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Statistics for Data Science and Business Analysis
Practical: Develop insights and KPIs from real-world business datasets.
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.
Reading
Building recommendation systems.
Reading
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
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