Business & Management Analytics

Use data to understand and improve business performance through descriptive, diagnostic, predictive, and prescriptive analytics. Use tools like Tableau and Power BI to uncover insights for marketing and operations.

Business analytics drives better decision making by analyzing data to generate insights, predict future outcomes, and optimize operations, with applications in marketing, finance, supply chain, human resources, and healthcare.

Explore descriptive, diagnostic, predictive, and prescriptive analytics to understand past performance, diagnose causes, forecast outcomes, and optimize decisions.

Discover Python, a high-level interpreted language, famed for readability and versatility, enabling web development with Django and Flask, data analysis with NumPy, SciPy, pandas, and matplotlib.

Explore how Conda and Anaconda manage environments and libraries, leverage Jupyter notebooks for interactive data work, and use VS code for efficient Python development.

Learn Google Colab's browser-based, cloud-first Python environment with free GPUs and TPUs, Google Drive integration, and pre-installed TensorFlow, Keras, NumPy, and SciPy libraries.

Explore Python data structures: lists, tuples, and sets, covering indexing, slicing, negative indexing, mutability and immutability, and key operations like append, remove, and set unions, intersections, and membership tests.

Explore Python file handling by opening, reading, writing, and closing files in various modes. Learn to use pandas for loading, manipulating, and exporting dataframes to CSV, Excel, and JSON.

Learn to write robust Python code by using try and except, else and finally, manage specific errors, and create custom exceptions for input validation.

Explore object oriented programming (OOP) in Python, focusing on objects, classes, attributes, and methods, and how encapsulation, inheritance, and polymorphism enable reusable, modular code.

Learn the basics of data visualization in Python using Matplotlib and Seaborn, including line plots, bar charts, histograms, and heatmaps.

Explore descriptive statistics to summarize data and identify patterns and anomalies. Learn central tendency, dispersion, and shape through mean, median, mode, range, variance, standard deviation, skewness, kurtosis, and percentiles.

Explore correlation and regression analysis, including Pearson and Spearman measures, simple and multiple linear regression, nonlinear regression with polynomial, exponential, and logistic forms, and their business, healthcare, and engineering applications.

Improve decision making by ensuring data quality through profiling, cleaning, validation, documentation, and automation, preserving accuracy, completeness, consistency, timeliness, and relevance.

Learn data cleaning techniques to fix missing values, duplicates, outliers, and inconsistencies, delivering clean, reliable data ready for analysis and modeling with standardization, validation, and pipelines.

Handle missing values in data pre-processing with imputation, drop strategies, and flagging techniques, including mean, median, and mode, forward and backward fill, interpolation, and k nearest neighbors.

Explore feature scaling and normalization to ensure equal feature contribution and faster learning, with min-max scaling, standardization, robust scaling, L1/L2 normalization for distance-based models like kNN and SVM.

Learn standardization techniques to transform data to zero mean and unit variance, ensuring equal feature contribution and faster convergence in models such as PCA, logistic regression, and SVM.

Encode categorical variables into numeric representations using label, one-hot, binary, ordinal, target, and frequency encodings for nominal and ordinal data.

Explore feature engineering by transforming raw data into informative features that boost model performance, interpretability, and robustness through exploratory data analysis and a structured feature creation process.

Explore dimensionality reduction to reduce features while preserving essential information, covering feature selection and feature extraction methods like PCA, LDA, t-SNE, and autoencoders, with practical visualization and modeling trade-offs.

Mathematical modeling uses formulas to represent real world issues, enabling decision making, optimization, and resource use by defining variables, parameters, constraints, objectives, and solving with analytical, numerical, or simulation methods.

Explore common symbols and notations in mathematical modeling, including decision variables, parameters, indices, constants, sets, summation, product, and objective and constraint functions for maximization or minimization in production planning.

Maximize profit by selecting the production mix of products A and B under 100 labor hours and 8 materials per week, using a linear programming approach.

Use the pulp library to maximize profit by choosing x and y for two products under 100 hours and eight material units, with profits 30 and 40 per unit.

Explore network flows through five algorithms, including minimum cost and maximum flow, with graphical representations and Bellman-Ford and Edmonds-Karp, applied in python for logistics and traffic management.

Learn the maximum flow problem and how Ford-Fulkerson uses breadth-first search to find augmenting paths in capacity graphs represented by an adjacency matrix, updating residual capacities between source and sink.

Visualize a directed flow network with NetworkX and matplotlib, define nodes and capacities, compute the maximum flow from S to T, and show labeled edges with a spring layout.

Gain insight into the Bellman-Ford algorithm for shortest paths from a single source, handling negative weights and detecting negative cycles via edge relaxation on an adjacency-list graph.

Edmonds-karp uses breadth-first search to find shortest augmenting paths from source to sink. It uses forward and reverse edges to manage residual capacity and computes the maximum flow.

Explore genetic algorithms as an optimization method inspired by natural selection, using populations, fitness, crossover, and mutation to explore solution spaces across generations.

Explore genetic algorithm concepts, including population, chromosome, gene, and fitness function, and how selection, crossover, mutation, and elitism drive evolution. Compare generational and steady state models, and define termination conditions.

Explore how genetic algorithms based on natural selection and evolution simulate defense strategies that adapt in time to warfare, identifying effective plans through mission success, resource use, and casualty reduction.

Define a genetic algorithm math model with sets, indices, actions, resources, scenarios, parameters, and decision variables. Maximize overall strategy effectiveness across scenarios under resource, feasibility, and likelihood constraints.

Explore a genetic algorithm coded with a deep evolutionary library to optimize action-resource strategies across scenarios, using a fitness function with penalties and operators like crossover, mutation, and tournament selection.

Explore large scale optimization for supply chains by solving production and distribution problems across factories and warehouses. Minimize total transportation costs while respecting production capacities and warehouse demands.

Explore advanced production planning with an optimizer class, implement lot sizing, BOMs and ATP in a Streamlit app, and compare make to order versus make to stock.

Apply lime to explain text classification predictions by highlighting how individual words influence a logistic regression model's sentiment output, using a small dataset and a tf-idf pipeline.

Apply robust optimization with cvxpy to minimize the worst-case production cost under mean demand with uncertainty, and examine how uncertainty multipliers shape two-product production and costs.

Explore dynamic programming as a powerful optimization technique that decomposes complex problems into simpler subproblems, uses memorization and the Bellman equation to determine optimal policies across stages.

Explore a dynamic programming model for a multi-constraint knapsack, using binary decisions x_I to maximize total value under capacity constraints across dimensions.

Explore dynamic programming for a multi-dimensional knapsack by coding a function that selects items to maximize value without exceeding multi-constraint capacities, using a dictionary-based dp table and reverse-update logic.

Build a multi-period portfolio optimization model in Julia using mixed integer non-linear programming to balance risk and return under sector diversification rules and transaction costs.

Apply a dynamic inventory model with sequential decision analytics to adapt order quantities based on current inventory, reorder point, and target inventory level, minimizing total costs.