What Is the Difference Between Data Science and Statistics?
The fields of data science and statistics have many similarities. Both focus on extracting data and using it to analyze and solve real-world problems.
Data scientists use statistical analysis. However, data scientists need to be familiar with statistics, among other areas.In some cases, people with a background or education in statistics can gain additional knowledge through a degree program or job training and begin a data science career.
The similarities may make it seem like data science and statistics are different names for the same professional specialization; that is not the case. Data science is a multidisciplinary field that requires skills in programming, computer science, machine learning and creating algorithms.
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Data science involves the collection, organization, analysis and visualization of large amounts of data. Statisticians, meanwhile, use mathematical models to quantify relationships between variables and outcomes and make predictions based on those relationships. Statisticians do not use computer science, algorithms or machine learning to the same degree as computer scientists.
The basic concepts of each specialty can further illustrate the similarities and differences between data science and statistics.
Data scientists source, organize, extract and analyze data with the goal of addressing specific problems or answering questions. They focus on creating systems that perform these analyses and produce useful results. Data scientists involved with big data use immense amounts of information. Their role includes finding ways to extract relevant data from information warehouses.
Data scientists work with different types of companies. Some analyze data to provide insights that help businesses make decisions. They can also create systems that automate decision making. For example, a data scientist designed a system that collects data from your video viewing history and uses it to make personalized recommendations on Netflix.
In this way, data scientists are more focused on areas such as machine learning and computer science than statisticians. They are also involved in the creation and use of data systems, whereas statisticians focus more on the equations and mathematical models that they use for their analysis.
Because of its focus on computers and databases, many people consider data science a branch of computer science.
The main focus of statisticians is using mathematical and statistical models to analyze data.
Like data science, statistics have a broad range of applications. Also, like data scientists, statisticians collect information and use it to perform analyses. Their focus is on analyzing data to provide answers and insights that can inform decision-making.
Informing decision-making is a goal that data science and statistics share. The difference between these two specialties is the way that they handle that information to inform decision-making.
Data scientists and statisticians can work in a variety of fields. Demand for computer and information research scientists, which includes data scientists, is spiking as more companies and organizations begin looking to big data to make better decisions and improve operations.
Data analysts are often entry-level workers in the field of data science. They may have a bachelor’s or master’s degree in data science or a related field. A data analyst focuses specifically on analyzing data using the tools and methods that a data scientist develops. In many cases, a data analyst is an entry-level employee who wants to gain experience and become a full-fledged data scientist.
Data scientists combine analysis with algorithm creation, machine learning, data mining, and warehousing. In this career, your job duties include ensuring that the entire data pipeline, from data mining operations to finished algorithm or visualization of analysis results, meets the requirements of each project.
Data engineers create the infrastructure that handles all the data for data scientists. In this career, you manage the nuts and bolts of the data pipeline. You code tools that extract data from warehouses, or your build databases to store relevant information. Data engineers can also troubleshoot when problems arise with databases.
A business intelligence analyst with a background in data science can use their expertise to analyze business data and come up with data-driven solutions for strategic and operational issues. Their analysis can also help with decisions related to marketing, human resources, employee training and competitor activities.
Statisticians provide analysis using mathematical models and statistical equations. In this career, you select and analyze data after choosing the proper approach for your study. Your goal is to identify patterns and trends and use them to define relationships and make predictions. Statisticians can work for companies, health care providers, governments, financial institutions and in academic settings.
Public health statisticians use statistical analysis to make predictions and study public health issues from a mathematical perspective. They can examine health data to find patterns for the spread of illnesses and diseases and define the need for health education or health care services within a community.
Econometricians work in the financial industry. They use statistics to make forecasts about economic growth and to calculate and manage risk. In addition to a degree in statistics or related subjects, econometricians need to know about economics and finance. They may have studied these subjects at the undergraduate or graduate level.
Many statistics and data science careers require a master’s degree in a relevant field. In addition to technical knowledge (also known as hard skills), professionals in these fields need specific soft skills and personal traits to succeed.
Data scientists typically need a degree in data science or a related field.
Because they work with databases, data scientists need knowledge of relevant computer languages, such as R, SQL, Python, C++, or Java.
This career also requires knowledge of statistical analysis and mathematics.
A degree or experience in computer science can give data science professionals the necessary hard skills to manipulate data and write codes and algorithms.
Data scientists need to have the skills to work with algorithms. They can gain this experience by studying mathematics, machine learning and artificial intelligence.
In data science, you need strong analytical skills. You must analyze data, define problems and questions to address during a study, and come up with methods that give you the data to answer the question.
Data scientists work as part of a team with analysts, programmers, data engineers and administrators. You need to be able to work and communicate effectively with these other team members.
Problem-solving skills are vital for data scientists. In addition to using these abilities to answer the relevant questions during projects, you also need to be able to adjust your plan and solve technical and algorithmic problems.
Statisticians need a degree or equivalent experience in statistics or mathematics.
Mathematical skills are essential for statisticians. Your job requires the ability to perform complex calculations and also to select the proper methods for a given project. In addition to understanding statistics, you need to know calculus, linear algebra and probability.
Some statisticians need to know computer programming languages such as Python. These languages can help create tools to streamline your statistical analysis.
Visualization and reporting programs are helpful for presenting your findings to decision-makers. Often, statistics professionals need to explain their conclusions in a way non-experts will understand.
Communication skills are also vital. Not only do statisticians need to present their findings, but they may also need to work with other team members who are sourcing data or performing analysis. Additionally, statisticians need to work with professionals in the area they are analyzing to define problems and come up with relevant variables.
Organization skills are vital as well. One mistake in a statistical model could produce flawed results that could damage an entire project.
Both data science and statistics have a wide range of applications. However, the concepts that drive these types of analyses can seem abstract to people who are not familiar with them.
Here are some real-world examples of data science and statistics in action.
Data scientists can use real-time data to improve traffic safety. By collecting data on traffic, incidents and vehicle flows, data scientists can help find patterns of movement or behavior that lead to accidents. Once they define these causes, transportation authorities can address those variables that cause the most accidents.
Data science is also prevalent in e-commerce. Companies use customer data and viewing history to recommend relevant products to consumers. Data scientists are behind the artificial intelligence systems that make such recommendations.
Data scientists can also work on systems in health care. For example, wearable devices can provide data a medical professional can use to assess a person’s condition and get an early warning about potential health problems. Data scientists can create systems for reading and organizing this information in a way that provides relevant information to physicians.
Statisticians can create models that provide accurate weather forecasts. Statistics experts use past weather data to develop models that can predict weather conditions. While not perfect, these systems are often remarkably precise for predicting weather patterns.
Statisticians are often behind decisions to launch public health initiatives. Public health officials receive reports from statisticians that show the prevalence of disease or issues in a specific area or demographic group. They then focus on prevention and education efforts in these areas.
At sporting events, you often see statistics for players and teams. These stats come from past performance in similar situations or conditions. Athletes and teams may even employ statisticians to help with decisions during competitions.