Data science is all about turning data into action, and we create more data now than ever before. As a result, more and more companies are looking for data science professionals to analyze their data. According to LinkedIn, the job market for data science specialists grew by 35% this year — both data scientists and data engineers appear high on their list of the top emerging jobs in the U.S.
The supply of data scientists can't keep up with the demand. In other words, it's the perfect time to enter the rapidly-growing job market — and we'll show you how.
In this article, we'll cover everything you need to know to figure out if a career in data science is right for you. Then we'll show you how to get started with courses like our Data Scientist Career Path that teach you all the skills you'll need to enter the job market. Read on for a full overview or use the table of contents below to skip to a specific section.
- What is data science?
- Data science jobs
- Data science languages
- Data science skills
- How to start your career in data science
What is data science?
Data science combines probability, statistics, and machine learning with domain knowledge to generate insights from data. These insights range from predicting outcomes and trends to visualizing relationships and patterns.
Data science affects almost everything we come in contact with from curated playlists to shopping recommendations to disease detection. Data scientists are also at the forefront of creating self-driving cars, chatbots, and our data-driven world.
Want more details on how data science is used? Sophie, one of our Curriculum Developers, gives us an in-depth look at data science's many applications.
Data science jobs
Every time you open an app or log onto a website, you're contributing to the wealth of data that data scientists use to help businesses better serve their customers. In another post, we examine some of the other factors behind the demand for data science jobs.
Turning all this data into actionable insights is no easy task. It requires the collaborative effort of skilled professionals with varied knowledge and expertise. But what's the difference between data analysts vs. data scientists? Data scientists vs. data engineers? Let's find out.
Ultimately, data scientists help organizations collect, organize, and interpret data to achieve their goals. These goals include market research, prediction, generating insights from data, building machine learning models, and more.
Last year, we interviewed Catherine Zhou to find out what a data scientist does. Catherine explains how the many different ways we can use data make it hard to concretely define a data scientist's responsibilities. They usually vary between companies as every organization has its own goals.
The nebulous responsibilities of data scientists leave many feeling confused about their career trajectory. But, once you start your career as a data scientist, you'll find something that you excel at — and that will become your specialty. Possible specializations include (but aren't limited to):
- Machine Learning
- Natural Language Processing
- Computer Vision
- Big Data
- Artificial intelligence
Not every company needs a data scientist's advanced capabilities in machine learning or predictive analysis. For many, a skilled data analyst may better fit their needs.
After data has been collected and organized, data analysts go through it to identify any prevalent trends. Unlike data scientists, who often use advanced algorithms to build models and test hypotheses, data analysts are primarily responsible for finding patterns, irregularities, and issues and communicating the results to stakeholders.
If you want to get started working with data right away without going deep into machine learning, check out our Data Analyst Career Path.
In 2018, we spoke with Ryan Tuck, a data engineer at Warby Parker. When asked about his duties as a data engineer, Ryan explained how he created and maintained "the plumbing required to support the data and reporting needs of the business."
Michelle, one of our Senior Curriculum Developers, elaborates on Ryan’s plumber analogy. She explains that Data Engineers are essential to keeping a company’s data safe. They keep the pipelines clear and free from leaks so that a company’s data is clean, safe, and reliable.
Machine Learning Engineers
Machine learning combines pattern recognition and predictive analysis with computational statistics to teach computers how to identify patterns and predict outcomes. Essentially, by training a computer with data about different things, you can teach it to discern between them.
Machine learning lies at the heart of emerging technologies like facial recognition, gene therapy, and artificial meat. New applications are invented every day as companies vie to find the next hot innovation. The field has become so popular that machine learning engineers have the largest job market in 2021.
The data science industry is still evolving, so you may find job postings whose responsibilities align with those listed above but under a different title. Some of these titles include business intelligence analysts (BI analysts), Data Storyteller, Systems Analysts, NLP Engineers, Data Architects, Deep Learning Specialists and more.
Data science languages
There are hundreds of different programming languages, and many have their own applications in data science. Still, some languages are more prominent than others. Below, we'll walk you through three of the most popular programming languages used by data professionals.
Python is favored among programmers across every discipline for its versatility and readability. Its wide range of powerful libraries and packages allow it to perform the modeling and calculating required for every application of data science.
R is a statistical programming language with data structures, variable types, and tools built specifically for data science, including analysis and visualization. R's base installation can perform functions such as linear regressions and t-tests, and you can use it with RStudio to easily inspect its output.
Programmers use SQL to query and edit the data stored in their databases. SQL is a staple in data science and data analysis, as data professionals use it to extract data from a database before analyzing it with Python or R. It's also incredibly versatile, with its syntax for basic queries being similar to other relational databases like MySQL, PostgreSQL, and SQLite.
In addition to the three listed above, there are other lesser-known programming languages used by data scientists. If you're interested in data science for business or healthcare, you might want to learn SAS. If you're more interested in math or science, Julia or MATLAB might be a better fit.
The languages you'll use are largely determined by your goals as a data science professional. To help you find the best one for you, we've put together this list of data science languages and their many applications.
Data science skills
Knowing how to code is only half the battle. Data scientists also need to collect, organize, and manipulate data, use it to find solutions, and convey their solutions in an easily comprehensible way.
Turning huge amounts of raw data into something useful can be difficult. Before getting started, you'll need to know what problems you can solve using data and what types of data you'll need to solve them.
Data manipulation and analysis
Once you have a question or problem in mind, the next step is collecting and organizing relevant data. Data scientists use tools like SQL or APIs to extract relevant data from larger datasets, then languages like Python or R to explore and visualize it.
This may sound straightforward, but cleaning and preparing data can be very time-consuming. You'll need to keep an eye out for missing data, outliers, errors — anything that might throw off your results.
Many data scientists work closely with non-technical teams. To effectively communicate with managers, executives, and other stakeholders, you'll need to know how to present your findings in terms that are easily understood.
These skills form the foundation of those you'll need for your career in data science, but there are many more. Sophie elaborates on the concepts listed above and more in her list of skills you'll need as a data scientist.
How to start your career in data science
As you can see, data professionals have an expansive list of knowledge and skills. The road towards a data science career is a long one — but we'll show you how to get there.
Step 1: Build your knowledge
Now that you have a basic understanding of data science careers, the next step is getting you there. Codecademy's Curriculum Developers have cultivated a wide range of courses to help you prepare for your career in data science. If you're starting from scratch, our Data Scientist and Data Analyst Career Paths will teach you how to code, along with all the skills you'll need to manipulate data.
If you're already familiar with programming languages and want to learn how to use them for data science, we've got you covered. The Skill Paths linked below will show you how to:
- Analyze Data with Python, SQL, or R
- Analyze financial data with Python
- Visualize data with Python
- Master Statistics with Python
- Get started with Machine Learning
- Apply Natural Language Processing with Python
After you gain the necessary knowledge and skills, it's time to start creating projects and portfolios to help you stand out to prospective employers.
Step 2: Assemble your portfolio
Include data science projects in your portfolio to showcase your skills, which is a must if you don't have any relevant work experience. If you don't know how to build a portfolio, don't worry. We've got you covered.
Both our Data Scientist and Data Analyst Career Paths include tutorials on portfolio building. You'll also create Portfolio Projects using real-world data about medical insurance, GDP, endangered species, and more. Or, you could create projects independently.
Gabriel Guzman, a self-described python enthusiast with a passion for data visualization, breaks the creation of data science projects down into four easy steps:
- Familiarize yourself with your area of interest
- Determine your question
- Find datasets related to your question
- Familiarize yourself with the dataset
Data analyst Albert Lee also gives us tips on building data science projects and portfolios. Before getting started, he recommends reviewing job postings by the companies you want to work for and speaking with data scientists.
Reviewing job postings will help give you a better understanding of the skills you'll need to showcase in your portfolio. Data scientists can provide insight into the current state of the field.
If you don't know any data scientists, reach out to one. There are multiple data science communities online filled with people who love discussing their work and contributing to others' growth.
Immersing yourself in data science communities will help you develop your projects. Looking at other people's projects can help you find new ideas and datasets. And, once you've completed your project, you can share it with data science professionals and receive feedback and suggestions for improvement.
Once you've completed your first data science project, start another. Continue to level up your skills by taking on more sophisticated projects. This will make your portfolio more impressive, and it's also a great way to prepare for technical interviews.
Step 3: Begin your job search
After you've built your skills and assembled your portfolio, it's time to start your job search. Our Skill and Career Paths also include interview practice and tips to help you launch your data science career. We wish you the best of luck on your journey!