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Application Open [Batch 33]

Admission deadline: (Batch 33)
2026-09-30

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Applied Machine Learning
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Applied Data Science and Business Analytics
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Generative AI and Deep Learning
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Introduction to Python
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Course Detail:

Course Title: Applied Data Science and Business Analytics

Introduction

The Applied Data Science and Business Analytics course at the Bangladesh-Korea Information Access Center (BK-IAC) is designed for professionals, entrepreneurs, researchers, and decision-makers who want to use data to solve real-world business problems.

The course provides practical knowledge of the complete data science and business analytics workflow, including data collection, database querying, data preprocessing, exploratory data analysis, statistical inference, machine learning, time-series forecasting, model interpretation, and business reporting. Participants will work with real-world datasets and learn how to transform raw data into meaningful insights for strategic and operational decision-making.

The course emphasizes hands-on learning using Python, NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn, and SQL. Participants will also complete assignments and a case-study-based final project.

Objectives

To apply Python, NumPy, and Pandas for business data analysis.
To develop practical skills in data collection, database querying, cleaning, preprocessing, transformation, and feature engineering.
To perform exploratory data analysis and communicate insights through effective visualizations.
To understand descriptive statistics, probability, hypothesis testing, and A/B testing.
To develop regression, classification, clustering, and forecasting models using Scikit-Learn.
To evaluate machine learning models using appropriate performance metrics.
To interpret model predictions and understand the business implications of analytical results.
To analyze real-world business problems related to sales, customers, finance, investment, and operations.
To prepare business reports and data-driven recommendations.

Expected Learning Outcomes

After successfully completing the course, participants will be able to:

Collect, query, clean, transform, and analyze structured business data.
Identify trends, relationships, anomalies, and patterns in datasets.
Select appropriate statistical and machine learning methods for a business problem.
Develop and evaluate predictive and descriptive analytical models.
Translate analytical results into actionable business recommendations.
Create professional visualizations and reports for decision-makers.
Complete an end-to-end data science and business analytics project.

Prerequisite

Participants are expected to have prior programming knowledge, particularly basic Python programming. They should be familiar with variables, conditional statements, loops, functions, lists, dictionaries, and basic file handling.

The first classes will provide a short refresher on Python libraries used for data analysis, but the course will not teach introductory Python programming from the beginning.

Participants without adequate Python knowledge are strongly encouraged to complete our Introduction to Python course before enrolling. Enrolling without the required programming background may make it difficult to follow the practical exercises and assignments.

Tentative Class Schedule

The course will run for 8 weeks, with two classes per week. Each class will be approximately 3 hours long, resulting in a total of 48 contact hours. The tentative lecture plan is as follows:

``` ```

Class#	Content
1	Introduction to Data Science and Business Analytics; Data-Driven Decision-Making; Python Environment Setup; Jupyter Notebook and Google Colab; NumPy and Pandas Refresher
2	Working with Business Data Using Pandas: DataFrames, Indexing, Filtering, Sorting, Aggregation, GroupBy, Pivot Tables, and File Operations
3	SQL for Business Analytics—I: Relational Database Fundamentals, Tables, Keys, SELECT, WHERE, ORDER BY, DISTINCT, Aggregate Functions, GROUP BY, and HAVING
4	Data Collection and Handling: SQL JOIN Operations and Subqueries; Loading Data from SQL Databases, CSV, Excel, and JSON into Pandas; Combining and Reshaping Datasets
5	Data Cleaning and Preprocessing: Missing Values, Duplicate Records, Inconsistent Data, Outlier Detection, Categorical Variables, Scaling, and Transformation
6	Exploratory Data Analysis—I: Descriptive Statistics, Business Metrics, Distribution Analysis, Group-Based Analysis, Correlation, and Pattern Identification
7	Exploratory Data Analysis—II and Visualization: Matplotlib and Seaborn; Histogram, Box Plot, KDE Plot, Violin Plot, Pair Plot, Bar Chart, Heatmap, and Time-Series Plot Assignment 1: Data Cleaning, EDA, and Visualization
8	Evaluation and Presentation of Assignment 1; Discussion of Common Data Analysis and Visualization Mistakes
9	Statistical Inference for Business: Sampling, Confidence Intervals, Hypothesis Formulation, Statistical Significance, Common Statistical Tests, and A/B Testing
10	Regression Analytics: Simple Linear Regression, Multiple Linear Regression, Assumptions, Feature Selection, Performance Evaluation, and Business Interpretation
11	Supervised Machine Learning for Business: Classification Problems, Logistic Regression, Decision Trees, Random Forests, and Practical Scikit-Learn Workflows
12	Model Development and Evaluation: Train-Test Split, Cross-Validation, Data Leakage, Feature Engineering, Scikit-Learn Pipelines, Hyperparameter Tuning, and Model Comparison Assignment 2: Predictive Analytics and Machine Learning
13	Unsupervised Learning and Customer Analytics: Clustering, K-Means, Customer Segmentation, Principal Component Analysis, and Business Applications
14	Time-Series Analytics and Forecasting: Trends, Seasonality, Moving Averages, Forecast Evaluation, ARIMA, and Introduction to Machine-Learning-Based Forecasting Evaluation of Assignment 2
15	Applied Business Analytics Case Study: Integrating Data Extraction, Data Preprocessing, Exploratory Analysis, Visualization, Statistical Analysis, and Predictive Modeling
16	Explainable and Responsible Data Science: Feature Importance, Model Interpretation, Bias, Fairness, Privacy, Ethical Use of Data, and Communicating Model Limitations Final Case-Study Presentation and Evaluation: End-to-End Analysis of a Business Dataset, Findings, and Business Recommendations

Possible Case-Study Areas

Sales forecasting and revenue analysis
Customer segmentation and customer behavior analysis
Customer churn prediction
Credit risk and loan approval analysis
Stock-market and investment analysis
Marketing campaign and A/B test analysis
Demand forecasting and inventory optimization
Employee performance and attrition analysis
Fraud and anomaly detection

Tools and Technologies

Python
Jupyter Notebook and Google Colab
NumPy and Pandas
Matplotlib and Seaborn
Scikit-Learn
SQL
Relational Database Management Systems

Learning and Evaluation Method

Classes will be conducted in a multimedia-equipped environment.
All classes will combine theoretical discussion with hands-on practical exercises.
Expert faculty members from the relevant fields of data science and business analytics will conduct the classes.
All instructors will be faculty members from the Department of CSE, BUET.
Participants will have access to computers for practical exercises and project work.
Real-world databases and datasets will be used to demonstrate business analytics and data science applications.
Participants will be evaluated through assignments, practical exercises, and a final case study.
A certificate will be awarded upon successfully completing the course and fulfilling the evaluation requirements.

Further Query

Email: iac@cse.buet.ac.bd
Phone: 9665650-80 Ext-6438
Mobile: 01670032959