan, jump in and start building, and be sure to showcase your best work prominently online.

Develop Your Professional Brand and Network

Landing a data science job is not just about having the right skills – it‘s also about making the right connections and selling yourself effectively to employers. Establishing a strong professional brand and growing your network within the data science community will open up many more opportunities.

Here are some ideas for raising your profile as an aspiring data scientist:

Blog about data science: Start a data science blog to share tutorials, project walkthroughs, or industry insights. Blogging is a great way to reinforce your own knowledge while demonstrating expertise and passion to employers. Promote your posts on social media and engage with readers who comment.

Speak at meetups or conferences: Look for data science meetups in your local area to attend and consider applying to give a short talk on a project you‘ve worked on. Speaking at conferences is also a great way to share knowledge and meet other data scientists. Start small with local events and work your way up to larger conferences.

Be active on social media: Twitter, LinkedIn, and Medium are great places to connect with and learn from other data scientists. Share interesting articles you‘ve read, comment on others‘ posts, and show that you‘re engaged with the latest industry trends. Having a professional social media presence can lead to opportunities finding you.

Attend hackathons and workshops: Participating in data science events is a great way to immerse yourself in the community and learn from others. Treat every event as an opportunity to make new connections – follow up with the people you meet, connect on LinkedIn, and nurture relationships. You never know what opportunities may arise from your network.

Reach out for coffee chats: Identify data scientists you admire and politely ask if you can buy them coffee to pick their brain about the industry. Come prepared with thoughtful questions and respect their time. Building genuine relationships with industry veterans can lead to mentorship, referrals, and even job opportunities down the line.

Provide value and build goodwill: Look for ways to help others in the data science community without expecting anything in return. Share useful resources, make introductions, volunteer at events – be a giver. Building a reputation as a helpful, knowledgeable, and trustworthy person will make others more likely to think of you when job opportunities come up.

In short, building a strong professional brand and network requires ongoing effort but it‘s well worth it to uncover hidden job opportunities and accelerate your data science career. Focus on raising your visibility, providing value to others, and forming genuine connections.

Consider Alternative Education Options

While we‘ve emphasized that a formal degree isn‘t necessary to break into data science, that doesn‘t mean you can‘t pursue other structured educational paths besides a master‘s program. Let‘s look at some of the alternatives:

MOOCs (Massive Online Open Courses): Many top universities like Stanford, MIT, and Johns Hopkins now offer their data science courses online for free or at low cost. Spending a few months systematically working through a series of online courses can provide an excellent foundation. Some popular data science MOOC providers include Coursera, EdX, Udemy, and DataCamp.

Bootcamps: For a more immersive educational experience, consider enrolling in a data science bootcamp. These short-term intensive programs are designed to make you job-ready through hands-on projects and career coaching. Expect to spend 3-6 months studying full-time and be prepared to pay up to $16,000 in tuition. Well-known data science bootcamps include Metis, Insight, and NYC Data Science Academy.

Online Master‘s Degrees: For those who want the depth of a graduate degree without relocating, online master‘s programs in data science are now available from many universities. These programs allow you to earn the same degree as on-campus students but with the flexibility to complete coursework on your own schedule. Expect to spend 1-3 years and $10,000-$50,000 in tuition.

Each of these options has pros and cons in terms of cost, time commitment, and learning format. MOOCs offer low cost and flexibility but require more self-discipline. Bootcamps provide more structure and career support but are expensive. Online degrees are a bigger investment but offer more depth.

Ultimately there is no "right path" – you‘ll have to choose the option that best fits your learning style, budget, and goals. Many successful data scientists are self-taught, while others have found that a more structured program accelerated their learning. With hard work and dedication, you can succeed with any of these paths.

Landing Your First Data Science Job

You‘ve armed yourself with the necessary skills and project experience – now it‘s time to pursue your first data science job. Here are some tips for positioning yourself effectively in the job market:

Tailor your online presence: Update your resume, LinkedIn, GitHub, and portfolio to highlight your most impressive data science projects and skills. Have a compelling personal summary that conveys your passion for data science and the unique value you bring. Get feedback from other data scientists and hiring managers to optimize how you‘re selling yourself.

Practice interviewing: Data science interviews typically involve a mix of technical questions, case studies, and discussing past projects. Brush up on key data science concepts, practice coding on a whiteboard, and review business case studies. Have thoughtful questions prepared to ask the interviewer. Mock interviewing with a peer can help you get comfortable and identify areas to improve.

Emphasize passion and curiosity: Employers love candidates who are genuinely excited about data science and eager to continuously learn. Convey this passion in your cover letters by sharing how you‘ve gone above and beyond in your data science journey and some of the side projects you‘ve tackled out of curiosity. Always be ready to discuss the latest data science concepts you‘ve been learning about.

Be open to related roles: If you‘re not having luck landing a data scientist job right away, consider adjacent roles like data analyst, business intelligence analyst, or data engineer. These positions will still allow you to build relevant skills and often have lower barriers to entry. Many data scientists started off in these roles and worked their way up.

Keep building and networking: The job search can take time so it‘s important to stay focused and keep making progress. Continue expanding your portfolio with new projects, engage in the data science community, and set up coffee chats to grow your network. With persistence and patience, the right opportunity will come.

Rejection is par for the course in any job search, so don‘t get discouraged if you face some "no‘s" along the way. Treat every interview as a learning opportunity, seek feedback, and keep pressing forward. If you‘ve put in the hard work to gain the necessary skills, you will find the right data science role for you.

Never Stop Learning

Becoming a data scientist is not just a one-time goal post to reach – it‘s a commitment to continuous learning and growth. Data science is a constantly evolving field with new techniques, tools, and best practices emerging all the time. To stay relevant and advance in your career, you‘ll need to keep your skills sharp and stay on top of industry trends.

Some ideas for continuing your data science education on the job:

• Take on increasingly complex projects at work that stretch your skills
• Attend industry conferences like Strata, KDD, and NeurIPS to learn from leaders
• Pursue certifications like AWS Certified Machine Learning or Deep Learning Specialization
• Participate in data science competitions like Kaggle or DrivenData
• Read research papers on arXiv and stay up to date with data science blogs
• Contribute to open source projects and share your own projects
• Mentor more junior data scientists and learn from teaching others
• Consider specializing in an emerging area like Natural Language Processing or Computer Vision

The most successful data scientists are committed to being lifelong learners. They‘re not afraid to tackle new challenges, admit when they don‘t know something, and continuously expand their knowledge. Embrace this growth mindset and you‘ll go far.

You Can Do This!

Breaking into data science without a traditional degree may seem intimidating, but it is absolutely possible with the right approach. By educating yourself, building impressive projects, making the right connections, and selling your skills effectively, you can launch a successful data science career.

It won‘t necessarily be easy – you‘ll need to put in a lot of hard work, face rejections, and push yourself outside your comfort zone. But if you stay focused on your goal and adopt a growth mindset, you will get there.

Countless others have made this journey before you, going from complete beginners to contributing data scientists in the industry. You can do it too. Keep educating yourself a little bit each day, maintain that curiosity and passion for playing with data, and constantly create new opportunities for yourself.

We would love to hear from readers who have successfully broken into data science without a degree. What strategies worked for you? What advice would you give to other aspiring data scientists? Let us know in the comments!