HOW TO BECOME
a Data Scientist
We generate a lot of data. From our collection of facebook likes, the photos on our digital cameras, and every email we send, to measuring carbon emissions and stock trends. It is estimated that at least 2.5 quintillion bytes (or exabytes) of data is produced every day.
We are in constant use of that data as well. As we generate more and more raw data and find more and more applications for that data we need a greater number of people to wrangle big data.
Equal parts mathematician, computer scientist, trend analyst, and problem solving expert–data scientists are in high demand today. Data science isn’t just in need at Google either, from Walmart to political campaigns understanding the mass of raw data we produce has become essential to modern businesses.
So what are some of the upsides of a career in data science? Check out these statistics:
- According to the Bureau of Labor Statistics, data science data science and similar fields are projected to grow by 16% over the next 10 years, with many new jobs added regularly.
- Data scientists in the United States earned a median income of $108,224 in 2019.
- There will be a predicted 50% gap in the supply vs demand for data scientists in the coming years.
- Experts predict that 40 zettabytes of data will be in existence by 2020.
What Is Data Science?
Data science is a high-tech career path that focuses on managing data and databases. In the modern information age, data is everything–lots of individual pieces of information drive the systems we rely on today.
Data science figures out what to do with all of this information, how to store it, how to interpret it, and convert it from raw data into usable information.
To accomplish these tasks, data scientists use databases and other tools to organize and interpret data. Databases are like digital filing cabinets, complete with sorting systems and other vital features. Databases are used for everything from keeping tabs on inventory at a lumber yard to helping you find relevant information when you search for something on Google.
Data mining and other tasks are also a common element of data science, as companies constantly look for ways to improve advertising and products. Data is the main driver of many parts of the technology industry, and it’s hugely important to the modern economy. Data science is the profession that manages all of these things, and constantly improves and creates new ways to work with data more efficiently.
What Do Data Scientists Do?
Data scientists create and manage databases. Additionally, these highly trained professionals use coding languages such as Python to create software programs. Data scientists also manipulate large swaths of otherwise impossibly mundane and (seemingly) useless data into useful information, which companies and agencies can use to improve their products or analyze trends.
Data interpretation is an important part of a data scientist’s job. Companies often hire data scientists to help them streamline their databases, or transition from other forms of data storage. Additionally, machine learning and artificial intelligence are a growing responsibility for data scientists, especially in high-tech fields.
Data Science Job Description
Due to the complexity and variety found in the field, data scientists do not always have a clear job description. For example, data scientists often perform the work of data analysts, in addition to other duties–but they aren’t data analysts.
- Gathering high quantities of big data and turning it into a more usable format.
- Understanding and utilizing statistics, along with statistical tests and distributions.
- Handling business-related problems with data-driven techniques.
- Using, understanding, and creating modern data analysis techniques like machine learning, deep learning and text analytics.
- Communicating and collaborating with both IT and business.
- Finding order and patterns in a confetti stream of data and spotting trends that are useful in a business sense.
- Using predictive models and to generate insight.
As you can see, the job responsibilities for data scientists vary widely between companies and industries. Data scientist responsibilities can vary within a company as well. Data science is a highly specialized career path, and vitally important to scores of industries.
Now, let’s take a look at the different types of careers found under data science:
A data analyst is close to a data scientist, but they don’t handle big data. This may seem like a small distinction, but it’s like the difference between an auto engineer and an aeronautical engineer. They use a lot of the same methods and ideas, but not all of those methods and ideas translate between the two fields. A data analyst requires less education and experience over a data scientist.
A data engineer is like an evolved data scientist. They typically need considerable experience as a data scientist, and they develop the code, software, tools, and infrastructure that data analysts and data scientists use every day. They need to be talented programmers especially, with good knowledge of and ability with APIs.
What Are the Required Skills for Data Science Careers?
Data scientists must be well-trained in many subjects, including math and coding. And while programming is an important part of the job, data scientists need to know more than just a few coding languages to work in the field. Here are a few of the most important skills data scientists need to adopt:
High-Level Programming Language Knowledge
Programming is a big part of data science. To work in the field, prospective data scientists must master a variety of coding languages.
Machine learning is similar to AI and utilizes a lot of the same skills. Data scientists are often expected to work on (or work with) machine learning systems. As a result, an understanding of machine learning is a skill data scientists should aim to develop.
While data scientists don’t need to be a college-educated statistician to do the job, an in-depth understanding of statistics is essential to most data science positions.
Data is all about the details, small and large. As a result, a person who trains themselves to be detail-oriented can excel in the industry.
Artificial intelligence, or AI, can organize and sort data at an alarmingly fast rate, with excellent accuracy as well. Because of this, AI is increasingly popular, and data scientists are at the forefront of its use and development.
Data scientists are a part of a larger team, as they don’t work within a vacuum. Data scientists need to learn to work with other engineers (who might not understand what data science is) and work on a team towards a mutual goal.
How Much Do Data
Data science, like many careers in the tech industry, has the potential to be very lucrative. Data scientists themselves earn more than many other tech workers, even with the same level of education or experience. According to Glassdoor, the average salary for a data scientist in the U.S. is $108,224.
However, salaries can vary widely between companies and regions. In the table below, we’ve highlighted the average salaries for data scientists in 15 major metropolitan areas.
|San Francisco, CA||$131,964|
|Los Angeles, CA||$110,896|
|New York, NY||$106,784|
But your salary as a data scientist will also depend greatly on the particular field you’re working in, as well as your experience level.
Data Science Coding Languages
Python is a powerful and popular general-purpose programming language. The massive community surrounding the language has developed so many useful and powerful tools for analyzing data, machine learning, and artificial intelligence.
Apache Hadoop isn’t a programming language, it’s a collection of open-source code and procedures designed for use with the storage of big data. It isn’t practical to fit it all that data one hard drive so hadoop helps by storing data on multiple.
R programming language is built by scientists for scientists. It isn’t as efficient or as easily applicable as Python is for one reason. It isn’t built to do anything other than analyze data.
SQL performs a similar function as Hive does in big data but uses a little different process to index the data. SQL is typically used for normal database handling alone, but for the volume required in big data it still needs to sit atop Hadoop to be effective.
While Python and R are great for specific, single machine applications, when you need to roll out analytical software that will function anywhere Java and its JVM cousins are the answer. Java can be used to to do almost anything, anywhere, so it is popular among data scientists for its lack of limitations and scalability.
How Can You Become a Data
If managing databases, coding with SQL, and interporating large data sets sounds like a career you’d like to pursue, continue reading. Data science jobs are everywhere, and there’s a notable shortage of qualified candidates. That means now is a great time to get into the industry.
However, data scientists typically must have some sort of formal education–not as a requirement of all these positions, but to learn the complex job. Here are the most popular data science education options.
Data Science Learning Paths
Data Science Bootcamps
Data Science bootcamps are fast, intensive career education programs designed to teach students marketable skills for a very specialized field. Data Science bootcamps compress all of the fundamentals in a shorter, more intense learning experience. They teach you practical concepts before helping you jump directly into the workforce to generate real-world experience.
Traditionally, data scientists receive their training in college. Computer science graduates are commonly found in the field. Colleges and universities have some upsides, such as prestige, career mobility, and meeting the requirements set by some employers. However, many employers don’t require college degrees automatically, and university tuition is notoriously expensive.
Despite how complex data science may seem, it is possible to learn the core concepts of data analysis and big data like Bayesian thinking on your own using free online resources. However, without a background in math, statistics, and coding, this approach will not be easy. Using this approach also generally takes much longer and leaves you prone to significant gaps in your knowledge and skills. Further, you also miss out on much of the hands-on experience, community support, and mentorship that you have access to with a data science bootcamp.
What Companies Are Hiring Data Scientists?
Data scientists work in a wide variety of industries. The need for competent, safe, and up-to-date data management programs and personnel span between the financial sector all the way to manufacturing and retail. However, the center for data science opportunity lies in the tech industry. Here are four major tech giants who employ data scientists, along with some average self-reported salaries.
What does a data scientist do?
A data scientist’s job is to manage and create databases. This is typically done through Python and various tools.
Is data science a good career?
Yes, data is a major aspect of many companies, and having someone collect, compile, and create a system for it is vital. Data science is a high-level tech position with lucrative pay.
What skills are needed to become a data scientist?
Data scientists must know programming. They also need skills in statistics, machine learning, and artificial intelligence.
How much does a data scientist make per year?
Data scientist salaries vary. A data scientist can earn anywhere from $93,206 to upwards of $131,964 per year. $108,224 is the average salary for a data scientist in the U.S.