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Principles of Data Science

(PRIN-DS.AJ1) / ISBN : 978-1-64459-630-2
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About This Course

Skills You’ll Get

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1

Introduction

  • Who is this course for?
  • What this course covers
  • To get the most out of this course
  • Conventions used
2

Data Science Terminology

  • What is data science?
  • The data science Venn diagram
  • Some more terminology
  • Data science case studies
  • Summary
3

Types of Data

  • Structured versus unstructured data
  • The four levels of data
  • Summary
  • Questions and answers
4

The Five Steps of Data Science

  • Introduction to data science
  • Exploring the data
  • Summary
5

Basic Mathematics

  • Basic symbols and terminology
  • Linear algebra
  • Summary
6

Impossible or Improbable – A Gentle Introduction to Probability

  • Basic definitions
  • Bayesian versus frequentist
  • How to utilize the rules of probability
  • Introduction to binary classifiers
  • Summary
7

Advanced Probability

  • Bayesian ideas revisited
  • Random variables
  • Summary
8

What Are the Chances? An Introduction to Statistics

  • What are statistics?
  • How do we obtain and sample data?
  • How do we measure statistics?
  • The empirical rule
  • Summary
9

Advanced Statistics

  • Understanding point estimates
  • Sampling distributions
  • Confidence intervals
  • Hypothesis tests
  • Summary
10

Communicating Data

  • Why does communication matter?
  • Identifying effective visualizations
  • When graphs and statistics lie
  • Verbal communication
  • Summary
11

How to Tell if Your Toaster is Learning – Machine Learning Essentials

  • Introducing ML
  • Types of ML
  • Predicting continuous variables with linear regression
  • Summary
12

Predictions Don’t Grow on Trees, or Do They?

  • Performing naïve Bayes classification
  • Understanding decision trees
  • Diving deep into UL
  • Feature extraction and PCA
  • Summary
13

Introduction to Transfer Learning and Pre-Trained Models

  • Understanding pre-trained models
  • Different types of TL
  • TL with BERT and GPT
  • Summary
14

Mitigating Algorithmic Bias and Tackling Model and Data Drift

  • Understanding algorithmic bias
  • Sources of algorithmic bias
  • Measuring bias
  • Consequences of unaddressed bias and the importance of fairness
  • Mitigating algorithmic bias
  • Bias in LLMs
  • Emerging techniques in bias and fairness in ML
  • Understanding model drift and decay
  • Mitigating drift
  • Summary
15

AI Governance

  • Mastering data governance
  • Navigating the intricacy and the anatomy of ML governance
  • A guide to architectural governance
  • Summary
16

Navigating Real-World Data Science Case Studies in Action

  • Introduction to the COMPAS dataset case study
  • Text embeddings using pretrainedmodels and OpenAI
  • Summary

1

Data Science Terminology

  • Extracting and Analyzing Cashtags in Tweets
2

Types of Data

  • Exploring CSV Data
  • Analyzing Temperature Data Using Statistical Methods
3

The Five Steps of Data Science

  • Performing Time-Based Analysis
  • Mastering Data Insights
4

Basic Mathematics

  • Computing Similarities with Set Operations
  • Working with Vectors and Matrices
  • Performing Matrix Operations and Analyzing Execution Time
5

Impossible or Improbable – A Gentle Introduction to Probability

  • Simulating Random Rolls and Calculating Probabilities
  • Generating and Analyzing Random Data
6

Advanced Probability

  • Using Probability to Examine Survival Factors in a Dataset
  • Creating and Visualizing the Normal Distribution
  • Simulating Dice Rolls and Analyzing Statistical Averages
7

What Are the Chances? An Introduction to Statistics

  • Evaluating the Central Tendency and Variability of Data
  • Analyzing A/B Testing Results
  • Applying Z-Scores to Data Analysis
8

Advanced Statistics

  • Estimating Break Lengths and Demographic Proportions
  • Converting Bimodal Data to a Normal Distribution Using Sampling
  • Calculating and Interpreting Confidence Intervals
  • Testing Hypotheses: Type I and II Errors
9

Communicating Data

  • Comparing Distribution Metrics with Histograms and Box Plots
  • Visualizing Data with Scatter and Bar Charts
  • Quantifying Data Relationships Through Correlation Analysis
10

How to Tell if Your Toaster is Learning – Machine Learning Essentials

  • Predicting Alcohol Consumption Using Regression Models
  • Preparing Data for Regression and Visualization
11

Predictions Don’t Grow on Trees, or Do They?

  • Processing and Analyzing SMS Data
  • Transforming Data and Creating Decision Tree Models
  • Clustering Data Using K-Means
  • Optimizing Models Using Feature Selection and PCA
12

Introduction to Transfer Learning and Pre-Trained Models

  • Fine-Tuning a Pre-Trained Model for Sentiment Analysis
13

Mitigating Algorithmic Bias and Tackling Model and Data Drift

  • Generating and Visualizing Word Data
14

AI Governance

  • Interpreting Sentiment Analysis Predictions with LIME
15

Navigating Real-World Data Science Case Studies in Action

  • Visualizing Distributions and Encoding Categorical Variables

Principles of Data Science

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