Every time you interact with a mobile app, use the internet, or visit a website data is generated. Every day large amounts of data are generated from millions of users, and nearly all of this data is valuable.
Understanding the data generated by customers leads to data-driven insights on products and services. As these swaths of data prove to be ever more useful to businesses, the demand for qualified professionals to handle and understand this data will only continue to grow.
Enter data scientists, who earn high salaries deciphering this highly valuable data. It’s no surprise that the demand for data scientists is at an all-time high. Because of this, more and more people are considering how to become a data scientist.
Here, you’ll find all the information you need to decide on whether you want to pursue a career in data science, and what steps are needed to get one. We’ll also give you key facts about careers in data science, such as the expected salaries, and training programs available to you.
A data scientist is someone who works with data to solve a particular problem. They use mathematics, algorithms, and machine learning to create insights from a data set. While some data scientists do work in a traditional academic setting, the data science job title considered here takes place in a business setting.
The data scientist will use all of the information an application, website, or any other source has generated to help an organization understand its users, improve their services, or draw other conclusions.
Data scientists may use statistical analysis, mathematics, and Machine Learning techniques to effectively analyze a set of data and derive the insights they need.
What Does a Data Scientist Do?
Data scientists are responsible for collecting and analyzing data produced by a program or system. They develop custom algorithms and build models to produce valuable insights from trends in this data. Data scientists will then use these insights to solve problems within an organization.
For example, a data scientist could use information about user preferences to recommend products a user may want to buy on an e-commerce site. Data scientists could also take user interaction data from an app to find ways to make the app’s UX more user friendly.
Data scientists will do more than just analyze data. They have to derive insights from a dataset and present these findings to other departments. If a data scientist finds out users do not go ahead with buying products they have put in their basket, they’ll have to figure out why and tell the engineering and design teams so they can improve the user experiences.
Is Data Science Right for Me?
While data science is an attractive career field, like many STEM fields it isn’t for everyone.While anyone can learn data science, here are a few of the traits shared by many data scientists that love their job:
An analytical nature
A love for math or statistics
A desire to learn continually
A knack for explaining complex things simply
A passion for computers or technology
While it’s not essential to have every item on this list, if you have none of them you may not enjoy a career in data science.
Data Scientist Job Outlook
The job outlook for data scientists is strong, as experts expect more companies to hire people who will help them analyze their data. According to the U.S. Bureau of Labor Statistics, job opportunities in data science are expected to increase by 16 percent by 2028, which is much faster than average.
How Much Do Data Scientists Earn?
Data scientists earn high salaries. According to ZipRecruiter, the average salary for a data scientist is over $108,971 Entry-level data scientists usually earn over $95,195, and more experienced data scientists can earn salaries of up to $185,500.
The exact salary you can expect to earn will depend on the company for which you work and the place you live. For example, the average salary for a data scientist in San Francisco is over $138,000 per year, whereas the same data scientist would earn an average of over $113,000 in Indianapolis, Indiana.
Further, your salary will depend on the experience you have in data science. When you’re getting started, you can expect to earn around $69,000, as previously mentioned. And as you get more experience, you’ll be able to unlock higher salaries. It’s important to note that these figures do not include stock options or other perks such as private health insurance offered by some technology companies — you should keep that in mind when you’re searching for a job.
Some data scientists, rather than working in an office, will work as an independent consultant. There are many consulting opportunities available for data scientists, especially at larger companies. These types of jobs can pay thousands for only a few hours of work, which can make this a lucrative career path.
How much can you earn? (source: Glassdoor)
Senior Data Scientist
How to Become a Data Scientist
There are a number of steps to becoming a data scientist. Here are the main steps you’ll need to go through in order to pursue a career in data science.
Choose a data science career path
Learn about data science through a university degree, bootcamp, or self-study
Develop and refine your skills while building your data science portfolio
Prepare for and start your job search
Three Types of Data Scientists
The first step is to consider what kind of work you would like to do as a data scientist. There are many applications for data scientists, from machine learning engineers to enterprise architects. Here are three of the most popular data science career paths:
Data Scientists are people who develop solutions for difficult problems and create programs to analyze data. They’ll use technologies such as SQL, R, Python, Hadoop, MongoDB, Tableau, or Scala.
Data Engineers are responsible for creating the methods used to analyze data at scale. This will involve maintaining databases, creating queries, and creating pipelines to store data.
Data Analysts are often entry-level data scientists who are just starting in their career. They’ll be analyzing data and writing recommendations, but they usually don’t have to create their own technical programs to solve problems.
How to Learn Data Science
There are many paths you can take to learn data science. Here are the most common paths people take when they are getting started in a career in data science:
Pursue a Computer Science degree at a college or university;
Attend a coding bootcamp that offers a course in data science;
Learn data science through self study.
Each path has its own benefits and drawbacks, and many data scientists have used a combination of the above methods.
In the past, pursuing a computer science degree was the main path toward becoming a data scientist. However, over the last few years, another popular option has emerged: coding bootcamps.
Coding bootcamps are short-term, intensive programs designed to help you develop the skills you need to thrive in a specific career in the computer science industry. Instead of attending university for four years and taking out loans, bootcamps are designed to teach you the practical skills you need to thrive in a career in the tech industry in a short period of time. During a bootcamp, you’ll also be given the career support you need to thrive in a career in technology.
Top Skills Needed for Data Science Careers
In order to succeed in a career in data science, you’ll need to practice what you’ve learned while growing your skills. There are very few hiring managers looking for data scientists without any experience, so finding ways to build some is important while at the same time growing your portfolio.
Here’s a list of a few ways to gain experience and grow your portfolio before landing your first job:
Pro bono work
In addition, let’s take a look at some of the data science skills you’ll need to grow during this time to succeed in a data science career, starting with technical skills.
Essential Technical Skills for Data Scientists
There are a couple of technical skills you’ll need to become a successful data scientist. These skills include programming languages, data analysis techniques, and other technical concepts you need to know.
Ability to Prepare Data. As a data scientist, you’ll likely encounter cluttered datasets. You’ll need to know how to prepare data for analysis effectively so you can solve a specific problem. This will involve sourcing, arranging, processing, modelling, and synthesizing data, and amending data into a readable format. They will also have to use data mining to find the right information to solve through a particular problem. Data scientists will often have to read and interpret big data as well — large data sets which could hold deep insights into a particular problem.
Basic Statistics. Data scientists need to have a good understanding of the fundamentals of statistics and statistical analysis. This will involve knowing about distributions, probabilities, A/B statistical testing, and other statistical concepts. This will make it easier for you to analyze datasets and identify relationships between data in a dataset.
Data Wrangling/Munging. Often, data scientists are given datasets in formats they do not expect. In this case, you’ll need to know how to process that data in a way your programs and systems can read. This will include responding to inconsistent formatting, missing values, and other problems in a dataset. You may also have to format big data sets, which are large in nature and require algorithms to sort through.
Data Visualization. Whether you are a junior data scientist or a senior data engineer, you’ll need to know how to visualize data. This skill is particularly important because you’ll need to be able to present your findings to other members of an organization, who would rather see a graph than a dataset. You’ll need to be able to generate a functional visualization based on the information in a particular dataset, and use tools such as d3.js and Tableau to do so.
Programming languages. While data scientists don’t code as much as software developers, using code to build your own algorithms is an important part of the job. While some companies will have specific languages they expect you to know, these are the most common ones you can expect:
Python - Python is the goto language for machine learning and the burning star of data science. While it’s far from the only language used in data science, it will likely be the one you see the most.
R - R is nearly as popular as Python for handling and displaying data, but while Python has other uses R is designed almost exclusively for this task.
SQL - Structured Query Language (SQL) is less of a programming language and more of a series of commands used to handle the storage and manipulation of vast amounts of data.
Java - While Python and R hold the king and queen position among data scientists, Java is also regularly used because of its age and quality libraries. Beyond this, much of the most well used data science software is programmed in Java.
Essential Soft Skills for Data Scientists
To be a successful data scientist, you’ll need more than just technical skills. You’ll need to have a strong set of interpersonal (“soft”) skills. Here are a few of the soft skills you can expect to use in your job.
Communication. As a data scientist, you’ll need to communicate with other data scientists to share your findings. You’ll also need to work with other departments to help them solve their data problems. For example, the marketing department may ask you to analyze data from a campaign, or the development team may ask you to figure out why people are having trouble using a certain webpage.
Business Mindset. Data science is all about solving problems by using data. Thus, you’ll need to know how to process business problems, and use that information to help you craft programs to accomplish a certain goal. You should be able to approach problems as if you were an executive, and present your findings in a way non-technical people will understand if necessary.
Critical Thinking. Data scientists need to be able to use critical thinking skills to evaluate data and find insights in large datasets. You’ll need to be able to think about how to design a solution to a complex problem and think about those problems from different angles and perspectives.
How to Find a Data Science Job
Now that you’ve developed your skills, grown your portfolio, and gained some experience, it’s time to start applying for your first real data science job. Here are a few useful tasks and tips that will help you in landing a job:
Prepare a solid technical resume Your resume is your first impression with most hiring managers, and having a clean resume that well represents your skills is essential.
Clean up your portfolio Your portfolio acts as proof of your data science skills, and a display of the quality of your work. You want to only include the projects you are most proud of, ditching quantity for quality.
Prepare for a technical interview While your portfolio may look good, hiring managers will want to make sure that you’re well rounded in your understanding of data science beyond the projects you’ve completed. A technical interview gives them the chance to test out your knowledge with technical questions.
The good news is that if you’ve chosen to pursue an education in data science through a bootcamp, you won’t have to worry too much about these steps. Most bootcamps include robust career services that include mentorship, interview prep, portfolio help, and resume guidance.
Data Science Overview
Bootcamp certificate, Bachelor's degree, or self learning.
Essential Technical Skills
Data preparation, statistics, data wrangling/munging, data visualization, programming languages.
Essential Soft Skills
Communication, business mindset, critical thinking.
Good news: Career Karma is here to help you along every step of your journey to becoming a data scientist. We’ll provide you with the mentorship and resources you need to break into your dream career in data science. We’ll also introduce you to a group of your peers who can help hold you accountable to your goals.
We can help you get accepted into one of the top data science bootcamps, so you can get all of the training you need to succeed in your new career.
There has never been a better time to pursue a career in data science, and you can get started today!
Mentors, instructors, and peers at your fingertips
Quick-start to a new career
Learn to collaborate with others
Build a strong professional network in technology
Requires motivation and hard work
Fast-paced learning style
Staying up to date with evolving web technologies
Apply to Data Scientist Bootcamps
Whether you’ve decided you’re ready to apply for a data scientist bootcamp or you still aren’t sure which coding program you want to attend, Career Karma can help. Our mentors are here to not only help you find the perfect coding bootcamp for you, but we will also help you every step of the way from the application process to supporting you with any questions or hiccups you run into while interviewing with multiple bootcamps.
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Download the Career Karma to start learning how to code and meet other students preparing for coding bootcamps.
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