How to Become a Data Scientist
As the number of data science career opportunities continue to grow, many universities are launching undergraduate and graduate programs within the field. To stand out from the rest of the pack, a Bachelor's or Master's degree is recommended directly within data science in order to receive fundamental education such as programming, linear algebra, probability, and machine learning. Other disciplines can include computer science, mathematics, or statistics.
Since the category of data science can be very broad, there are many variations of programs available. For example, Georgia Institute of Technology offers an online Master of Science in Analytics with concentrations in Analytical Tools, Computational Analytics, and Business Analytics. Grand Canyon University offers another online program, a Master of Science in Health Informatics, that has a focus on healthcare data management systems. Colorado Technical University provides a Master of Science in Computer Science with an emphasis in Data Science.
There are Bachelor's degrees in Data Science, Data Analytics, and Computer Science to satisfy undergraduates and they are typically completed in four years. Master's degrees are geared toward working professionals to advance their career, and online programs can provide flexibility for those to complete coursework at their own pace while meeting assignment deadlines. These can be completed within a couple years for students that can commit to a full-time schedule, but part-time students will generally take at least three years.
Software in Data Science
Python and R
These are the two most popular programming tools within data science. Python is a simplified, general purpose programming language that has had three iterations since its release in 1991, and is both open source and compatible with a number of operating systems (Windows, Mac, Linux, and more). Coding within the program is object-oriented and can be used for any level of programming project. It is popular within the data science field due to its easy readability and useful libraries, such as Numpy, Pandas, and Scikit-learn.
R is another open-source programming language that is more geared toward data analysis. It is written in C and FORTRAN, and was developed just a few years after Python in 1993. While it has an initial steep learning curve, there are thousands of libraries to work with, such as time series analysis, data mining, and observing statistical issues. RStudio is the most common integrated development environment to use for R programming, having tools for editing and graph visualizations.
Both programming methods tend to be interchanged, or both required in the data science community. There are a number of similarities, as they both create algorithms to manipulate data, can be used to tap into a variety of data sets, open source software, and it is fairly simple to become experienced with either product. While both are fundamentally the same, how they operate is what makes them difference. In recent years, due to its better machine learning capabilities and easy syntax, Python has become the more dominant language.
SQL, pronounced "sequel" and abbreviated for Structured Query Language, is the process of accessing and manipulating databases with statements, most commonly in Relational Database Management Systems, or RDBMS. This is how analysts can query, insert, update, and remove information from a database. This process is also how to create and structure tables and indexes within the database. Within data science, this is one of the most commonly used methods for querying data, whether it be for universities or corporations.
Apache Hadoop is another open source software with a goal of making extensive, large data sets easier to manage. When relational databases are not enough, such as what to recommend customers through a complicated algorithm, Hadoop has the ability to spread information along many servers and processors. The decentralized nature of the program also keeps the information running smoothly when a server gets kicked offline. The development of Hadoop began with Google when they were collecting data for an open source project called Nutch.
The framework within Hadoop is separated into the following segments: Common, HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator), and MapReduce. These sectors have the ability to contain information, distribute it, and scale it upwards when needed. Hadoop has the ability to work in business datacenters and along the cloud.
Provides a series of software products that aid in data visualization for business intelligence that was created back in 2003 and has been acquired by Salesforce. The goal is to provide a number of options for anyone to present information in any visual form, be it interactive charts and maps on websites, blogs, social media, and other platforms. Products offer a variety of ways to accomplish data visualization tasks, such as the Desktop format for business or personal use, an Online format that can be completed through the cloud, and a Public format that provides powerful tools for free.
SAS, or Statistical Analysis System, is another software package that is geared toward a large number of data science subjects, such as business intelligence, data management, and predictive methods. There are over 200 different segments of SAS that can accomplish numerous tasks, such as mining data, quality control, and creating visualization, The software was developed at North Carolina State University back in 1976, which provided one of the first educational programs to achieve a degree in analytics.
Certifications and Bootcamps
Data science certificates are a good way to demonstrate that professionals have the skills necessary to pursue a different career path without the time commitment toward a full degree. Many are offered online or in a hybrid format of on-campus lectures and online coursework to allow for maximum flexibility for those that want to continue working while obtaining a certificate. Instead of moving to an institution's location, students can simply view lectures and complete assignments and projects from the convenience of their own home. Typically, course credit from certificates can be applied toward a Master's degree in the future.
One example of professional certification offered through a university is the HarvardX Data Science program. Through the edX platform, students can take courses such as Probability, Inference and Modeling, Wrangling, and Visualization, each taking eight weeks to complete, and are offered fully online at no cost to enter, though there is a cost for advanced features and to receive actual credit. Other universities that have teamed up with edX include Georgia Tech and the University of California-San Diego.
Every organization, like Amazon, Cloudera, and Microsoft, provide resources and testing opportunities online to complete a certificate. These vendor-based certifications are ideal for companies that are looking for experience within specific tool sets. As an example, Microsoft has a Certified Azure Data Scientist Associate program, which tests students for their ability in using machine learning with the Azure software to analyze and develop models. While educational material is generally offered by the company and free without an instructor, it can cost hundreds of dollars to take each test.
Bootcamps in the field of data science are rigorous multi-week courses that train people in specific subject matter. They differ from traditional higher education, like obtaining a degree in computer science, in terms of having a shorter time commitment and being cost effective. Typically, bootcamps will last around 12 weeks or less on each topic and at a fraction of the cost it takes in a Master's degree program. Some programs even have a guarantee of job employment at the completion of the program, and those that finish early will pay less tuition.
In many ways, attending a bootcamp is similar to obtaining a certification. It is a quick way to receive credentials when making a career change or moving up to a better opportunity. However, professionals will need to make the short-term time commitment. Many bootcamps require a significant number of hours, sometimes reaching the amount of 8 to 10 hours each day when pursuing the really short courses.
One of the most popular bootcamps is the online-exclusive Springboard. Career focuses they offer include Data Science, Machine Learning Engineering, Data Analytics, UI/UX Design, and Digital Marketing. All but the final option has a job guarantee where they will be hired within six months of graduation or they will get their money back. Graduates will work with career coaches who will aid them in scoring an opportunity, but it may not necessarily be in the exact field.
Entry Level Jobs
When it comes to entry-level positions in the field of data science, there are similar career opportunities in data, business, and marketing analysis. These positions will require at least a Bachelor’s degree with coursework in statistics, analytics, and basic programming. Proficiency in software such as R, Python, SQL, and Microsoft Excel are common. For those that do not have a degree, certification from a university or a vendor can showcase the skills obtained in analyzing data. Most importantly, experience needs to be shown in the form of internships and creating a portfolio that provides example projects to prove that the candidate is able to accomplish the tasks that the organization needs completed.
For those specifically looking at entry-level data science jobs, these will require a bit more education and further experience with software tools such as Tableau and SAS. While a Bachelor’s degree can satisfy some job requirements, a Master’s degree is recommended due to further skills needed in working with data and it will highlight candidates from others in the pool.. The more duties a job lists, such as cleaning data, developing predictive models, and creating data visualizations, the more likely it is the job opportunity will require higher education.
According to the Bureau of Labor Statistics, jobs within the sector of computer and information research scientists, which includes data scientists, make an average salary of $118,370 per year. This amount tends to lower for those that work within the education and federal government sector, but rises with employees that are part of research and development or software development. Because data science is such a broad category and duties can vary based on employment, the range sits between $70,000 and $180,000 when looking at the position across the United States.
In terms of average salary among all states, the highest-paying opportunities reside along the Northeast – specifically New York, Massachusetts, New Jersey, Maryland, and Virginia – the West Coast states of Washington, Oregon, and California, Colorado within the mountainous region, and Texas in the Southwest. According to BLS, none of these average state salaries are above $150,000, leaving some of the most highest-paid opportunities to senior positions that have years of experience.
When breaking down other jobs within or related to data science, business and marketing analysts can see some of the lower salaries (around $70,000 annually). Specific to business analysts, they require the least amount of programming experience and are more aligned with communicating to stakeholders. They have the ability to translate all the information analyzed by data scientists and making the right business decisions. This ability also makes them the interpreter between information technology and management.
Marketing analysts look to gain an edge on the competition and research consumer demand, making recommendations on how the organization they represent can better tailor the goods and services they offer to them. Typically, they are responsible for finding the best ways to bring in data (such as creating surveys or interviewing clients) and observing what happens in specific locations or creating advertisement toward that appeals toward specific demographics.
Developers, managers, and senior positions are where the higher-paying jobs are at within data science. This includes data architects, software engineers, and managers within information systems and databases. All these positions require high-level education (Masters and PhD) along with many years of professional experience. When it comes to higher-paying data science positions, that will likely have the same duties that these jobs have, which have some involvement in creating the foundation and processes that the organization uses in this field.