Data Science vs Statistics is a topic which demands all our attention. Why? Because data is the biggest resource and revenue in the current business ecosystem. By collecting data, companies in every industry have demonstrated the ability to analyse it to generate business insights which help them improve their overall performance and growth.  So how is it done? – The answer is Data Science. It is a branch of science that uses data to predict future business trends. It collects, analyses and visualises data with the help of advanced mathematics and statistics. In this article, we will talk about the interesting concept of Data science and statistics. 

Data Science vs Statistics: How are They Different?

Before we try to dive deep into understanding the differences between data science and statistics, let us understand what is data science and what is statistics.

What is Data Science?

Data science is a detailed process it encapsulates four different steps –

  • Data Architecture
  • Acquisition
  • Analysis and
  • Archival

In the whole process of deriving results, data science uses advanced techniques such as mathematics and statistics to model data for deep analysis. Due to this level of process personalisation, it is capable of effortlessly solving all real-time issues with complete efficiency. Read more about how data science can solve real-time problems in our blog.

A Brief Look into Statistic Definition

Data science heavily relies on a few methods to derive results and statistics is one of them. Statistics deals with the study of data and its applications. Using it, data scientists are able to gather data, analyse it and interpret results to predict future trends. By taking numerical and categorical data as inputs, it processes the information and interprets the data for scientists to assist them in decision making. Statistics in data science is further divided into two types – Descriptive Statistics and Inferential statistics. While both of these use similar statistical measures, the way they operate and the goals they are used to achieve are entirely different. Read our blog on Descriptive Statistics vs Inferential Statistics to learn more about them in detail. 

Data Science vs Statistics: The Key Differences

Let’s now understand the key differences between data science and statistics.

  1. How is it defined?
    Data science is an interdisciplinary study that collects inputs from various kinds of data, i.e. structured and unstructured, to analyse and predict future trends based on them. It ideally is used for understanding the real-time problem or scenario with the help of data. Whereas, the definition of statistics is a branch of mathematics which provides a collection of methods to collect, analyse, interpret and represent the data.
  2. What does it do?
    Data science focuses on solving data-related problems and supports decision-making processes. It also models big data for analysis to understand the trends, behaviours, patterns in data, which help in improving the overall business performance. On the contrary, statistics is used to design and represent real-time data in the form of tables, charts, etc. in order to understand the techniques of analysis and support the process of decision making. 
  3. How is it done? 
    Data science applies scientific methods of problem-solving on the random data collected and identifies data requirements and techniques needed to obtain the desired results. On the other hand, statistics uses mathematical formulas and models to estimate values for various data attributes. It helps in showcasing the data behaviours in a pictorial manner.
  4. What does it solve?
    Data science uses advanced scientific computing techniques like machine learning, advanced mathematics and statistics to derive results and trends from the data. It employs programming, understanding of business models and trends. These data science skills are used to provide perfect predictions. Whereas, statistics is just a process used in data science to measure and estimate a data attribute by applying statistical functions and algorithms on the datasets. 
  5. What are the application areas? 
    Data science is now used in a variety of industries for the purpose of market analysis and trend prediction. It is also employed in healthcare systems, fraud and intrusion detection systems, manufacturing value chain and finance. Statistics is mainly used in commerce and trade. However, it is not limited to that area and is also used in economics, psychology, biology, astronomy as a way to conduct detailed data visualisation operations and studies. 

While learning more about data science vs statistics, we understand how both of them are interlinked. Statistics is as a key process involved in data science. It helps in the visualisation aspect of the data analysis process and also helps data scientists to understand data trends and patterns. The mathematics involved in statistics helps in gaining a deeper insight into the structure of the data, which helps us to identify and apply the right data science technique to derive the optimal results out of them. Data science, on the other hand, is a huge process that manages to isolate, collect, identify, analyse and predict a lot of information from the various datasets obtained. The introduction of big data analysis changes the approach of data science phenomenally. It sets the predicament of how data science skills are further going to develop and change the way data is perceived and used for decision-making. 

Now that you have learnt about the key differences between Data science and statistics, it is time to take the right actions, i.e. gain expertise in the field. If you are wondering about how you can do it? Let me help you with that. Springboard’s 1:1 mentoring-led, project-based data science online learning program that will help you prepare yourself for a meaningful and successful career in emerging technologies. You can check out our website to know more about it.