Statistics For Data Science - Notes, Syllabus & Materials | YNetStudyHub
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Statistics For Data Science

Comprehensive curriculum, topic notes, practice questions, and past examination papers for Statistics For Data Science on YNetStudyHub.

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Topics & Syllabus 30

#1 Introduction to Statistics
This topic covers the basic concepts of statistics, including descriptive statistics...
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#2 Probability Theory
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#3 Data Visualization
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#4 Sampling and Estimation
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#5 Hypothesis Testing
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#6 Regression Analysis
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#7 ANOVA and Experimental Design
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#8 Time Series Analysis
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#9 Bayesian Statistics
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#10 Machine Learning and Statistics
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#11 Introduction to Statistics
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#12 Descriptive Statistics
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#13 Inferential Statistics
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#14 Probability Theory
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#15 Sampling Techniques
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#16 Correlation and Regression Analysis
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#17 Statistical Testing
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#18 Data Visualization
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#19 Bayesian Statistics
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#20 Time Series Analysis
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#21 Introduction to Statistics
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#22 Descriptive Statistics
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#23 Probability Theory
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#24 Inferential Statistics
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#25 Regression Analysis
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#26 Data Visualization
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#27 Correlation Analysis
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#28 Sampling Methods
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#29 Experimental Design
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#30 Bayesian Statistics
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Unit Outline 60h

Learning Objectives

5 objectives
  • Understand fundamental concepts of descriptive and inferential statistics.
  • Apply probability theory and sampling techniques to analyze data.
  • Develop skills in data visualization and interpretation of statistical graphics.
  • Perform regression analysis, hypothesis testing, ANOVA, and time series analysis.
  • Explore Bayesian statistics and the interplay between statistics and machine learning.

Content Outline

Preview

Unit 743: Comprehensive Statistics and Data Analysis

1. Introduction to Statistics

  • Overview of statistics and its role in data science
  • Types of data: qualitative vs quantitative
  • Measures of central tendency: mean, median, mode
  • Measures of variability: variance, standard deviation, range

2. Descriptive Statistics

  • Summarizing data sets
  • Measures of central tendency revisited
  • Measures of dispersion: variance, standard deviation
  • Graphical representations: bar charts, histograms, box plots

3. Probability Theory

  • Basic probability concepts and rules
  • Probability distributions (discrete and continuous)
  • Conditional probability and independence
  • Bayes' theorem and applications

4. Sampling and Estimation

  • Population vs sample
  • Sampling methods: simple random, stratified, cluster, systematic
  • Sampling distributions and properties
  • Point estimation and confidence intervals

5. Inferential Statistics

  • Concept of statistical inference
  • Hypothesis testing framework
    • Null and alternative hypotheses
    • Type I and Type II errors
  • Confidence intervals

6. Hypothesis Testing

  • Selecting appropriate test statistics
  • Tests for means, proportions, and variances
  • p-values and significance levels
  • Interpreting test results

7. Regression Analysis

  • Simple linear regression
  • Multiple regression analysis
  • Logistic regression overview
  • Model interpretation and diagnostics
  • Assessing model fit: R-squared, residual analysis
  • Prediction using regression models

8. Correlation Analysis

  • Measuring relationships between variables
  • Pearson correlation coefficient
  • Spearman's rank correlation coefficient
  • Interpretation and limitations

9. ANOVA and Experimental Design

  • Analysis of Variance (ANOVA)
    • One-way ANOVA
    • Two-way ANOVA
    • Factorial designs
  • Principles of experimental design
    • Control groups
    • Randomization
    • Experimental variables
  • Sources of variation in experiments

10. Time Series Analysis

  • Characteristics of time series data
  • Trend analysis
  • Seasonality and cyclic patterns
  • Autocorrelation and stationarity
  • Forecasting techniques: moving averages, exponential smoothing, ARIMA
  • Evaluating forecasting models

11. Bayesian Statistics

  • Bayesian inference fundamentals
  • Prior, likelihood, and posterior distributions
  • Bayesian updating and modeling
  • Comparison of Bayesian and frequentist approaches

12. Data Visualization

  • Importance of data visualization
  • Common visualization techniques
    • Bar charts
    • Histograms
    • Scatter plots
    • Box plots
    • Heatmaps
  • Best practices for effective communication

13. Machine Learning and Statistics

  • Intersection of statistics and machine learning
  • Model evaluation metrics: accuracy, precision, recall, F1-score
  • Cross-validation techniques
  • Bias-variance tradeoff
  • Role of statistics in model building and evaluation

Summary and Integration

  • Application of statistical techniques in data science
  • Integration of concepts through case studies and projects
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Bachelor of Science in Data Science

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