Unit 741: Introduction to Data Science

1. What is Data Science?

• Definition and scope of data science
• Importance of data science in various industries
• Role and responsibilities of a data scientist
• Interdisciplinary nature: statistics, mathematics, computer science, domain expertise

2. History of Data Science

• Evolution and milestones of data science
• Roots in statistics, computer science, and domain knowledge
• Growth in the era of big data and advanced computing

3. Data Science Lifecycle

• Stages overview: data collection, data cleaning, data exploration, model building, evaluation, deployment
• End-to-end workflow and iterative nature of the process

4. Data Types and Data Sources

• Types of data: structured, unstructured, semi-structured
• Common data sources: databases, text documents, images, sensors, social media

5. Data Collection and Cleaning

• Methods for collecting data from different sources
• Data quality issues and challenges
• Techniques for data cleaning: handling missing data, duplicates, errors

6. Data Preprocessing

• Data transformation and normalization
• Feature engineering and feature selection
• Preparing data for analysis and modeling

7. Exploratory Data Analysis (EDA)

• Purpose and importance of EDA
• Statistical summaries and descriptive statistics
• Visualization techniques to detect patterns and anomalies

8. Data Visualization

• Significance in data science for communication
• Types of visualizations: bar charts, histograms, scatter plots, heatmaps
• Tools and libraries: Matplotlib, Seaborn, Tableau
• Best practices in data visualization

9. Statistical Analysis for Data Science

• Basic statistical concepts: mean, median, variance, correlation
• Inferential statistics: hypothesis testing, regression analysis

10. Machine Learning Basics

• Introduction to machine learning concepts
• Types of learning: supervised vs unsupervised
• Algorithms overview: classification, regression, clustering
• Training, testing, and validation of models

11. Model Evaluation and Validation

• Performance metrics: accuracy, precision, recall, F1-score
• Cross-validation techniques
• Overfitting and underfitting
• Strategies to improve model performance

12. Big Data and Data Mining

• Understanding big data characteristics
• Technologies for big data processing: Hadoop, Spark
• Data mining techniques for pattern discovery and knowledge extraction

13. Ethical Considerations in Data Science

• Privacy and data protection issues
• Bias in data and algorithms
• Responsible and ethical use of data science methods
• Legal and societal implications

14. Introduction to Python for Data Science

• Python basics: data types, control structures, functions
• Libraries: NumPy, Pandas for data manipulation
• Simple data analysis examples

15. Introduction to R for Data Science

• Basics of R programming
• Data manipulation and visualization in R
• Statistical analysis using R

16. Data Science Project Management

• Defining project goals and scope
• Planning tasks and milestones
• Team collaboration and communication
• Presenting findings to stakeholders

17. Data Science Applications

• Case studies across industries: healthcare, finance, marketing, social media
• Real-world examples of data-driven decision making