Data is omnipresent, which makes data science a buzzword today. With rising demand for data science roles in different domains, several universities now offer specialised degrees in specific areas of data science at undergraduate and graduate levels. Online platforms also offer data science with python courses that are tailored for different learners’ needs.
Many parallels exist between the subjects of data science and statistics. Both these fields deal with obtaining data and analysing and solving real-world issues. Interestingly, data scientists were not very common two decades ago in any industry. This role was being successfully carried out by statisticians earlier, who worked on all aspects of data science. But have you ever wondered why data science roles are in greater demand now? And how do data science and statistics differ? This article will provide the answers to the above questions in the next section. Read on to discover all about data science vs Statistics and getting the best data science certification online.
Are Statistics and Data Science one and the same?
While we can certainly list a few similarities between data science and statistics, they are not one and the same. In data science, there is data collection, data organization, followed by data analysis, and visualization to draw meaningful insights from it. It is important to note that data science involves a heavy use of computers, coding, and algorithms to process large amounts of data. Statistics, on the other hand, is based on the application of mathematical models to quantify correlation between variables and outcomes derived from data. It performs predictions based on those relationships.
Let us delve deeper into understanding the difference between data science and statistics by comparing Data science and Statistics based on various factors.
Know more about measures of dispersion.
Top 7 differences between data science and statistics
The following table summarizes the top 7 differences between data science and statistics:
- Is an interdisciplinary branch of computer science used to gain valuable information from a large data using statistics, computers and technology.
- Using data science, we can convert a real-life problem into a research project for decision making
- Is a mathematical science for analysing existing data pertaining to specific problems, applying statistical tools to this data, and presenting the results for decision-making.
- Applied statistics is a modified application used in data science
- primary goal is to identify underlying trends and patterns in a data for decision making
- can work on any size of data, especially used to work on big data
- works well on both quantitative and qualitative data
- Key steps include
- data mining
- data pre-processing
- Exploratory Data Analysis (EDA)
- Model building and optimization
- Some important techniques include regression, classification
- primary goal is to determine cause-and-effect relationship in analysed data, is a purely mathematical approach
- analyses a smaller sampled data
- works only on quantitative data
- Key terms include
- Standard deviation (σ)
- Variance (σ2)
- Some important techniques include probability distribution, acceptance sampling and statistical quality control
|Application Areas||Can be applied in specialized areas like computer vision, natural language processing, disaster management, recommender systems and search engines, etc.||Can be applied in areas where random variations are observed in sampled data like medical, information technology, economics, engineering, finance, marketing, accounting, and business, etc.|
- Is to identify the best modelling technique by evaluating the model's predicted accuracy.
- Data scientists usually compare the predicted accuracy of several machine learning models prior to selecting the most accurate model.
- Is to begin with a simple model such as linear regression for a statistical analysis and check the consistency of the data to determine if satisfies the model hypothesis.
- Idea is to build on the basic single model that best fits the data rather than comparing several models unlike data science.
- Clearly defined roles and tasks that vary as per qualification and experience
- Data scientist, data analyst, data architect, data engineer, database manager, etc. are some typical roles in data science.
- Rising demand for data science degree holders can be seen in the past few years
- Average data science salaries begin at $60k/yr and may go upto $1.1L/yr for senior and experienced professionals.
- No clearly defined roles with hierarchy as several roles in statistics include positions for statisticians who can work in different industries as per business requirements.
- Market researchers, financial analysts, business analysts, economists, and database administrators, etc. are some typical statistician roles
- These roles have always been in demand globally even when data science was not so popular
- Average salaries fall between $75k - $1L/yr depending on the role responsibilities within an organization, which grow with experience.
|Skill sets and tools|
- Requires a degree in data science or a similar subject along with a good understanding of different algorithms is expected
- A working knowledge of statistics and mathematics is crucial along with good analytical skills
- Fluency in programming languages like Python, R, C/C++, Java etc. is also a must
- Soft skills like teamwork, efficient communication and organisation, and problem-solving abilities are also important
- Requires a degree in statistics or mathematics
- Excellent mathematical skills with advanced knowledge of calculus, linear algebra, and probability are expected
- Fluency in tools like Excel, SAS, SPSS, Minitab etc. is essential for statistical analysis with some basic knowledge of Python might be required
- Communication skills and strong planning skills are also required
|Real world Applications||Some real-life applications of data science include – |
- Computer vision applications
- Retail and e-commerce
- Banking and Finance for fraud detection
- Aviation for flight planning and routing
- Manufacturing industry for predictive maintenance
- Transportation and logistics for fleet management
- Chat bots
|Some real-life applications of statistics include –|
- Stock market
- Weather forecasting
- Sports and sporting events
- Public Administration
- Consumer goods
- Insurance industry
- Disaster prevention etc.
Let us examine the key differences in these two highly sought-after domains, in a greater detail, starting with the common definitions of data science and statistics.
Data science can be defined as a branch of computer science due to its focus on computers and databases. It is also an interdisciplinary subject that allows valuable information to be extracted from a huge amount of data (structured or unstructured) using statistics, computers and technology. It is possible to convert any business challenge into a research project with the use of data science and turn it back into a practical solution for the problem.
Statistics is a mathematical science that deals with data collection, data organization, data analysis, its interpretation, and presentation.
As the computing power of machines continues to scale with the advancement in information technology, it has significantly influenced the use of statistical science also. With emerging technologies like the internet of things, we can gather valuable data from a variety of sources on the internet, as well as data collected from various sensors. With growing access to big data, there is a rising demand for experts with applied statistics understanding. These experts visualise and analyse data, to make sense of it, and then use it to solve real-world challenging issues. Hence, we can say that statistics is a crucial part of modern data science.
This comparison is equally valid for applied statistics vs data science as the old format of statistics is now taking the shape of applied statistics. Today, applied statistics is a modified application of statistics like data science that is used in evaluating data to help identify and assess organisational needs.
2. Key Concepts Used in Data Science & Statistics
Both data science and statistics differ in the type of data they use, the size of the data and the way they interpret the outcomes.
Statistics has a purely mathematical approach and analyses a smaller and more manageable sampled data representing the collected data for a particular problem. The primary goal of a statistical analysis is to determine the cause-and-effect relationship in the analysed data. Statistics works only on quantitative data and never on qualitative data. However, it is possible to modify qualitative data into a suitable format for statistical analysis. Since we live in the information era, most of our everyday information can be quantified effectively using statistics. Mean (average), median (value repeating maximum number of times), mode (central value of the total number of observations), standard deviation (σ) and variance (σ2) are five important terms in statistics used to compare data.
With statistics, we can analyse the past events and use this information to predict what could happen in the future. The methods of data collection and data sources are decided by statisticians, followed by the design of experiments and estimation. Acceptance sampling and statistical quality control are also important techniques employed in statistics.
Data science can work very well on both qualitative and quantitative data, especially big data. It has a broader range of uses than statistics. The main approach in data science is to predict the outcome based on the underlying trends and patterns amongst various attributes in the analysed dataset. This approach purely focuses on model building and tuning on a pre-processed dataset for a specific problem. A typical data science process consists of the following steps –
- Data collection
- Data pre-processing
- Data analysis and exploration (EDA)
- Model building using the prepared data and applying suitable algorithms
- Generate predictions
- Compare various models, optimize and fine tune the best model
3. Application Areas
Statistics and data science both have a wide variety of applications.
Statistics allows us to make accurate predictions for a larger population from the sampled data by reducing the uncertainty. Hence, it is successfully being applied in a variety of sectors, including medical, information technology, economics, engineering, finance, marketing, accounting, and business, where random variations are often seen and statistically analysed sampled data can be used to make better predictions to solve a particular problem.
Data science is also applicable to similar areas like statistics, along with some specialised fields like computer vision, natural language processing, disaster management, recommender systems and search engines, etc.
Many data science challenges are tackled using a modelling technique that focuses on a model's predicted accuracy. Data scientists usually evaluate the predicted accuracy of several machine learning approaches prior to selecting the most accurate model. On the other hand, a simple model such as linear regression is generally the starting point for a statistical analysis and consistency of the data is evaluated to determine if satisfies the model hypothesis. In data analysis, two statistical approaches are used: descriptive statistics, which utilise indexes such as the mean or standard deviation to describe data from a sample, and inferential statistics, which derive inferences from characteristics of a population, a data that is likely to have random variation.
Thus, we can say that statistics builds on the basic single model that best fits the data, whereas data science compares several ways to develop the best machine learning model.
5. Career Options
Data scientists and statisticians may both work in a wide range of industries.
Typical data science roles that can be observed in any industry are data scientist, data analyst, data architect, data engineer, database manager, etc. Average data science salaries for entry, mid-level to senior positions are around $60k/yr, $80k/yr, $1L/yr respectively. There are clearly defined roles in data science, and the job description varies according to the required qualifications and experience.
Various roles in statistics include positions for statisticians who can work as market researchers, financial analysts, business analysts, economists, and database administrators. However, there is not a clearly defined hierarchy in the roles of statisticians, and designations can vary from organization to organization. There is a good demand for statisticians globally, and their salaries fall between $75k to $1L/year and above depending on the role responsibilities, which grow with experience.
6. Skill sets and Tools
Because statistics and data science are specialist fields, most positions need advanced education or a master's degree in a related discipline. Professionals in these industries require particular social skills and personal attributes in addition to technical knowledge.
A degree in data science or a similar subject is often required for data scientists. Because data scientists deal with databases, they must be fluent in a programming languages like Python, R, C/C++, Java etc. This career also necessitates a working grasp of statistics and mathematics. A computer science degree or experience can provide data scientists the abilities they need to deal with data and build code and algorithms. Data scientists study mathematics, machine learning, and artificial intelligence coursework to gain this experience. Similarly, strong analytical skills are required for data science as it is expected to assess the data, determine the goal or concerns, and choose how to give data to answer those questions. Soft skills like teamwork, efficient communication and organisation, and problem-solving abilities are also required for data scientists. KnowledgeHut’s data science with python course is a great option to begin your data science journey.
For professionals in Statistics, a degree in statistics or mathematics is a must. Being mathematically proficient enables statisticians to do complex calculations and choose the optimal answer for a given project. In addition to statistics, they are required to know calculus, linear algebra, and probability. Statisticians work with tools like Excel, SAS, SPSS, Minitab etc. for the statistical analysis. They are sometimes expected to have a working knowledge of a programming language, such as Python, because it might help them design tools for optimising statistical analyses. Communication skills and strong planning skills are also required in the statistical profession to successfully communicate the findings of the study and explain the results to a non-technical audience.
7. Real-World Applications
Let’s explore some real-life examples of data science and statistics at work.
Healthcare is the most popular use of data science. Data science may be used to gather and analyse trends in clinical data to forecast some dangerous illnesses, allowing medical experts to provide patients with the best possible therapy. Similarly, there has been a significant demand in recent years for fitness wearables or smart devices that can monitor important health factors or biostatistics such as heart rate, sleep quality, wearer activity, steps taken throughout the day, and so on, and then use these to estimate individual's fitness levels. There is a thin line when we do the comparative study of biostatistics vs data science. Biostatistics involves higher level of statistical analysis using limited set of tools whereas data science requires a greater understanding of the engineering aspects of big data.
Computer vision applications are another fascinating use of data science. These are applications that employ machine vision to identify objects in real time, like vehicle detection systems. The retail and e-commerce industries are also using data science. Big businesses like Amazon, Netflix, and others use data science to analyse their client base and deliver personalisation to improve the customer buying experience.
Banks and financial organisations also use data science to avoid fraud and mitigate risk. They can perform consumer data management, real-time predictive analytics, customer segmentation, and more, with the help of data science.
A few other real-world applications of data science include
- aviation industry for flight route planning and booking.
- manufacturing industry for predictive maintenance, cost reduction, and improved production efficiency through better resource allocation.
- transportation and logistics industry for efficient fleet management and resource optimization, and chat bots for a better customer experience.
The stock market is one of the most often seen applications in which statistics is used to determine dynamically changing stock values. The use of statistics makes financial planning and investment decision-making easier for investors.
Weather forecasting is another use of statistics. Weather forecasters employ the concepts of probability and statistics. They use a variety of concepts and technologies to forecast with greatest accuracy.
Another area where statistics is frequently employed is sports and sporting events for comparing circumstances and making decisions for players/teams during the games. Besides these, statistics is also widely used in the areas of Research, Public Administration, Business, Consumer goods, Insurance industry, Disaster prevention etc.
The Parallel Tracks of Statistics & Data Science
To summarize, data science and statistics are certainly different. In the world of data, these two have their own importance. Statistics gains importance when there is a need for testing, experimental design, normality distribution, and diagnostic plotting, whereas data science is non-negotiable when tasks require working with big data, some level of coding, and automating machine learning models. In conclusion, we can say the relationship between data science and statistics is that the latter is a powerful tool used in data science.
Frequently Asked Questions
Is Data Science Similar to Statistics?
No, they are different. However, statistics and data science both study and analyse volumes of data with different strategies and tools. Statistics mainly involves mathematical relations and the design of experiments to arrive at certain decisions. In contrast, data science is a broad field that uses algorithms, machine learning, and deep learning techniques to build models that will predict the required outcome for a specific application.
What Is the Difference Between a Data Scientist and a Statistician?
The role of a data scientist is cross-functional, requiring knowledge of both technical and soft skills needed for a particular task. A data scientist should be good at coding and should also understand business intelligence concepts to address critical business challenges for his organization.
A Statistician usually works on the design of experiments, selecting study approaches, analysing survey reports, etc. For all these tasks, he must have a strong background in mathematics and experimental design and basic programming knowledge.
So, Is Statistics Really Needed for Data Science?
It is obvious that these two fields are different. Hence, it is possible to get confused and wonder whether statistics are necessary for the field of data science. Well, easily the answer is "Yes, you do require statistics for data science." Data science is a combination of activities that begins with the acquisition of data in structured, semi-structured, or unstructured form and continues with pre-processing, processing, analyzing, and interpreting to produce relevant insights for decision making. During all these steps, statistics cannot be missed or neglected to get the desired outcome.
Can I Be a Data Scientist With a Statistics Degree?
After reading the article, I am convinced that you will agree that data science cannot progress or survive without statistics. Hence, if you want to have a career in data science, you must familiarise yourself with statistics to a good extent. In fact, good statisticians can be good data scientists too. Thus, it is easier to see that a statistics degree imparts the necessary mathematical skills required for data science. Hence, an individual with a statistics degree can become a data scientist. However, for a data science graduate with elementary statistics coursework, it is difficult to become a statistician and would require taking advanced level mathematics courses.