Data Science and Statistics are two major fields in today’s world that help organisations to improve their future professional endeavours. With the growing rate of cybercriminals and hackers, organisational safety has become more prominent in this industry. Several methods have evolved to protect it and work towards the betterment of the future of companies. The main purpose of both these fields is to create trends, analyse data, analyse statistics, and find solutions. However, their approaches, techniques, and types seem to differ from each other. A vast number of job roles have also been available in this field over the period, and several programs have also evolved over time.
Top companies in India like TS, Accenture, Tech Mahindra, and Genpact have been giving opportunities to uncountable freshers to start their careers in data science. Whether a company is big or a small one, many businesses rely on this method for their betterment. Huge companies like Netflix, Amazon, Airlines internet search prefer the data science method to ensure better performance.
What is Data Science?
Data science is the field that has an interdisciplinary approach focusing on extracting knowledge from data sets and applying them across various domains. The sources that this knowledge is extracted from are generally big-sized data. Data Science, as a broad problem-solving tool, has developed over the past few years. It requires specialisation in certain programming languages like Python and R to run smoothly. The broader group consists of several other techniques like machine learning, business analysis, data analysis, and many more.
Data scientists mainly focus on brainstorming and analysing new data patterns with the help of data interpretation, predictive analysis, visualisation, and data manipulation. They invent new strategies that work for their organisation. Experts in this field have to go through several courses and training in order to be able to handle huge amounts of data information.
What is Statistics?
On the other hand, statistics is the discipline involved with collecting, analysing, interpreting, and presenting data. As an approach to data interpretation, statistics consists of two major approaches: data analysis and descriptive statistics.
Statistics is a subdivision of data science that mainly focuses on solving specific data collection, analysis, and visualisation problems. It is a method that is used by countless researchers, scientists, and mathematicians.
What do Data Scientists do?
- Professional data scientists associated with companies need to focus on the data-analytic problems that might occur primarily.
- The major steps that they need to focus on are data insights collection, data interpretation, predictive analysis, data modulation, and visualisation.
- They work on analysing and brainstorming new data patterns alongside the already-existing ones in order to make considerable benefits for their companies.
Interpreting the remaining data patterns takes over a major part of their work schedule. They try and analyse data patterns to avoid any kind of problem that may come their way in the future.
What do Statisticians do?
- Statisticians generally focus on data that is relevant to their research purpose. Their major role has evolved in the field of academia quite rapidly.
- Their major role is to gather, analyse, and interpret data which will eventually help companies make better business decisions in the future.
- They conduct surveys and mainly rely on those survey results to conduct their research. They focus on getting to a probable conclusion with the help of these sample answers extracted from the surveys.
Major differences between these two fields:
- Data Science involves a multidisciplinary blend of data interface, predictive analysis, visualisation, and algorithm development. The main work that data scientists focus on is brainstorming new ideas, interpreting extracted data, and visualising the end results in order to make necessary changes inside the organisational system. Data scientists need to be specialists in certain fields like Mathematics, Algebra, Communication, power of interpretation and visualisation, along with data mining and data mining procedures.
Statistics is a field that mainly focuses on studying data and is widely applied in several fields:
- A statistician’s role has been in existence for many years. It is older compared to the role of a data scientist. Countless businesses and companies rely on statisticians.
- One of the major differences seems to be the preparation that is required to become experts in the fields. Aspiring data scientists need to have at least a basic level of knowledge in several fields like Mathematics, Communication, programming languages, Computer Science, etc. In order to become a statistician, one does not need to have an engineering background. Aspiring data scientists can take any kind of data science course to become professionals in this field soon.
Statisticians are preferred to be experts in experimental designing, conducting surveys and studies regarding the nature of data, creating estimations, and conducting research.
A data scientist is preferred when he is from an engineering background, has deep knowledge of at least one programming language like Python, is good in mathematics and algebra. Data scientists need to have good communication and analyzing skills as well.
- Data science consists of several micro fields like computer science, software engineering, statistics, programming, math, etc. Statistics, as a subdivision of the broader procedure, focus on answering and solving certain small and specific questions.
- The major difference between these two data analysis approaches would be the amount of data managed by the two practices. Data science, being the broader group, can manage a huge amount of data and also large databases. Whereas statisticians focus on a rather specific area and try resolving the issues pertaining to that specific area. Therefore, the approach of statisticians is to focus on a smaller chunk of data and solve the existing issues in that particular area.
- Data science is used to solve problems related to broad future predictions, results, and insights. Data scientists determine and analyse trends and structures in a broader database. In contrast, statisticians focus on the reasons that cause small issues in the first place. Their primary focuses are mathematical and quantifiable models like tables, graphs, and charts to analyse smaller chunks of data information.
- The number of potential applications also differ from one field to the other. Data science is the broader field that has numerous applications where predictive data analysis is heavily needed. Some of the major sectors are finance, market analysis, and healthcare. However, statistics applications can be found in fields like psychology, astronomy, and trade.
- Statisticians focus on obtaining information from numerical to categorical data. Their major focus is to gather and analyse data. However, their approach differs from the data scientists, who seem to have a broader perspective in terms of data analysing and visualisation.
Some of the skills that both fields require are communication skills, mathematics skills, exploratory data analysis, creating forecasts and predictions, visualisation powers, and the ability to create trends.
Aspiring statisticians and data scientists require proper training and guidance in order to begin their careers in these fields. In order to do so, the first and foremost thing one should do is to categorise and understand their distinctions. There are several business analytic courses available both online and offline that aspiring professionals can check out. Several noticeable differences are there between these two fields of data interpretation. However, no debate is there as to which one is better in terms of data analysis for both the fields have equally significant contributions to make. Experts from both fields bring value in today’s world and constantly work towards making a safe future for their respective organisations.