Data Science and Business Analytics Simplified 101

The advent of technology as a critical driving force in all sectors of the world has resulted in massive Data Generation today. A quick look into today’s Commercial Sector and you will surely come across these two terms, Data Science and Business Analytics. This is simply because data has become an integral part of any successful organization in the modern era.

Upon a complete walkthrough of this article, you will be having a decent knowledge of the key concepts of Data Science and Business Analytics. You will also get an idea about the importance of Business Analytics in Data Science.

Table of Contents

• Introduction to Data Science
  • Key Skills Required for Data Science
• Introduction to Business Analytics
  • Key Skills Required for Business Analytics
• Understanding the Importance of Business Analytics for Data Science
• Applications of Data Science and Business Analytics across Industries
  • Pattern Recognition
  • Predictive Modeling
  • Recommendation Engines & Personalization Systems
• Conclusion

Introduction to Data Science

The last decade has seen a rapid increase in the quantity of data that businesses have on their clients. This information comes from a variety of sources, including Social Media, Mobile Apps, Financial Transactions, E-Commerce, Internet Searches, and the Internet of Things.

Previously, companies could use numerous Data Mining techniques and tools to evaluate a smaller set of data. However, with the explosion of data from various sources, Data Science as a field is swiftly gaining traction. Similar to Data Mining, Data Science is an interdisciplinary topic concerned with scientific methods, procedures, and systems for extracting knowledge or insights from data in various formats, Structured or Unstructured. The word “insights” is emphasized in the term, as it refers to generating findings that were previously unknown using classic Data Mining approaches or simple Trend and Regression Analysis.

As a result, Data Science is thought to be at the crossroads of Mathematics, Statistics, Software, and Business domain expertise. One of the most basic applications of Data Science for any business is the ability to forecast Client Attrition ahead of time. It can assist the organization in focusing on Customer Retention rather than solely on more expensive customer acquisition.

Key Skills Required for Data Science

The core skills required for Data Science are as follows:

• Statistical Analysis: You must be familiar with Statistical tests, Probability estimators for having a good grasp on trends, patterns, and anomaly detection.
• Computer Science and Programming Knowledge: Learning Data Science requires you to deal with mammoth datasets to find solutions to certain problems, For this, you may need to write Computer Programs and therefore, you need to have a good command on Programming languages like Python, Java and SQL.
• Machine Learning: Machine Learning is a subfield of Data Science that is equally important as anything else. You need to be familiar with Statistical Algorithms to enable the computer to learn from the data automatically.
• Linear Algebra and Calculus: Mathematical knowledge of concepts such as Calculus and Algebra go hand in hand in building a Machine Learning model.
• Data Visualization: After you have all the necessary data to work on, you need to have a decent understanding about presenting the data to the technical as well as non-technical audiences.

Introduction to Business Analytics

Business Analytics is a methodical strategy for initiating and managing change in organizations, whether for Profit, Government, or Non-Profit organizations. Business Analytics is used to discover and express the need for change in how organizations operate, as well as to help organizations in implementing that change. It helps businesses to identify and specify the solutions that will maximize the value given by an organization to its stakeholders. Business Analysts work at various levels of a company, and their responsibilities range from formulating a strategy to creating the Enterprise Architecture, to having a leadership position in setting Program and Project Goals and criteria.

Key Skills Required for Business Analytics

Business Analytics acts as a bridge between IT Services and Business by connecting the dots between them using Data Analysis. The core skills required for Business Analytics are as follows:

• Data Interpretation: Companies manage a large amount of data. As a Business Analyst, you must be able to clean the data sets and make them usable for Data Interpretation.
• Data Visualization: Data Visualization is one of the key components of Business Analytics. You must be familiar with Charts, Graphs, and all the tools required to visualize the data for finding patterns and drawing insights.
• Analytical Reasoning: You need to have strong Logical Reasoning to perform Data Analysis efficiently.
• Mathematical & Statistical Skills: The ability to collect, organize and process numerical records is used for modelling, estimation, and forecasting in Business Analytics.
• Communication Skills: A Business Analyst needs to constantly work closely with the Business and Development teams hence, strong interpersonal skills come in handy while pitching ideas to the prospects in an easy-to-understand manner.

Understanding the Importance of Business Analytics for Data Science

While there are numerous Data Science tools available in the market, articulating the use cases from a business perspective is a vital aspect of any Data Science Initiative’s success. In most firms, IT teams are skilled at deciphering data and performing analysis on it. However, the discipline of Data Science is unique in the terms that teams may not always know what they’re looking for in the data because the insights aren’t always proven. As a result, the function of the Business Analyst becomes increasingly important in the industry, as you’ll need someone fluent not just in the IT domain but also in the language of business leaders.

To better understand the interdependency of Data Science and Business Analytics. Consider a situation where a Data Scientist would require the likely outcome of a certain action to an IT problem. To tackle this situation data must be analyzed to arrive at insights which would be done by the Business Analyst. The data would then be reviewed by the technology team, with the results being presented to the business team in the form of data patterns and insights. To generate insights for the situation at hand, a Business Analyst should be able to apply numerous Predictive Modeling techniques and make the appropriate model selection. Therefore, Data Science and Business Analytics complement each other in driving a business forward.

Applications of Data Science and Business Analytics across Industries

Data Science and Business Analytics go hand in hand in Business Development, and the convergence of the two fields has led to the emergence of specific applications in different industries. Some of them are mentioned below.

1) Pattern Recognition

Finding patterns in data sets is a key Data Science and Business Analytics goal. Pattern recognition, for example, aids Retailers and E-Commerce Enterprises in detecting trends in client’s purchase behavior. Making product offerings relevant and guaranteeing Supply Chain stability is critical for companies that want to keep their consumers satisfied and prevent them from buying from competitors. 

2) Predictive Modeling

Data Science and Business Analytics together strive to improve Predictive Modeling accuracy in addition to recognizing trends and outliers. Data Science combines Machine Learning and other algorithmic approaches to massive data sets to improve decision-making capabilities by generating models and by analyzing the data at multiple levels.

3) Recommendation Engines & Personalization Systems

Organizations can now construct complete profiles of individual customers using a combination of Data Science, Machine Learning, and Big Data. Their systems can learn people’s tastes over time and match them with others who share them, this process is known as Hyper-Personalization.

This is a very popular case of the convergence of the two fields- Data Science and Business Analytics. Companies like Home Depot, Lowe’s, and Netflix use data-driven Hyper-Personalization strategies like Recommendation Engines and Tailored Marketing to better target their services to customers.

Conclusion

Data Science is a catch-all term for anything involving the analysis of massive Data Collections. Data Science is a crossroads of Programming, Statistics, and Data Analytics that isn’t only about Statistics or Algorithms. The end result of Data Science is Business Analytics. The convergence of Data Science and Business Analytics can lead to significant growth in business.

In this article, you learned about Data Science and Business Analytics. You also learned about how Data Science and Business Analytics complement each other.

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