Introduction to Data Science and Business Analysis
What is data science? Overview of the data science lifecycle.
The importance of data analysis in business.
Key roles in data science and business analysis.
Tools and technologies in data science (Python, R, SQL, Excel).
Understanding and Preparing Data
Types of data (structured, unstructured, semi-structured).
Techniques for data cleaning: handling missing values, duplicates, and outliers.
Data wrangling and preprocessing for analysis.
Introduction to feature engineering.
Practical: Using Python (Pandas) to clean and prepare a dataset.
Data Exploration and Visualization
Exploratory data analysis (EDA) techniques.
Using Python libraries (Matplotlib, Seaborn) for visualization.
Univariate, bivariate, and multivariate analysis.
Insights and decision-making through visualization.
Practical: Perform EDA and visualize key metrics on a sample dataset.
Introduction to Descriptive and Inferential Statistics
Descriptive statistics (mean, median, mode, variance, standard deviation).
Inferential statistics (hypothesis testing, confidence intervals, p-values).
Types of distributions and significance testing.
Practical: Conduct descriptive and inferential statistical analysis on sample data.
Data-Driven Decision Making in Business
Key performance indicators (KPIs) and metrics in business analysis.
Using data to inform strategic decisions.
Case studies: Data-driven business success stories.
How to communicate data insights to stakeholders.
Practical: Develop insights and KPIs from real-world business datasets.
Introduction to Machine Learning for Business Analysis
Overview of machine learning algorithms (supervised, unsupervised).
Predictive modeling and its importance in business.
Simple regression and classification techniques.
Introduction to decision trees and clustering for business.
Practical: Build a simple predictive model in Python (e.g., linear regression).
Advanced Machine Learning Techniques for Business
Time series analysis for forecasting.
Advanced classification and clustering algorithms.
Building recommendation systems.
Model evaluation and tuning.
Practical: Implement a time series forecasting model for business data.
SQL for Data Science and Business Analysis
Writing SQL queries for data extraction.
Using aggregate functions and joining tables.
Advanced SQL queries for data analysis.
Using SQL to build and analyze business metrics.
Practical: Write SQL queries to extract and analyze data from a business dataset.
Communicating Data Insights
Principles of data storytelling.
Creating impactful dashboards and reports (Power BI/Tableau).
Visualizations for business: choosing the right charts for the right data.
Structuring presentations and reports for decision-makers.
Practical: Create a dashboard using Power BI or Tableau to present key insights.
Capstone Project and Business Case Study
Case study: Analyzing data to solve a business problem (e.g., customer segmentation, sales forecasting).
End-to-end project workflow (data collection, analysis, modeling, insights).
Presenting final analysis and recommendations.
Practical: Work on a comprehensive project and present findings to peers.