Even though data science is one of this century’s hottest fields, how to break into the field is still a little confusing, especially to those who don’t have technical backgrounds.
Truth is, data science is a popular and emerging field, but depending on the company, a company's leadership may not truly know what a “data scientist” does either. And as the field defines itself with more and more opportunities, it’ll be no time at all before data scientists are in even higher demand than they currently are.
Data Science, Machine Learning & AI
If you have some technical interest and love numbers, then looking into a career in data science is the right move. In fact, LinkedIn says that it's grown as a field by more than 650% since 2012. That’s incredible!
The field is growing in scope, too—data science is forging the path forward for machine learning and AI. Programming those capabilities takes a smart and capable data scientist. The closely-related machine learning engineer has also seen phenomenal growth according to that same report.
What’s Driving All This Interest in Data Science?
In the last decade or two, organizations have discovered more data than ever before. In fact, The Economist likens it to the oil boom of the last century. Our online lives, interactions, work habits and more are creating a rich pool of information that companies have never been able to tap into before.
New patterns, systems, and routines are emerging from all fields and walks of life. But what do you do with this information? What insight can be gleaned from it? That’s where data science comes in.
What is a Data Scientist, Anyway?
A data scientist explores new possibilities and makes assumptions based on data, much like the scientist that you have in your mind from your younger years. But instead of using a laboratory and running tests on rats, data science tests involve lots of data, algorithms, and mathematical equations to run models and hypotheticals to glean new insights from data. They’re looking for new meaning and patterns that could be hidden within the data.
Data scientists are set apart from data analysts because they use advanced algorithms. Setting up these algorithms, such as regression or machine learning algorithms, is where the advanced knowledge comes in. Very often, a data scientist will create these algorithmic rules and then the data analyst will look over the output to find any patterns, irregularities or issues that need to be resolved.
How do you get there? How do you build your skills and gain the right experience to make your data scientist or data analyst position a reality?
What programming languages do you need to know as a data scientist?
As a data scientist, you’ll be tasked with making sense of big data—that’s lots raw numbers and information. To sort through it all, you’ll need to know certain programming and querying languages:
Your goal as a data scientist is to bring order to chaos; structure to unstructured data. To start with programming and data science, you’ll want to know some Python. As one of the most popular programming languages in the world, Python is a key player in web and application development. It’s also an important language to know for developing the scripts that machine learning depends on.
What’s also fueling its popularity is its simplicity. New programmers can learn it quickly, so you’ll have a solid, jack-of-all-trades programming language under your belt that you can then apply to data science or another programming field. Developers and data scientists love Python because of its versatility and control.
Oftentimes, data scientists will be asked questions and then need to find particular answers. Having SQL as part of your skillset allows you to do that quickly. SQL is especially useful for finding data in tables within a relational database management system. These files with tables of data can be linked together into the same fields. Then with SQL, you find and update the data and create new schema if needed. SQL takes normal spreadsheets to another level, because SQL queries can filter through millions of cells.
With Matplotlib, you can use Python to create and display charts and images. Matplotlib includes bar plots, scatter charts and more, all built and customized with the Python programming language.
Data Science Job Experience: How (and Where) to Get It
Before anyone hands you the keys to their data science empire, you’ll need a little practice. How this happens depends on you and the company. You’re well on your way with a great background from Codecademy, but what practical experience will help?
1. Your own side project
For any company to take you seriously without any relevant work experience, you’ll have to show them what you can do. This often means a side project. Codecademy Pro offers projects within courses, and many of these can later be developed into a fully-fledged side project. You can use these examples to show potential employers your skills and accomplishments.
2. Look for contract opportunities or internships
This should be with a company that has a need for data scientists or data analysts, because the goal is to you could slide right into a full-time position once your internship is finished.
Of course, this will depend on where you live and what types of companies are near you. Emerging fields for data scientists include financial tech, healthcare, biotech and manufacturing. But the list of industries that need data scientists is quickly growing.
If internships aren’t the right fit for you, seek ways to help clients through freelance or contract work. Once you help a client out, they’re more likely to recommend you to others for contract work, or they will be a great reference for your next position.
3. Apply to Full-Time Positions
If you’re brand new to the field, it’ll be hard to carve your own path. If you want to test the job market, aim for mid-sized companies that may already have a few data scientists on board and need some additional project help.
Ideally, you’d want team members that can help you navigate the waters and may even lend a helping hand. That way you can learn the ropes and chip in where needed.
Conclusion: Emphasize Your Traits
Thought data science roles are in great demand, you won’t just get handed any job that you apply to. All things being equal, you’ll also need to emphasize the traits that set you apart.
For data scientists, this includes curiosity, an interest in developing your technical skills further, and an intuitive statistical sense. Great data scientists combine these traits to sniff out important trends, outlooks, and insights from their data sets.