Principles of Data Science

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About This Course

Skills You’ll Get

Interactive Lessons

16+ Interactive Lessons |

Hands-On Labs

33+ LiveLab | 33+ Video tutorials | 01:06+ Hours

Lesson Plan

Introduction

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

Data Science Terminology

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

Types of Data

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

The Five Steps of Data Science

Introduction to data science
Exploring the data
Summary

Basic Mathematics

Basic symbols and terminology
Linear algebra
Summary

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

Advanced Probability

Bayesian ideas revisited
Random variables
Summary

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

Advanced Statistics

Understanding point estimates
Sampling distributions
Confidence intervals
Hypothesis tests
Summary

Communicating Data

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

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

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

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

Introduction to Transfer Learning and Pre-Trained Models

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

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

AI Governance

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

Navigating Real-World Data Science Case Studies in Action

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

Hands-on LAB Activities

Data Science Terminology

Extracting and Analyzing Cashtags in Tweets

Types of Data

Exploring CSV Data
Analyzing Temperature Data Using Statistical Methods

The Five Steps of Data Science

Performing Time-Based Analysis
Mastering Data Insights

Basic Mathematics

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

Impossible or Improbable – A Gentle Introduction to Probability

Simulating Random Rolls and Calculating Probabilities
Generating and Analyzing Random Data

Advanced Probability

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

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

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

Communicating Data

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

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

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

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

Introduction to Transfer Learning and Pre-Trained Models

Fine-Tuning a Pre-Trained Model for Sentiment Analysis

Mitigating Algorithmic Bias and Tackling Model and Data Drift

Generating and Visualizing Word Data

AI Governance

Interpreting Sentiment Analysis Predictions with LIME

Navigating Real-World Data Science Case Studies in Action

Visualizing Distributions and Encoding Categorical Variables

Lab

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Principles of Data Science