Data Science

Chapter 1: Introduction to Data Science

• 1.1) What is Data Science?

• 1.2) Evolution of Data Science

• 1.3) Components of Data Science

  • Data Engineering

  • Machine Learning

  • Business Intelligence

• 1.4) Data Science vs. Data Analytics vs. Big Data

• 1.5) Applications of Data Science

• 1.6) Roles in a Data Science Project

  • Data Scientist, Data Analyst, Data Engineer

Chapter 2: Data Collection and Preprocessing

• 2.1) Data Types and Sources

  • Structured, Unstructured, Semi-structured

  • APIs, Web Scraping, Databases

• 2.2) Data Cleaning

  • Handling Missing Values

  • Outlier Detection and Treatment

  • Data Type Conversion

• 2.3) Data Transformation

  • Normalization and Standardization

  • Encoding Categorical Variables

• 2.4) Feature Engineering

  • Feature Creation

  • Feature Selection

  • Dimensionality Reduction

Chapter 3: Exploratory Data Analysis (EDA)

• 3.1) Importance of EDA

• 3.2) Descriptive Statistics

  • Mean, Median, Mode, Variance, Skewness

• 3.3) Data Visualization Techniques

  • Histograms, Boxplots, Scatterplots, Heatmaps

• 3.4) Correlation and Covariance

• 3.5) Tools for EDA

  • Python (Pandas, Seaborn, Matplotlib)

  • Jupyter Notebook

Chapter 4: Probability and Statistics for Data Science

• 4.1) Probability Basics

  • Conditional Probability

  • Bayes’ Theorem

• 4.2) Probability Distributions

  • Normal, Binomial, Poisson

• 4.3) Inferential Statistics

  • Sampling

  • Hypothesis Testing

  • Confidence Intervals

• 4.4) Statistical Tests

  • t-test, Chi-Square Test, ANOVA

Chapter 5: Machine Learning for Data Science

• 5.1) Supervised vs. Unsupervised Learning

• 5.2) Regression Algorithms

  • Linear and Logistic Regression

• 5.3) Classification Algorithms

  • Decision Trees, KNN, SVM, Naive Bayes

• 5.4) Clustering Techniques

  • K-Means, Hierarchical Clustering

• 5.5) Model Evaluation Metrics

  • Accuracy, Precision, Recall, F1-score, AUC

Chapter 6: Data Visualization and Communication

• 6.1) Principles of Good Visualization

• 6.2) Dashboard Design

• 6.3) Data Storytelling

• 6.4) Tools

  • Tableau, Power BI

  • Python Libraries: Seaborn, Plotly, Bokeh

Chapter 7: Big Data and Cloud Computing Basics

• 7.1) Introduction to Big Data

  • Characteristics (Volume, Velocity, Variety)

• 7.2) Hadoop Ecosystem

  • HDFS, MapReduce, YARN

• 7.3) Spark Overview

• 7.4) Introduction to Cloud Platforms

  • AWS, Google Cloud, Azure

• 7.5) Cloud Tools for Data Science

  • Colab, Sagemaker, BigQuery

Chapter 8: Case Studies and Applications

• 8.1) Data Science in Healthcare

• 8.2) Data Science in Finance

• 8.3) Recommendation Systems

• 8.4) Social Media and Text Analytics

• 8.5) Ethics and Bias in Data Science