Data Analytics

Chapter 1: Introduction to Data Analytics

  • 1.1) Definition and Scope of Data Analytics

  • 1.2) Types of Data Analytics

    • Descriptive, Diagnostic, Predictive, Prescriptive

  • 1.3) Importance in Decision Making

  • 1.4) Data Analytics Life Cycle

  • 1.5) Tools for Data Analytics

    • Python, R, Excel, Power BI

Chapter 2: Data Collection and Cleaning

  • 2.1) Data Collection Techniques

    • Surveys, Web Scraping, APIs

  • 2.2) Data Types and Sources

    • Structured, Semi-structured, Unstructured

  • 2.3) Data Cleaning Techniques

    • Handling Missing Values

    • Outlier Detection and Treatment

    • Duplicate Removal and Data Consistency

  • 2.4) Data Transformation

    • Normalization and Standardization

Chapter 3: Data Exploration and Visualization

  • 3.1) Exploratory Data Analysis (EDA)

  • 3.2) Summary Statistics

    • Mean, Median, Mode, Variance, Standard Deviation

  • 3.3) Data Visualization Techniques

    • Bar Charts, Histograms, Boxplots, Scatterplots

  • 3.4) Visualization Tools

    • Python (Matplotlib, Seaborn), Tableau, Power BI

Chapter 4: Statistical Foundations for Data Analytics

  • 4.1) Probability Concepts

    • Conditional Probability, Bayes’ Theorem

  • 4.2) Distributions

    • Normal, Binomial, Poisson

  • 4.3) Sampling Techniques

    • Random, Stratified, Cluster Sampling

  • 4.4) Hypothesis Testing

    • Null and Alternative Hypothesis

    • t-test, z-test, Chi-square test

Chapter 5: Predictive Analytics and Machine Learning

  • 5.1) Introduction to Predictive Modeling

  • 5.2) Regression Techniques

    • Linear Regression, Multiple Regression

  • 5.3) Classification Techniques

    • Decision Trees, K-Nearest Neighbors (KNN), Logistic Regression

  • 5.4) Model Evaluation

    • Confusion Matrix, Accuracy, Precision, Recall, F1 Score

Chapter 6: Data Mining Techniques

  • 6.1) Introduction to Data Mining

  • 6.2) Clustering

    • K-Means, Hierarchical Clustering

  • 6.3) Association Rule Mining

    • Apriori Algorithm, Support, Confidence, Lift

  • 6.4) Anomaly Detection

Chapter 7: Time Series Analysis

  • 7.1) Components of Time Series

    • Trend, Seasonality, Noise

  • 7.2) Moving Averages and Smoothing Techniques

  • 7.3) Forecasting Models

    • AR, MA, ARMA, ARIMA

  • 7.4) Applications in Industry

Chapter 8: Big Data Analytics

  • 8.1) Introduction to Big Data

    • 5Vs: Volume, Velocity, Variety, Veracity, Value

  • 8.2) Hadoop Ecosystem

    • HDFS, MapReduce

  • 8.3) Apache Spark Basics

  • 8.4) NoSQL Databases

    • MongoDB, Cassandra

Chapter 9: Tools and Technologies

  • 9.1) Data Analytics Tools

    • R, Python, Excel, Tableau

  • 9.2) Data Processing Libraries

    • NumPy, Pandas, Scikit-learn

  • 9.3) Data Visualization Tools

    • Power BI, Matplotlib, Seaborn

  • 9.4) Case Study with Jupyter Notebooks

Chapter 10: Applications and Case Studies

  • 10.1) Data Analytics in Business

  • 10.2) Analytics in Healthcare

  • 10.3) Financial Analytics

  • 10.4) Social Media Analytics

  • 10.5) Ethical and Legal Issues in Data Analytics

    • Data Privacy, GDPR, Bias in Data

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