Case study: Analyzing data to solve a business problem (e.g., customer segmentation, sales forecasting).

Statistics for Data Science and Business Analysis Case study: Analyzing data to solve a business problem (e.g., customer segmentation, sales forecasting).

Statistics for Data Science and Business Analysis

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Introduction to Data Science and Business Analysis

What is data science? Overview of the data science lifecycle.

Reading

The importance of data analysis in business.

Reading

Key roles in data science and business analysis.

Reading

Tools and technologies in data science (Python, R, SQL, Excel).

Reading

Understanding and Preparing Data

Types of data (structured, unstructured, semi-structured).

Reading

Techniques for data cleaning: handling missing values, duplicates, and outliers.

Reading

Data wrangling and preprocessing for analysis.

Reading

Introduction to feature engineering.

Reading

Practical: Using Python (Pandas) to clean and prepare a dataset.

Reading

Data Exploration and Visualization

Exploratory data analysis (EDA) techniques.

Reading

Using Python libraries (Matplotlib, Seaborn) for visualization.

Reading

Univariate, bivariate, and multivariate analysis.

Reading

Insights and decision-making through visualization.

Reading

Practical: Perform EDA and visualize key metrics on a sample dataset.

Reading

Introduction to Descriptive and Inferential Statistics

Descriptive statistics (mean, median, mode, variance, standard deviation).

Reading

Inferential statistics (hypothesis testing, confidence intervals, p-values).

Reading

Types of distributions and significance testing.

Reading

Practical: Conduct descriptive and inferential statistical analysis on sample data.

Reading

Data-Driven Decision Making in Business

Key performance indicators (KPIs) and metrics in business analysis.

Reading

Using data to inform strategic decisions.

Reading

Case studies: Data-driven business success stories.

Reading

How to communicate data insights to stakeholders.

Reading

Practical: Develop insights and KPIs from real-world business datasets.

Reading

Introduction to Machine Learning for Business Analysis

Overview of machine learning algorithms (supervised, unsupervised).

Reading

Predictive modeling and its importance in business.

Reading

Simple regression and classification techniques.

Reading

Introduction to decision trees and clustering for business.

Reading

Practical: Build a simple predictive model in Python (e.g., linear regression).

Reading

Advanced Machine Learning Techniques for Business

Time series analysis for forecasting.

Reading

Advanced classification and clustering algorithms.

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Building recommendation systems.

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Model evaluation and tuning.

Reading

Practical: Implement a time series forecasting model for business data.

Reading

SQL for Data Science and Business Analysis

Writing SQL queries for data extraction.

Reading

Using aggregate functions and joining tables.

Reading

Advanced SQL queries for data analysis.

Reading

Using SQL to build and analyze business metrics.

Reading

Practical: Write SQL queries to extract and analyze data from a business dataset.

Reading

Communicating Data Insights

Principles of data storytelling.

Reading

Creating impactful dashboards and reports (Power BI/Tableau).

Reading

Visualizations for business: choosing the right charts for the right data.

Reading

Structuring presentations and reports for decision-makers.

Reading

Practical: Create a dashboard using Power BI or Tableau to present key insights.

Reading

Capstone Project and Business Case Study

Case study: Analyzing data to solve a business problem (e.g., customer segmentation, sales forecasting).

Reading

End-to-end project workflow (data collection, analysis, modeling, insights).

Reading

Presenting final analysis and recommendations.

Reading

Practical: Work on a comprehensive project and present findings to peers.

Reading

Announcements Course Info

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Statistics for Data Science and Business Analysis Case study: Analyzing data to solve a business problem (e.g., customer segmentation, sales forecasting).

Statistics for Data Science and Business Analysis

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