Executive Programme in
Business Analytics (Batch 14)

Data lies at the core of competitive strategizing, and data analytics can reveal how organizations can innovate and build competitive advantage. Additionally, environmental data analysis aids businesses in evaluating the market conditions and competing aggressively with preparedness about the regulations, market conditions, and competitor moves. How data can be ingested, stored, and analyzed efficiently to draw competitive insights will be discussed in this module.

Statistics and mathematics are foundational pillars of business analytics, providing the essential tools and methodologies to extract valuable insights from data for informed decision-making in the business world. Statistics involves techniques for data collection, summarization, and inference, enabling the analysis of patterns and trends crucial for understanding market behavior, customer preferences, and operational efficiency. Mathematics, especially concepts from linear algebra, calculus, and optimization, underpin algorithms and models used in business analytics. These mathematical tools empower businesses to optimize processes, forecast trends, allocate resources efficiently, and build predictive models for sales, demand, and financial projections. Together, statistics and mathematics form the analytical backbone for deriving actionable intelligence, enhancing competitiveness, and steering strategic initiatives within the dynamic landscape of business analytics. In this module, we focus on the following topics:

• Application of statistics on analytics
• Distributions
• Hypothesis testing
• Linear algebra and others

Data visualization and descriptive analytics are crucial components of data analysis, providing insights and understanding from raw data. Data visualization involves representing data graphically to enable effective interpretation, making complex patterns and trends more accessible to users. Descriptive analytics involves summarizing and presenting key features of a dataset, such as central tendencies, distributions, and relationships between variables, offering a clear and comprehensive overview. Through visually appealing charts, graphs, and statistical measures, data visualization and descriptive analytics allow analysts and decision-makers to grasp the fundamental characteristics of the data, identify outliers, discern patterns, and draw preliminary conclusions, enabling informed decision-making and actionable insights for improved business strategies and problem-solving.

• Visualization with Excel
• Visualization with Python
• Visualization with Tableau

Unsupervised machine learning involves deriving patterns, structures, or insights from unlabeled data, where the model explores the inherent structure of the input without explicit guidance in the form of labeled outputs. The model's goal is to uncover hidden patterns, group similar data points, or reduce the dimensionality of the data without predefined target values. The absence of labeled data makes unsupervised learning particularly valuable for discovering valuable information and gaining a deeper understanding of complex datasets in various domains. Some of the tentative topics are mentioned as follows:

• Clustering
• Apriori Algorithm
• Recommender Systems

In the realm of advanced analytics, two transformative topics stand out: Big Data and Cloud Computing. Big Data involves the processing and analysis of immense volumes of structured and unstructured data, surpassing the capabilities of traditional database systems. It encompasses the challenges of capturing, storing, managing, and analyzing this data to extract valuable insights and patterns that can inform decision-making. Cloud Computing, on the other hand, revolutionizes data analytics by providing scalable and flexible computational resources over the internet. It offers a vast infrastructure capable of handling the computational demands of Big Data analytics, allowing for efficient storage, processing, and real-time analysis. The synergy of Big Data and Cloud Computing empowers organizations to derive actionable insights, enhance operational efficiency, and gain a competitive edge by leveraging the massive potential of data in a cost-effective and agile manner. The broad outline is as follows:

• Hadoop and Spark
• Data Bricks

This module will cover supervised machine learning to train a model on a labeled dataset, where the model learns to predict output labels or values based on input features. During the training process, the model iteratively adjusts its parameters to minimize the difference between its predictions and the true labels provided in the training data. The key idea is to generalize this learned relationship to accurately predict new, unseen data. In other words, the model learns the mapping between inputs and outputs, enabling it to make informed decisions or predictions on future, unseen examples, making supervised learning a fundamental approach in solving a wide array of real-world problems. The broad outline of the module is as follows:

• Naïve Bayes
• Logistic Regression
• Decision Tree
• Bagging and Boosting Techniques

This module on deep learning is an introduction to the world of applying large neural network models to solve practical problems in the field of computer vision and natural language processing. We will first look at what are neural networks and how they work. We will then understand the challenges of scaling these models to large neural network models. We will then understand how convolutional neural networks work and how they can be used to solve computer vision problems. The third part of this module is on developing an understanding of sequence models using recurrent neural networks. We will understand how recurrent neural networks process and analyze sequential data using real-life examples.

The broad outline of the module is as follows:

• Introduction to Neural Networks
• Deep Neural Networks
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Natural Language Processing using RNN

Financial analytics is the field that provides high- and granular-level views of a company's financial data, helping to improve its business performance. Under this module, participants will be exposed to different tools and techniques that can be used to extract meaningful information from the raw financial data of a company. Managers may use this meaningful information in making financing, investing, and operating decisions and achieve their business goals. The content to be covered here includes:

• Portfolio construction and analysis: Risk-return trade-off
• Financial modeling and analysis: Time series models
• High-frequency data analysis: Seasonality and noise
• Textual analysis: Readability and sentiment of financial statements

Marketing analytics involves tracking and analyzing data from various sources to increase sales and meet marketing targets. With the advent of e-commerce and social media, digital marketing dominates most businesses, and marketing analytics serves as a critical tool for digital marketing applications. In this module, we examine how data can be tracked, stored, and analyzed for crafting marketing strategies to enhance the future return on investment (ROI).

Operational analytics involves the use of data analysis and real-time processing to enhance day-to-day operations and decision-making within an organization. By leveraging data from various operational systems and processes, this approach provides valuable insights into performance, efficiency, and productivity. It often employs techniques to optimize operations, streamline workflows, and identify areas for improvement. In this module, we understand how operational analytics is instrumental in monitoring key performance indicators (KPIs), detecting anomalies, and enabling timely adjustments to enhance overall business operations.

This decade is marked by the rise of disruptive platform businesses, like Uber, Netflix, and Bharatmatrimony, to name a few. These businesses are heavily driven by the data generated in the networked market where they operate. In this module, we examine various platforms and their strategies to employ data analytics for both growth and competitive advantage. With platform businesses being structurally unique and completely digital, the scope for data analytics is significantly distinct from traditional businesses.

• Platform businesses and networked market
• Data analytics in a networked ecosystem

Cyber analytics involves the systematic analysis of data related to cybersecurity to detect, prevent, and respond to potential cyber threats and attacks. By employing advanced techniques such as anomaly detection, behavioral analysis, and statistical modeling, cyber analysts can sift through vast amounts of data, including network traffic, system logs, and user behavior, to identify suspicious or malicious activities. The goal of this module is to understand how to uncover patterns, trends, and anomalies that may indicate cybersecurity breaches, vulnerabilities, or potential risks. The tentative outline is as follows:

• How to profile customers based on traffic?
• How to use graphs in cyber analytics?

Technological developments in the digital space are removing constraints and creating exciting new possibilities that affect people’s lives and the way enterprises function. Businesses are becoming increasingly digitized. Managers must help companies acquire digital capabilities and leadership capabilities required to ride the wave of digital transformations, where data analytics constitutes one of the key elements. This module provides an understanding of key aspects of digital transformations through various technologies like IoT, blockchain, and AI, and how business analytics can enhance the performance and applications of these technologies phenomenally.

• IoT and Analytics in Industry 4.0
• Applications of AI in Predictive Maintenance
• Digital twin using a data-driven approach

This module covers Responsible AI to ensure ethical and fair usage of AI in an accountable manner. It entails designing AI models and algorithms that are transparent, explainable, and devoid of biases or discriminatory behavior. In this module, we examine the broader societal implications of AI technologies, including privacy, security, and the potential impact on diverse communities.

The general management domain spans multiple fields like finance, operations, marketing, information systems, organizational behavior and human resources, economics, public policy, and others. In the present era, analytics is employed in the various applications of these domains. Advanced analytics can help companies solve a host of management problems, including those related to marketing, sales, HR, and supply-chain operations, which can lead to a long-term competitive advantage. For instance, HR analytics encompasses a data-driven approach to better manage talents at work. Similarly, financial analytics evaluates financial processes and functions to manage risks and returns, as discussed below. It is essential to realize the multi-domain application of business analytics, and therefore, the purpose of the general management module is to aid participants with an overview of the analytics applications in the various fields of management.