Statistics vs data science

With data as core and intertwined goals, statistics vs data science might feel indifferent from a distance. But when the knot is undone, you can explore the void that statistics and data science hold in between them. Yes, although statistics and data science use big data to acquire actionable insight and the right decisions, they are two different perspectives from a professional point of view. However, if they are used together, statistics vs data science has the capability to make the powerful duo. Basically, statistics is used to obtain information from numerical data. It has the feature to plot even unstructured data in form of tables, charts, and graphs. On the other hand, data science is a collaboration of technologies that is implied on big data to get useful answers. It is used as an umbrella term to represent machine learning, deep learning, computer vision, natural language processing, and many other technologies. Data scientists uncover the insights by performing analyses on them. While statistics vs data science do have some similarities, they have their own specialty and differences that make both stands away from each other.

Top Differences between Statistics vs Data science

Solving Data Problems

Data problems are very usual in a business environment. However, data scientists and statisticians have different ways to deal with them. For example, when a data scientist is encountered with a business issue, he/she addresses it by comparing the predictive accuracy of different machine learning methods. They pick the model which is most accurate as of the solution. But statisticians deal with it differently. They start by checking if the data is right and improvising the model to address any assumptions. Statisticians can solve the problem once all the models are checked and no assumptions are violated. 

Career Options and Job Opportunites

Both data science and statistics professionals are needed in a plethora of industries. Their demand is anticipated to grow even further in the future. In 2021, more top companies are looking for professionals who can deal with big data effectively, be it statisticians or data scientists. This inspires aspirants to take up positions liek data analysts, data scientists, data engineers, and business intelligence analysts in data science. On the other hand, statistics professionals apply for statisticians, econometrics, and industry-specific statistics positions. 

Dealing with Basic Data

Although both data scientists and statisticians have to deal with data, they have different approaches to it. Statisticians focus more on quantifying uncertainty whale data scientists deal with the database directly. Statisticians build models to acknowledge the gap between predictors and outcomes. They also deal with small quantities of data and effectively quantify them. But data scientists spend more time preparing the gathered raw data for direct analysis. 

Combating Real-World Issues

Statistic models are formulated even before data is gathered to answer real-world questions. Once the data is ready, statisticians represent them in form of charts, tables, and graphs. They understand the data analysis techniques and ensure the decisions are profit-oriented. On the other hand, data science begins when there are issues with data. Data scientists come into the picture to solve real-world problems by using a fusion of technologies. Besides, they also help companies understand trends, patterns, behaviors, and business performance. 

Education and Skill Perspective

Since data science deals with datasets directly using technologies, data science courses teach students their usage. They teach aspirants how to use data analysis, machine learning, advanced programming skills, and statistical theory methods to acquire solutions from big data. To do this, students should have a better understanding of programming languages like R, SQL, Python, C++, or Java and requires hands-on skills in computer science. But learning statistics is more focused on collecting, organizing, analyzing, and interpreting numerical data to address business issues. Therefore, they require statistical and mathematical knowledge.