How to Become a Data Scientist in 2020

• 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

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 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.

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 while building your data science portfolio
4. Prepare for and start your job search

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 self study.

• Personal projects
• Freelance work
• Pro bono work
• Internships

• 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.

• 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.

• 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.