How to Become a Data Scientist

infografic

Every time you interact with a mobile app, use the internet, or visit a website, data is generated. It’s no surprise that demand for data scientists who can handle this data is at an all-time high.
Data scientists are responsible for going through all of the data people have created and figuring out what it tells an organization about their products or services. The data scientist will collect and process data, then use those insights to help a business improve its offerings.
Insights produced by a data scientist could tell a retail store which products to stock, or could be used to recommend products to an existing customer. The data scientist turns massive lists of data into actionable recommendations which will help a business or organization achieve their goals.
But it can be difficult to break into a new career in data science. Here, you’ll find all the information you need to decide on whether you want to pursue a career in data science. We’ll also give you key facts about careers in data science, such as the expected salaries, and training programs available to you.

What is a Data Scientist?

A data scientist is someone who gathers and processes data to solve a particular problem, and will use mathematics to create insights from a data set. The data scientist will use all of the information an application or website has generated to help an organization understand its users, and improve their services. Data scientists are employed by both companies to help them solve problems and by governments to make use of all of the data they collect.
Data scientists may use statistical analysis, mathematics, and Machine Learning techniques to effectively analyze a set of data and derive the insights they need.

Three Types of Data Scientists

There are three main types of data scientist you can become:
Data Scientist

Data Scientist

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 Engineer

Data Engineer

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 Analyst

Data Analyst

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.

Data Scientist Job Description - What Does a Data Scientist Do?

Data scientists are responsible for collecting and analyzing data produced by a program or system. They will develop custom programs and algorithms to accomplish this goal. Data scientists will then use this information to solve problems within an organization by applying techniques from statistical analysis and mathematics.
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 can be used to provide personalized experiences to users, thereby improving their overall satisfaction with a particular product or service.
Data scientists will do more than just analyze data. They’ll have to think about what insights can be found in a particular dataset. And they’ll need to present their findings to other departments as well. 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.

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.

Data Scientist Salaries - 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)

$108,971
Senior Data Scientist
$95,195
Data Scientist

How Can You Become a Data Scientist?

There are many paths you can take if you are interested in becoming a data scientist. Here are the most common paths people take when they are getting started in a career in data science:
  1. Pursue a Computer Science degree at a college or university;
  2. Attend a coding bootcamp that offers a course in data science;
  3. Learn data science through your own studying.
Each path has its own benefits and drawbacks. 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, you can instead attend a coding bootcamp where you’ll learn what you need to know to become a data scientist in only a few months.

How to Learn Data Science

There is no one single way to learn about data science. Some data scientists go to university and pursue a degree in computer science degree. Others, however, are self-taught and do not have any formal education in computer science. This path is often best for people who either cannot afford to attend a formal program, or who already have technical experience and are looking to learn about a new technology.
Coding bootcamps are a popular alternative to university as well. These programs are designed to teach you the practical skills you need to thrive in a career in the tech industry. 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, there are a few skills you’ll need to develop and refine. Let’s start with the technical skills you need to succeed in a data science career.

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.
  • Ability to Write Code. Data scientists do not code as much as software developers — many problems are solved using tools such as Tableau. However, you’ll still need to be able to code. While some companies will have specific languages they expect you to know, the most common ones you can expect will include Python, R, SQL, and 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 Become a Data Scientist: Step-by-Step

In our full data science career guide, we’ll discuss these steps in more depth. Here are the main steps you’ll need to go through in order to pursue a career in data science.
  1. Choose a data science career path
  2. Learn about data science through a university degree, bootcamp, or self-study
  3. Develop and refine your skills
  4. Build your data science portfolio
  5. Write a technical resume
  6. Start your job search
  7. Prepare for a technical interview
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’ll also 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!
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Disadvantages

  • Requires motivation and hard work
  • Fast-paced learning style
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