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

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