Data science is emerging as a hot new discipline and everybody talks about the various ways in which data science impacts different industries and professions. Have you ever thought how the world is pacing up to prepare great enterprise data scientists? Data science is multidisciplinary in nature and is closely intertwined with the big data explosion. It goes far beyond business analytics, statistical analysis and data mining to identify data trends in huge data sets. Data Science is considered as child discipline - developed from several mature parental disciplines of software engineering, data engineering, business intelligence, scientific methods, visualization, statistics and a mishmash of many other disciplines. This article elaborates on how data science can be compared to the various analytics disciplines.

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Multidisciplinary Nature of Data Science

Data science can be related to an analogy – each guest in the guest list is invited to bring a friend to the party named data. The conversations that lead to innovation don’t just happen among the guests but they also include multiple data sources, thus, with data science, it is now possible to have deeper conversations that can lead to more creative and innovative ideas and also help the organization decide what ideas can be pursued to gain maximum profitability. This process of innovation and creative using data very well relates to the real essence of data science.

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According to Wikipedia:  “Data science incorporates varying elements and builds on techniques and theories from many fields, including math, statistics, data engineering, pattern recognition and learning, advanced computing, visualization, uncertainty modelling, data warehousing, and high performance computing with the goal of extracting meaning from data and creating data products.”

Given an objective, the ability to find out which data is accessible and useful, what is the effective way to manage this data, how to process the data and what kind of information can be extracted from the huge amounts of data is data science.

The data component of data science is derived from computer science and data engineering which deal with collecting, ingesting, transforming, retrieving and storing huge amounts of unstructured data that forms an integral part of data science applications. The science part of data science extracts meaningful insights from the data by using various tried and test scientific and statistical methods. Data science discipline requires computing and programming knowledge along with visualization so that any insights extracted from the data can be represented in a human understandable form. Statistics and Maths for the formal foundation base for data science.

The exciting thing about Data Science is that it can be applied to any business domain provided there is ability to gather valuable data on any given subject. However, this requires business domain expertise personnel who can identify the kind of data problems to be solved in a particular business domain, the types of answers business would be looking for and what is the best way to present the insights discovered so that it can be easily understood by business practitioners in their own ways.

Data Science vs Data Analysis

Data analysis emphasizes on correlative analysis to predict relationships between data sets or known variables to discover how a particular event can occur in the future. For instance, predicting when and which store locations should have sufficient stock of umbrellas and raincoats is dependent on future weather conditions. The weather might not have resulted in the buying behaviour of customers but it strongly relates to the sales of umbrellas and raincoats in future.

Data science emphasizes on providing strategic actionable insights into the world where people don’t know what they don’t know. For instance, identifying a future technology or trend that is not in existence now but will have great impact on an organization in future. The job role of a data analysts is narrow in terms of knowledge and experience when compared to a data scientist because analysts lack the business acumen.

Data Analysts focus more on descriptive nature of data analysis, but the role of a Data Scientist is to deep dive into the data and find actionable insights based on the data set. This is inferential in nature – where raw data is given and there are no guidelines, or goals for which the analysis is done. So the Data Scientist needs to find out what story the data tells and how these insights will be profitable for the business.

Data Science vs Data Mining

Data mining is a subset of data science that refers to the process of collecting data and searching it for patterns in data. The main goal is to design algorithms that extract insights from large unstructured data sets and validate the findings by applying identified patterns to novel subsets of data. The ultimate and direct business application of data mining is prediction. Data mining techniques consist of supervised classification, pattern recognition, clustering and various other statistical techniques. Statistics is the heart of data mining as it helps in differentiating between the significant findings and random noise. Data mining does not focus much on interpretability or discovering causes but emphasizes on providing a theory for estimating the probabilities of predictions.

Data Science is dependent on data mining. Rather data mining can be considered the first step of data science.

Data Science vs Machine Learning

Machine learning along with data science and big data is gaining traction because of its widespread use in various big data companies across the world. The major refining tools for doing data science is machine learning which is a cocktail of statistics, computer science and mathematics. Data science is a broader discipline that materializes around machine learning concepts which include interaction with existing systems like production databases, data acquisition and data cleaning.

Machine Learning and Statistics might be the stars but Data science is the orchestra of the big show. The goal of machine learning is to develop predictive models that are generic and can be applied to any domain related data problem, the predictive models developed using machine learning concepts are indistinguishable from a correct model. Machine learning algorithms automatically update themselves as they learn from data to discover new rules by using inferential statistics. Python programming language is used extensively for machine learning development.

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Data Science vs. Deep Learning

Deep learning is a branch of the broader field of machine learning. Deep learning involves the use of neural networks in problem-solving. A neural network is a framework where several machine learning techniques are combined to solve specific tasks. A deep learning system comprises a vast neural network trained using huge volumes of data. Deep learning architectures include recurrent neural networks (RNN) and convoluted neural networks (CNN). Deep learning is said to be “deep” due to the number of layers of transformation within each of the frameworks. 

Deep learning is a part of machine learning, and machine learning is a part of data science. 

Hence data science comprises the tools and techniques used in deep learning too.

Data Science vs. Data Engineering

Data engineering is all about the design and implementation of data architectures, including databases and large-scale data processing systems. Data engineers are responsible for testing and maintaining systems in which data can be stored and accessed by other data professionals when required but kept safe from unauthorized users. Data engineering generally involves processing raw data that may contain human errors or may be generated by machines. The data is not usually validated and may be unformatted. The data has to be converted from this unstructured format into a format that can be used for further processing and analysis by other professionals. Data scientists generally take the data prepared by data engineers to perform their further study. Data scientists and data engineers typically work very closely.

Data engineers can be considered to be the gatekeepers of data for a business. The field of data engineering requires the implementation of methods that can improve the quality, efficiency, and reliability of data stored in databases or data warehouses. It requires implementing various tools and techniques to acquire and aggregate data from other sources to a centralized data store. Data engineering usually goes hand-in-hand with data science, where the architecture of the data stores must meet the requirements of the data scientists and other stakeholders. Data engineering also requires creating efficient methods for querying the data.

Data Science vs Statistics

Statistics is a branch of mathematics for providing theoretical and practical support to data mining, business intelligence and data analysis tools.

Josh Wills, Data Scientist at Cloudera said “Data Scientist - Person who is better at statistics than any software engineer and better at software engineering than any statistician.”

Statistics emphasizes on developing smart mathematical models that can answer difficult questions about data sets by using limited computational resources whereas when we talk about Statistics for data science the same questions are answered using similar statistical techniques on huge unstructured data sets by using high computational resources. Most of the people who were called earlier statisticians are now being referred to as data analysts or data scientist. Data science is also closely related to other sub domains like statistical learning, computational statistics, statistical computing, Bayesian statistics and ensemble models.

A statistician without the knowledge of programming languages like Python or R, is just a statistician. A data scientist knows her programming languages along with mastering statistical modelling.

Data Science vs Operation Research

Operations research deals with decision making and optimization of various business projects like pricing, inventory management, supply chain, etc. Operations Research and Data science are closely related because OR algorithms are also applied on real world data. If operations research is the metal detector that guides to the right area of business then data science is the spade to dig into the data and extract value. Several OR analysts are making a career switch into data science as there are better opportunities in it when compared to OR and almost all the OR problems can be solved through the data science discipline.

Data Science vs Artificial Intelligence

Artificial Intelligence spans various knowledge domains like robotics, cognitive science, natural language processing, human-computer interaction, pattern recognition, etc.  Artificial Intelligence is a core part of data science and very well intersects with pattern recognition and the design of intelligent systems that perform various tasks. AI is stepping into the mainstream of data science as machines significantly contribute to making our lives better, whether it is deep learning, machine learning or predictive analytics - we will soon witness greater use of AI in data science discipline to make smarter business decisions.

Data Science vs. Business Analytics

The terms Data Science and Business Analytics are often used interchangeably. However, they are unique. Data science involves using algorithms, technology, and statistics to provide actionable insights based on the results of processing structured data, unstructured data, semistructured data, or a combination of the three. Business Analytics involves the statistical study of primarily structured business data. The scope of data science extends beyond business applications. Business analytics aims to provide solutions to specific problems or roadblocks faced by the business.

Data Science is a broad term that encompasses all things related to processing large volumes of data to gain insights from it. It is a crossover of data analytics, statistics, data visualization, and programming and is not limited solely to the algorithmic aspects. Business analytics is one of the end goals of data science. It is further divided into Statistical analysis and business intelligence.

Data Science vs Business Intelligence

As artificial intelligence makes a comeback, business intelligence is slightly declining because of its inability to adapt to novel unstructured data types which requires various data science techniques for processing and extracting information. Unless BI analysts learn programming, they cannot compete with some of the polyvalent data scientists who have expertise in decision science, presentation, insights extraction, business consulting and process optimization.

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Data Science vs Computer Science

Data science directly overlaps with the computer science discipline as it encompasses algorithmic and complex computational implementations, distributed architecture like Hadoop MapReduce for fast and scalable data processing, data plumbing for optimizing various data flows and in-memory analytics. Computer programming in Python and R language and various other problems like data compression, internet topology mapping, encryption and steganography also comes under Data Science.

Organizations across the world are striving hard to recruit millions of experts in data science and its related analytics disciplines like machine learning, statistics, predictive analytics, etc. Within a decade, data science will revolutionize the society in a way that is beyond imagination.

To be a part of this amazing technological revolution, upgrade your data science skills now!

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