Data science is a rapidly growing field. Today, data scientists work across industries. They also hold positions at lean startups, Fortune 500 companies, and government organizations. This guide is an introduction to what a career in data science looks like including what it takes to become a data scientist, what kinds of data science degrees you might need, and what kinds of career paths you can follow.
How to Become a Data Scientist?
Data science is an exciting profession in that part of the job is technical and requires the ability to work with computers and software. The other part of the job requires having an analytical mind and the ability to recognize trends and patterns within the information. Finally, the last aspect of data science requires being able to articulate findings to colleagues and superiors within a company or organization.
To be a data scientist, you’ll need a solid understanding of the industry you’re working in and know what problems the organization is trying to solve. In terms of data science, being able to discern which problems are important to solve for the business is critical, and identifying new ways the business should be leveraging its data.
Most data scientists hold at least a bachelor’s degree, but increasingly professionals in the field are obtaining advanced degrees. Data science has its degree and program offerings (meaning you can major in data science, get a master’s degree in data science, and even get a Ph.D. in data science. But data scientists also work in the field after getting a closely related degree, such as computer science or data analytics.
Data Science Degree
There are two different routes that students usually take when trying to get a data science degree or related qualifications. Many data sciences positions required an advanced degree in a related field in the past. This was mainly because there wasn’t a specific data science discipline or major.
But as the data science field matures, and as more companies and organizations are looking for data scientists, the degree requirements and expectations are also changing. Today, there are multiple paths to the profession:
Most data scientists have a master’s degree or a Ph.D. in computer science, mathematics, statistics, information science, or other relevant areas like bioinformatics (depending on the industry requirements for a specialized skill set). Some universities have started offering advanced degrees in data science, specifically.
Getting Data Science Experience
Between online courses and data science competitions, there are many ways to explore a career in data science before actually jumping in. Here are a few suggestions:
- MOOCs and bootcamps: Massive open online courses like Coursera, Udemy and Udacity offer several programs from beginner level to refresher courses that could help you build skills that are needed to become data scientists. Check out our related guide for more info on data science certifications.
- Kaggle: Kaggle is an online platform that was acquired by Google in 2017. It has open source datasets and competitions that can help you get practical experience of real world problems and messy data. Kaggle is a great way to investigate what data scientist do, and to get an understanding of the profession, before jumping into a degree program.
- Data science communities: Data science communities provide resources that help you with knowledge building and job related resources. Data science central, KDD and LinkedIn communities are some of the largest communities that offer powerful resources to equip you with knowledge and skills necessary to become an efficient data scientist.
What is a Data Scientist?
Where does data science come from?
Data science as a discipline began in the late 1990s. In 1996, UsamaFayyad, one of the early practitioners in the emerging field, mentioned in his research paper that data mining and knowledge discovery in databases (KDD) would play an important role in the way people interact with databases, especially scientific databases where analysis and exploration operations are essential.
Between the time when the research article was published, and now, we have come a long way in understanding how we can leverage data to optimize and improve our day-to-day tasks. At the workplace, new data-related roles and responsibilities have emerged in the last decade, with data scientists as the most popular.
Some people think that the title of a data scientist is just a swish for a data analyst. Others think that working on machine learning and artificial intelligence consumes most of the time in a data scientist’s day. Add to the confusion with the emerging title of machine learning engineer, and there seems to be a need to elaborate on the role of a data scientist.
While a data analyst identifies the trends and patterns in the existing data, a data scientist can deduce stories behind these patterns.
A data scientist has an advanced background in statistics, mathematics, and computer science. They can identify novel features and signals by developing prediction, clustering, classification, or recommendation models in the dataset with the use of algorithms used in machine learning and neural networks.
While commonalities exist between the responsibilities of a machine learning engineer and a data scientist, the key difference is that a data scientist builds and tests models on data. A machine learning engineer has a background in software engineering with an ability to develop and deploy models in production.
Data Scientist Job Description
A data scientist is a strategist who solves an organization’s ambiguous problems using data. In a usual workday of a data scientist, their responsibilities include the following:
Communication and teamwork
Data scientists regularly communicate with business stakeholders to identify and drive business opportunities and solutions. Data scientists develop data-driven strategies to find predictive and optimized solutions based on these communications. They need to keep other functional teams involved in this communication process as they devise their strategy for developing the desired outcome.
A key facet in the role of a data scientist is creating storylines around the data to assist other people in understanding the cause, effect, and strategies of optimizations. For instance, presenting a data table is not as effective as sharing the insights from those data in a storytelling format. Using storytelling techniques will help communicate their findings to the business stakeholders properly.
Data mining and wrangling
Approximately sixty to eighty percent of the time of a data scientist goes into data mining and cleaning it. This responsibility overlaps with the tasks of a data analyst. Data scientists use various algorithms and software tools to extract and process the data. This process includes the use of various techniques such as:
- Clustering and classification of data. Common use case examples where this technique is needed is to perform customer segmentation and build profiles before marketing a new product.
- Anomaly detection. Common use case example where this technique is needed is to detect fraud in financial transactions.
- Finding dependencies between data features. Common use case examples include using sequential pattern mining to understand the spending pattern of a customer.
Build predictive models and recommendations
Data scientists use strategies in machine learning, natural language processing, and deep learning to develop custom data models and algorithms to target business outcomes, as discussed with the business stakeholders.
These business outcomes could be anything related to optimization, revenue generation, customer satisfaction, etc. At the same time, the algorithmic models have to be optimized for effectiveness and accuracy with continuous incoming of new data. Once these models are built and checked for accuracy, data scientists communicate with machine learning engineers and other data engineers to move or deploy these models into production. This responsibility also involves developing necessary processes to maintain and monitor model performance.
Develop A/B testing frameworks
By having a cause and effect testing framework, data scientists can use various data samples from the experiment to improve model behaviors for micro-cohorts or individuals. Business stakeholders and researchers can simulate outcomes based on improvements demonstrated by these testing frameworks. Without A/B testing, the modeling developed by a data scientist will lack a stimulus-response system, and teams may not be able to scope the opportunity size accurately.
Data Scientist Qualifications
Knowledge of a statistical/programming language
R is one of the analytical/statistical tools designed for data analysis. This is one of the tools most commonly used by data scientists. Another common language used by them is Python. Python shares a large user base as it is not only used for analysis but also as a web framework (Flask, Django) data manipulation (pandas, numpy) with a lot of machine learning libraries (scikit-learn, tensorflow, nltk, spacy) for implementation of machine learning and natural language processing models.
Like Python with Jupyter Notebook or Bokeh, R Shiny provides an ability to create and share interactive dashboard visualizations. Python has libraries to perform manipulation when dealing with unstructured data like customer reviews or clinical documents. And, of course, there are other programming languages like Julia that data scientists use.
Data scientists may have to deal with both structured and unstructured data. SQL programming is another sought-after skill when dealing with structured data. This skill comes to use when working with relational databases in traditional on-premise database management systems and on Apache Spark or databases on cloud services like AWS, GCP, Azure. SQL language may have some nuances with query language syntax depending on the database in use.
Big data technologies
With the amount of data being captured in the form of clicks per second or streaming information, most organizations have equipped themselves to store their data using big data technologies like Spark and Hadoop systems. Spark has built-in modules for SQL, machine learning, and graph processing that can be accessed on a unified platform. As a result, data scientists should be knowledgeable enough to work with these technologies to gather data and process it efficiently.
Data Scientist Job Outlook and Salary Outcomes
According to the Bureau of Labor Statistics, the employment of computer and information research scientists is projected to grow 15 percent from 2019 to 2029, much faster than the average for all occupations. Job prospects are expected to be excellent.
For example, in other databases, like Ziprecruiter, annual salaries are as high as $190,500 and as low as $36,500. A majority of data scientist salaries currently range between $92,500 (25th percentile) to $138,500 (75th percentile), with top earners (90th percentile) making $164,500 annually across the United States. The average pay range for a data scientist varies greatly (by as much as $46,000), which suggests there may be many opportunities for advancement and increased pay based on skill level, location, and years of experience.
In 2021, the average annual pay for a data scientist in the United States was $119,413.