Fundamentals of Business Analytics

Learn the difference between structured data (like tables) and unstructured data (like text, video). Understand their roles in analytical decision-making.

Explore techniques for collecting raw data and preparing it for analysis. Learn how to clean, format, and organize messy data.

Discover methods to deal with missing or abnormal data points. Understand how they affect analysis and how to correct or exclude them.

Get introduced to databases and learn basic SQL commands. Understand how to query, filter, and manage data efficiently.

Identify key sources of business data and their applications. Learn how systems like CRM, ERP, and surveys generate valuable insights.

Understand what makes data “high quality” and the risks of poor-quality data. Explore challenges like inconsistency, redundancy, and accuracy.

Learn the fundamentals of presenting data visually. Focus on clarity, simplicity, and choosing the right chart or graph.

Explore how descriptive analytics helps summarize historical data. Learn to derive key patterns and trends from datasets.

Understand mean, median, mode, range, and standard deviation. These tools help describe data distributions and variability.

Learn to summarize categorical and numerical data. Cross-tabulations help identify relationships between variables.

Create engaging visual outputs using charts, graphs, and dashboards. Gain skills to communicate data findings effectively.

Understand how diagnostic analytics investigates "why" things happened. Go beyond trends to uncover root causes.

Drill Down is a technique that allows users to explore data from a high-level summary to more detailed levels for better insight and root cause analysis.

Data Mining is the process of using statistical and machine learning techniques to discover hidden patterns, trends, and relationships in large datasets.

Learn the crucial difference between relationships and actual cause-effect. Avoid common interpretation errors in analysis.

Hypothesis testing is a statistical method used to determine whether there is enough evidence in a sample to infer that a certain condition holds true for the entire population. It involves testing a null hypothesis against an alternative hypothesis using probability-based criteria like p-values and significance levels.

Explore methods like 5 Whys and Fishbone diagrams. Learn to systematically trace problems to their true origins.

Identify trends and patterns in datasets to support business strategies. Recognize seasonal, cyclical, and irregular trends.

Data segmentation is the process of dividing a dataset into distinct groups based on shared characteristics or behaviors. It helps in uncovering patterns, enabling targeted analysis, and supporting personalized decision-making strategies.

Time series decomposition is a technique that breaks down a time series into trend, seasonality, and residual components. It helps in understanding underlying patterns and improving forecasting accuracy by isolating each component.

Outlier detection is the process of identifying data points that deviate significantly from the overall pattern of a dataset. It helps in spotting errors, anomalies, or rare events that may affect analysis or indicate valuable insights.

Analyze real-world problems like falling sales or customer loss. Learn to apply diagnostic techniques to find solutions.

Explore how predictive analytics forecasts future outcomes using historical data. Learn the value of predictions in business.

Understand how regression predicts continuous values, and classification predicts categories. Build simple models for both.

Learn basic methods to forecast trends over time. Apply models to financial, sales, or operational data.

Evaluate model performance using key metrics. Learn how to interpret errors and classification results.

Learn how prescriptive analytics uses data, models, and algorithms to recommend optimal actions for business decision-making. This lecture introduces key concepts and real-world applications that help organizations achieve better outcomes by suggesting actionable strategies

Understand the fundamentals of optimization, including linear and goal programming, to solve complex business problems. Learn to formulate and solve mathematical models that maximize efficiency and achieve multiple objectives

Discover how decision trees visually map out choices and outcomes, aiding in structured decision-making. Sensitivity analysis is introduced to evaluate how changes in input variables affect results, helping you assess risk and model robustness

Learn scenario planning to prepare for multiple future possibilities and use Monte Carlo simulation to model uncertainty and forecast outcomes. Gain hands-on skills to apply these techniques in Excel for robust business analysis

Master the use of Excel’s Solver tool to optimize business decisions, such as maximizing profits or minimizing costs. This lecture provides step-by-step guidance for setting up and solving real-world optimization problems within Excel

Explore how AI and machine learning revolutionize business analytics by automating data-driven decisions and uncovering hidden patterns. Learn about real-world applications that give organizations a competitive edge through smarter, faster insights

Understand techniques to extract insights from unstructured text and social media data, enabling better customer sentiment analysis and brand monitoring. Discover tools and methods for analyzing trends and engagement across digital platforms

Learn the ethical principles guiding data analytics, including responsible data use and privacy protection. Examine common ethical dilemmas, biases, and best practices for maintaining data security and compliance in analytics projects

Analyze real-world case studies demonstrating the impact of analytics in finance, marketing, human resources, and supply chain management. Gain practical knowledge on how data-driven strategies solve business challenges across various domains