What Do Data Scientists Do?

“Data scientist” is more difficult to define than terms used to describe other scientists, such as chemist, biologist, geneticist, or meteorologist. Part of the problem may be due to the fact that “data science” became a commonly used term long before data science became a formal field of study.

Even now people who call themselves data scientists come from diverse fields and industries and interact with data in different ways. Some work more as database administrators (DBAs), others lean more toward statistical analysis, while some focus most of their efforts on writing algorithms. (An algorithm is a process or set of rules for performing calculations, processing data, or solving problems.)

A data scientist is anyone who extracts value from data using a variety of skills, tools, and methods, including human logic, statistical analysis, machine learning, and visualization software.

Specifically, data scientists do the following:

Identify areas in which data can be used to the benefit of the organization
Gather data from internal or external sources to answer questions, solve problems, and automate processes
Clean and organize the data to prepare it for use
Choose appropriate tools and methods for extracting value from data
Clearly communicate the knowledge and insights gleaned from the data or provide the stakeholders with visualization tools that support self-service analytics

If you’re a statistician, a data analyst, or a mathematician who specializes in developing machine learning algorithms, you can probably make a strong case that you’re a data scientist. However, as this field becomes more established, more and more organizations are looking for candidates who have a standardized skill set. Several universities, including Berkley, Syracuse, and Columbia are already moving in this direction, offering degree programs in the field of data science. Graduates are expected to have a wide variety of skills in the following areas:

Computer science/programming
Data warehousing
Statistics
Mathematics
Storytelling and data visualization
Standard query language (SQL)
Machine learning

Asking Interesting and Relevant Questions

A large part of what a data scientist does is ask interesting questions that are relevant to furthering (or challenging) the organization’s strategy and objectives.

Over the last 20 years, most organizations focused on increasing their operational efficiency by streamlining their business processes. They asked operational questions such as, “How can we work smarter, instead of harder?” and “How can we implement new technologies to save time and money?”

Data science is different; it isn’t objective-driven. It’s exploratory and uses a scientific method. It’s not about how well an organization operates; it’s about gaining useful business knowledge and insight. Part of the role of a data scientist is to work with leaders and other stakeholders in an organization to ask interesting and relevant questions and mine the data for answers. Questions are less objective-driven and more business-intelligence driven, such as:

What do we know about our customer?
How can we deliver a better product?
Why are we better than our competitors?

These are all questions that require a higher level of organizational thinking, and most organizations aren’t ready to ask these types of questions. They are driven to set milestones and create budgets. They haven’t been rewarded for being skeptical or inquisitive.

Mining Data

Data scientists work in data mining — the process of extracting value from data by using a combination of database management, statistics, mathematics, and machine learning. Although the methods can be complex, data mining relies primarily on old school logical processes, including the following:

Descriptive statistics: Analyzing, describing, or summarizing data in a meaningful way to discover patterns in the data.
Probability: Gauging the likelihood that something will happen.
Correlation: Measuring the degree to which two things are related.
Causation: Determining the likelihood that one event is the result of another event.
Predictive analytics: Applying statistical analysis to historical data in an attempt to predict the future.

Delivering the Goods

One of the best ways to understand what any professional does is to look at what they produce or deliver — the fruits of their labor. Deliverables for data scientists include:

Automated processes (such as loan approval/rejection)
Classifications (for example, distinguishing credit card charges as valid or fraudulent)
Forecasts (for example, sales and revenue)
Pattern detection without classification (for example, to identify patterns in medical lab tests that indicate the presence of certain diseases)
Predictions (for example, assessing a driver’s risk of getting into a serious accident based on demographics and driving records)
Product recommendations (for customers based on their purchase history or searches they’ve conducted)
Recognition (for example, facial or speech recognition)

Data science is all about harnessing the power of data to gain knowledge and insight, solve problems, automate processes, and make better decisions. The data scientist plays a key role in this process.

Frequently Asked Questions

What are the main responsibilities of a data scientist?

A data scientist is responsible for collecting, processing, and interpreting data. They use data to generate insights that help drive decision-making within an organization. Data scientists often analyze data from various sources, build predictive models, and employ machine learning algorithms to uncover patterns in large amounts of data.

How does a data scientist analyze data?

Data scientists typically analyze data using statistical methods, programming languages like Python, and specialized data science tools. They work with large data sets, clean and prepare data, and use visualization techniques to communicate their findings effectively.

What skills are needed to become a data scientist?

To become a data scientist, professionals need a strong foundation in mathematics, statistics, and computer science. Skills in programming languages like Python, data processing, data analysis, and the ability to interpret data are crucial. Data scientists must also be able to use machine learning algorithms and data visualization tools to derive insights from data.

What is the difference between a data scientist and a data analyst?

While both data scientists and data analysts work with data, their roles are different in scope and skills. Data analysts typically focus on extracting and interpreting data to solve specific problems or answer questions. In contrast, data scientists use more advanced techniques, including predictive modeling and machine learning, to identify patterns and make data-driven predictions. Data scientists also often handle unstructured data and require more extensive programming and statistical knowledge.

What is the role of machine learning in data science?

Machine learning plays a crucial role in data science. It involves algorithms and statistical models that enable data scientists to make predictions or decisions without being explicitly programmed. Data scientists use machine learning to analyze data, identify patterns, and build predictive models that can drive business insights and automation.

What tools do data scientists use?

Data scientists use a variety of tools to analyze data and build models. Common tools include programming languages like Python and R, data visualization tools such as Tableau, and machine learning libraries like TensorFlow and scikit-learn. Data processing frameworks like Hadoop and Spark are also frequently used to handle big data.

What types of data do data scientists work with?

Data scientists work with various types of data, including structured, semi-structured, and unstructured data. They collect data from different data sources, such as databases, web services, and social media platforms. Handling large amounts of data allows data scientists to uncover insights and build effective data models for predictive analysis.

What is the importance of data preprocessing in data science projects?

Data preprocessing is a critical step in data science projects. It involves cleaning and transforming raw data into a format suitable for analysis. This process helps improve data quality and ensures that the data is accurate and complete. Without effective data preprocessing, the results of data analysis and predictive modeling may be flawed, leading to incorrect insights and decisions.

This is my weekly newsletter that I call The Deep End because I want to go deeper than results you’ll see from searches or AI, incorporating insights from the history of data and data science. Each week I’ll go deep to explain a topic that’s relevant to people who work with technology. I’ll be posting about artificial intelligence, data science, and data ethics.

This newsletter is 100% human written 💪 (* aside from a quick run through grammar and spell check).