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DATA SCIENCE INFINITY
Welcome - let's get you set up for success!
Welcome to DATA SCIENCE INFINITY! (2:29)
Join the private Slack channel - and start getting *dedicated* support & guidance
How to get the most out of the private Slack channel
What makes a GREAT Data Scientist? (2:57)
Self Confidence & Imposter Syndrome (3:46)
Course Overview (3:19)
Get The Teachable App!
Your DSI Downloadable Resources Library
SQL for Data Science
Introduction to SQL for Data Science & Analytics (11:16)
3 Ways To Code SQL On The Course! (1:25)
Getting Setup With SQL Workbench/J - Check Your Java Version (4:54)
Getting Setup With SQL Workbench/J - Installation (14:12)
Getting Setup With SQL Workbench/J - Take A Look Around (7:35)
The DSI In-Browser SQL Editor (4:47)
Let's Take A Look At Our Data (ABC Grocery) (10:46)
The SELECT statement (PRACTICAL) (9:55)
The SELECT Statement (Test Yourself)
Applying selection conditions using the WHERE statement (PRACTICAL) (8:52)
The WHERE Statement (Test Yourself)
Aggregation functions and the GROUP BY statement (PRACTICAL) (10:08)
Aggregation functions and the GROUP BY statement (Test Yourself)
Conditional rules using CASE WHEN (PRACTICAL) (9:17)
Conditional rules using CASE WHEN (Test Yourself)
The use of WINDOW functions (PRACTICAL) (12:29)
The use of WINDOW functions (Test Yourself)
Joining tables using JOIN (PRACTICAL) (19:52)
Joining tables using JOIN (Test Yourself)
The only SQL Joins Cheatsheet you'll ever need (image)
Stacking data using UNION and UNION ALL (PRACTICAL) (4:48)
Executing multiple queries using TEMP TABLES and CTE (10:08)
Executing multiple queries using TEMP TABLES and CTE (Test Yourself)
Tips & Tricks 01 - Sub-Queries (Practical) (4:30)
Tips & Tricks 02 - Using Lag & Lead (Practical) (7:01)
Tips & Tricks 03 - Rounding Numeric Data (Practical) (2:10)
Tips & Tricks 04 - Selecting Random Samples (Practical) (1:50)
Tips & Tricks 05 - Working With Dates (Practical) (3:32)
Tips & Tricks 06 - Working with Text (Practical) (4:48)
Introduction To The Real-World SQL Tests (1:45)
Real World SQL Test 01 - Questions
Real World SQL Test 01 - Solutions (13:47)
Real World SQL Test 02 - Questions
Real World SQL Test 02 - Solutions (15:53)
Real World SQL Test 03 - Questions
Real World SQL Test 03 - Solutions (16:24)
Downloadable PDF: 10 Common SQL Theory Questions (With Answers)
Bonus Weekly SQL Challenges (Return To These To Keep Your Skills Sharp!)
Weekly SQL Challenge 01
Weekly SQL Challenge 02
Weekly SQL Challenge 03
Weekly SQL Challenge 04 (From David Langer)
Weekly SQL Challenge 05
Weekly SQL Challenge 06
Weekly SQL Challenge 07 (From Chris Perry)
Weekly SQL Challenge 08
Weekly SQL Challenge 09
Weekly SQL Challenge 10
Weekly SQL Challenge 11
Weekly SQL Challenge 12
Data Viz With Tableau!
Introducing Tableau (and what we'll learn!) (2:31)
Get your hands on the data!
Installing Tableau (6:02)
Importing Data Into Tableau (8:51)
Our First Data Visualisation! (15:21)
Measures, Dimensions, and Marks (10:23)
Filters (10:01)
Pages (7:24)
Let's Create Our Next Viz (Part 1) (14:19)
Let's Create Our Next Viz (Part 2) (10:52)
Calculated Fields (Part 1) (12:03)
Calculated Fields (Part 2) (10:47)
Calculated Fields (Part 3) (8:32)
Dashboard Requirements From Our Client! (2:34)
The Top 10 Earthquakes Table (7:17)
Percent Of Earthquakes By Location (5:20)
Creating Our Amazing Dashboard (Part 1) (11:48)
Creating Our Amazing Dashboard (Part 2) (7:52)
Creating Our Amazing Dashboard (Part 3) (3:24)
Download The Logo Image
Creating Our Amazing Dashboard (Part 4) (7:52)
Python for Data Science 01: Base Python
Introduction to Python for Data Science (8:56)
Installing Anaconda (PRACTICAL) (7:21)
Introduction to Spyder (PRACTICAL) (5:09)
Introducing VARIABLES and DATA TYPES (PRACTICAL) (10:04)
Assigning our data to VARIABLES (PRACTICAL) (4:50)
A deeper look at working with STRINGS (PRACTICAL) (16:19)
A deeper look at working with NUMBERS (PRACTICAL) (7:02)
Introduction to DATA STRUCTURES (PRACTICAL) (1:06)
Data Structure 1: LISTS (PRACTICAL) (17:41)
Data Structure 2: TUPLES (PRACTICAL) (7:04)
Data Structure 3: SETS (PRACTICAL) (10:57)
Data Structure 4: DICTIONARIES (PRACTICAL) (11:28)
Adding smarts to our code using CONDITIONAL STATEMENTS (PRACTICAL) (13:03)
Going loopy with FOR LOOPS (PRACTICAL) (12:57)
Loop de Loop with WHILE LOOPS (PRACTICAL) (5:59)
Receiving information using the INPUT FUNCTION (PRACTICAL) (5:30)
** MINI PROJECT ** Building a Number Guessing Game (PRACTICAL) (13:28)
Getting func'y with FUNCTIONS (PRACTICAL) (8:40)
** MINI PROJECT ** Finding Prime Numbers (PRACTICAL) (22:34)
A note on using pop() with sets in Python
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Get to know the very useful LIST COMPREHENSION (PRACTICAL) (9:04)
Handling Exceptions with....EXCEPTION HANDLING (PRACTICAL) (9:00)
Where to from here... (2:15)
Python for Data Science 02: Numpy
Introduction to Numpy (3:24)
Creating Numpy Arrays (PRACTICAL) (15:02)
Numpy Array Operations (PRACTICAL) (11:46)
Manipulating Numpy Arrays (PRACTICAL) (14:11)
** MINI PROJECT ** Calculating Planet Volumes (PRACTICAL) (10:31)
** MINI PROJECT ** Image Manipulation using Numpy (Get the data)
** MINI PROJECT ** Image Manipulation (PRACTICAL) (17:06)
Python for Data Science 03: Pandas
Introduction To Pandas (1:36)
Accessing & Downloading The Data
Creating Pandas DataFrames & Importing Data (PRACTICAL) (11:50)
Exploring & Understanding DataFrame Data (PRACTICAL) (14:23)
Accessing Specific Columns In Our DataFrame (PRACTICAL) (7:51)
Adding & Dropping Columns In Our DataFrame (PRACTICAL) (9:34)
Adding Columns Using Map, Replace, And Apply (PRACTICAL) (13:54)
Sorting & Ranking Data (PRACTICAL) (12:40)
Selecting Rows & Columns using LOC & ILOC (PRACTICAL) (19:35)
Renaming Columns (PRACTICAL) (8:43)
Joining & Merging DataFrames (PRACTICAL) (14:48)
Aggregating Data Using GROUPBY (PRACTICAL) (18:58)
Pivoting A DataFrame (PRACTICAL) (11:33)
Dealing With Missing Values (PRACTICAL) (21:32)
Dealing With Duplicate Data (PRACTICAL) (9:05)
Creating Charts And Plots Using Pandas (PRACTICAL) (15:52)
Exporting Data (PRACTICAL) (14:23)
Python for Data Science 04: Matplotlib
Introduction To Matplotlib (1:22)
Our First Plot (PRACTICAL) (8:56)
Formatting Our Plot: Features (PRACTICAL) (9:00)
Formatting Our Plot: Colours & Styles (PRACTICAL) (13:52)
Working With Subplots (PRACTICAL) (8:26)
Let's Grab Some Height & Weight Data To Use!
Creating & Refining A Histogram (PRACTICAL) (9:23)
Creating & Refining A Scatter Plot (PRACTICAL) (8:44)
Enhancing Our Plots Using Visual Aids (PRACTICAL) (8:03)
Adding Text To Our Plots (PRACTICAL) (17:08)
Saving Plots (PRACTICAL) (3:33)
An intuitive & fun adventure through Statistics for Data Science!
Section Introduction (1:07)
The Different "Types" Of Data (5:23)
Quiz Time! Statistics - Types Of Data
Understanding Distributions & Standard Deviation (14:50)
Normal Distributions + Z-Scores (6:15)
The 6 Distributions You Need To Know! (9:24)
Quiz Time! Statistics - Distributions
The Central Limit Theorem (9:36)
Getting Confident With Confidence Intervals (9:32)
Quiz Time! Statistics - Dealing With Uncertainty
Introduction To Hypothesis Testing & P-Values (12:41)
Hypothesis Testing: The One Sample T-Test (13:47)
Hypothesis Testing: The Independent Samples T-Test (14:02)
Hypothesis Testing: The Paired T-Test (9:43)
Hypothesis Testing: The Chi-Square Test Of Independence (12:09)
Note: Chi-Square Test vs. Z-Test For Proportions
Quiz Time! Statistics - Hypothesis Testing & P-Values
Jupyter Notebook for Data Science
An overview of Jupyter Notebook (12:53)
Github for Data Science
Overview of Git & Github (9:03)
Getting Started - Branches, Pull Requests, and Merges! (PRACTICAL) (17:45)
Forking a Repository (PRACTICAL) (6:27)
Pushing and Pulling between your local PC and GitHub (PRACTICAL) (13:33)
Downloadable PDF: The Essential Github Lingo for Data Science
Get The ABC Grocery Data!
Download The ABC Grocery Data
AB Testing - Theory & Application
What is AB Testing? (2:28)
Our Task for ABC Grocery! (2:09)
Getting The Data
Chi-Square Test for ABC Grocery (PRACTICAL) (18:32)
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Let's code up a One Sample T-Test (10:18)
Let's code up an Independent Samples T-Test (9:02)
Let's code up a Paired Sample T-Test (7:50)
Introduction to Machine Learning
Introduction to Machine Learning in Data Science (16:52)
Overview of Supervised Learning (13:12)
Overview of Unsupervised Learning (7:49)
Introducing scikit-learn for Machine Learning in Python (9:38)
Preparing & Cleaning Data for Machine Learning
Data Cleaning & Prep For ML - Introduction (1:01)
A Checklist for Data Cleaning & Preparation (7:17)
Dealing with Missing Values (THEORY) (8:47)
Dealing with Missing Values - Pandas (PRACTICAL) (12:42)
Dealing with Missing Values - SimpleImputer (PRACTICAL) (11:05)
Dealing with Missing Values - KNNImputer (PRACTICAL) (11:49)
Dealing with Categorical Variables (THEORY) (8:18)
Dealing with Categorical Variables - One Hot Encoder (PRACTICAL) (10:50)
Dealing with Outliers (THEORY) (8:55)
Dealing with Outliers (PRACTICAL) (13:34)
Feature Scaling for Machine Learning (THEORY) (9:19)
Feature Scaling for Machine Learning (PRACTICAL) (8:18)
Feature Selection in Machine Learning (THEORY) (12:04)
Feature Selection in Machine Learning - Getting the Sample Data
Feature Selection in Machine Learning - Correlation Matrix (PRACTICAL) (4:26)
Feature Selection in Machine Learning - Univariate Testing (PRACTICAL) (17:52)
Feature Selection in Machine Learning - RFECV (PRACTICAL) (13:48)
Model Validation & Over-fitting (THEORY) (8:53)
Model Validation & Over-fitting (PRACTICAL) (18:06)
Quiz Time! ML Data Cleaning & Preparation
Machine Learning for Regression Tasks
Introduction to Machine Learning for Regression (4:02)
Our Regression Task for ABC Grocery (2:39)
Our Regression Task for ABC Grocery - Getting The Data
Download The Sample Regression Data (For Later)
Our Regression Task for ABC Grocery - Creating The Data (16:16)
Linear Regression
High Level Overview (5:09)
Basic Code Stencil (PRACTICAL) (8:57)
The Formula for a Straight Line (ADVANCED THEORY) (9:00)
Finding the "best" line using Least Squares (ADVANCED THEORY) (12:57)
Evaluating Model Fit using R-Squared (ADVANCED THEORY) (11:32)
Multiple Input Variables (ADVANCED THEORY) (4:51)
Adjusted R-Squared (ADVANCED THEORY) (5:40)
Understanding P-Values (ADVANCED THEORY) (6:33)
Advanced Code Template (PRACTICAL) (29:31)
Quiz Time! Linear Regression Concepts
Decision Trees for Regression (Regression Trees)
High Level Overview (10:40)
Basic Code Stencil (PRACTICAL) (10:03)
Splitting Criteria (ADVANCED THEORY) (13:46)
Stopping Criteria (ADVANCED THEORY) (4:04)
Evaluating Model Performance (ADVANCED THEORY) (4:06)
Advanced Code Template (PRACTICAL) (21:20)
Quiz Time! Regression Tree Concepts
Random Forests for Regression
High Level Overview (9:58)
Basic Code Stencil (PRACTICAL) (9:16)
Feature Importance (ADVANCED THEORY) (5:40)
Evaluating Model Performance (ADVANCED THEORY) (4:15)
Advanced Code Template (PRACTICAL) (22:33)
Predicting The Missing Loyalty Scores (PRACTICAL) (8:53)
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Quiz Time! Random Forest (Regression) Concepts
Machine Learning for Classification Tasks
Introduction to Machine Learning for Classification (4:26)
Our Task for ABC Grocery (3:56)
Getting the sample data
Logistic Regression
High Level Overview (10:38)
Basic Code Stencil (PRACTICAL) (13:09)
Probability, Odds, and log(Odds) (ADVANCED THEORY) (6:50)
The Formula for a Sigmoid Curve (ADVANCED THEORY) (5:42)
Maximum Likelihood Estimation (ADVANCED THEORY) (8:26)
Evaluating Classification Accuracy (ADVANCED THEORY) (7:09)
Advanced Evaluation Techniques (ADVANCED THEORY) (11:12)
Changing the Classification Threshold (ADVANCED THEORY) (10:32)
Advanced Code Template (PRACTICAL) (30:49)
Quiz Time! Logistic Regression Concepts
Decision Trees for Classification (Classification Trees)
High Level Overview (7:15)
Basic Code Stencil (PRACTICAL) (9:01)
Splitting Criteria (ADVANCED THEORY) (12:05)
Stopping Criteria (ADVANCED THEORY) (4:10)
Evaluating Classification Accuracy (ADVANCED THEORY) (6:31)
Advanced Code Template (PRACTICAL) (16:44)
Quiz Time! Classification Tree Concepts
Random Forests for Classification
High Level Overview (9:17)
Basic Code Stencil (PRACTICAL) (6:35)
Feature Importance (ADVANCED THEORY) (4:40)
Evaluating Classification Accuracy (ADVANCED THEORY) (6:03)
Advanced Code Template (PRACTICAL) (15:40)
Quiz Time! Random Forest (Classification) Concepts
K-Nearest-Neighbours (KNN) for Classification
High Level Overview (5:46)
Basic Code Stencil (PRACTICAL) (6:30)
Measuring Distances In Multi-Dimensional Space (ADVANCED THEORY) (6:16)
The Importance Of Feature Scaling (ADVANCED THEORY) (5:07)
What Value For "K"? (ADVANCED THEORY) (4:26)
Advanced Code Template (PRACTICAL) (22:46)
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Quiz Time! KNN Concepts
Advanced applications of scikit-learn
Grid Search for Hyperparameter Tuning (THEORY) (6:15)
Grid Search for Hyperparameter Tuning (PRACTICAL) (10:41)
Pipelines - getting the sample data
Automating workflows with Pipelines (PRACTICAL) (23:27)
Unsupervised Learning: K-Means Clustering
High Level Overview (7:58)
Getting The Sample Data
Basic Code Stencil (PRACTICAL) (13:02)
Measuring Distances In Multi-Dimensional Space (ADVANCED THEORY) (5:58)
The Importance Of Feature Scaling (ADVANCED THEORY) (5:17)
What Value For "K"? (ADVANCED THEORY) (7:31)
Our Task For ABC Grocery (2:23)
Advanced Code Template (PRACTICAL) (26:18)
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Quiz Time! K-Means Concepts
Unsupervised Learning: Principal Component Analysis (PCA)
High Level Overview (8:26)
Our Task For ABC Grocery (2:30)
Getting The Data
Advanced Code Template (PRACTICAL) (21:02)
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Quiz Time! PCA Concepts
Unsupervised Learning: Association Rule Learning
High Level Overview (12:24)
Our Task For ABC Grocery (1:30)
Getting The Data
Advanced Code Template (PRACTICAL) (21:54)
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Quiz Time! Association Rule Learning
Causal Impact Analysis
High Level Overview (11:24)
Our Task For ABC Grocery (1:36)
Advanced Code Template (PRACTICAL) (18:06)
Add the pre-built project to your portfolio!
Quiz Time! Causal Impact Analysis
Machine Learning Model Deployment
Overview of ML Model Deployment & The Important Considerations We Need To Make! (8:17)
Getting & Setting Up The Required Files For This Section
An Overview Of Streamlit (9:57)
Let's Get Streamlit Installed! (6:35)
Coding Our Web-App Part 1 (PRACTICAL) (15:29)
Coding Our Web-App Part 2 (PRACTICAL) (18:10)
Deploying Our Web-App Part 1 (PRACTICAL) (4:22)
Deploying Our Web-App Part 2 (PRACTICAL) (11:53)
Set yourself apart: The art of turning business problems into Data Science solutions
Introduction (3:18)
Getting to the core of the business problem (10:55)
Deciding on the right Data Science solution (12:39)
Downloadable PDF - The 13 Questions You Must Ask!
Acing The Data Science & Analytics Hiring Process!
The Hiring Process - Module Introduction (2:18)
The 3 Stages of the Hiring Process (4:38)
Optimizing Your LinkedIn Profile (7:50)
Resumes & CVs - Introduction (2:02)
Resumes & CVs - What Should Yours Look Like? (+ Template Download) (1:43)
Resumes & CVs - Name + Headline + Contact Details (3:49)
Resumes & CVs - Motivation Section (7:18)
Resumes & CVs - Skills & Tools Section (3:38)
Resumes & CVs - Work Experience Section (7:27)
Resumes & CVs - Projects Section (3:57)
Resumes & CVs - Education Section (2:01)
Resumes & CVs - Courses And Certifications Section (3:17)
Resumes & CVs - Final Notes (3:12)
Projects & Portfolios - Introduction (1:16)
Projects & Portfolios - Do You Need One? (1:41)
Projects & Portfolios - What Projects To Include (2:33)
Projects & Portfolios - How To Structure A Project (5:03)
Applying For Roles - What Roles To Apply For (6 Tips) (3:58)
Applying For Roles - Meeting With A Recruiter Or HR (3:25)
Interviewing - An Overview of Data Interviews (3:53)
Interviewing - The CRAIG System - Overview (4:14)
Interviewing - The CRAIG System - Apply It To Your Projects (13:18)
Interviewing - The CRAIG System - Downloadable PDF Help Sheet
Interviewing - Building Rapport With Your Interviewer (2:47)
Interviewing - Answering Questions You Don't Know (3:43)
Interviewing - Answering The "Biggest Mistake" Question (2:48)
Interviewing - Tips For Take Home Assignments (7:00)
Interviewing - Tips For Coding Tests (6:24)
Interviewing - Questions To Ask Your Interviewer
Interviewing - Reframing Rejections (2:28)
LinkedIn Hack - Filtering Jobs Posted In Last Hour (1:51)
LinkedIn Hack - Finding Posts And People Who Are Hiring (2:37)
LinkedIn Hack - Template For Email/LinkedIn Introduction
Introduction To Deep Learning
Section Overview (3:40)
Deep Learning - Artificial Neural Networks
Introduction To Artificial Neural Networks (20:31)
Weights & Biases (17:15)
Activation Functions (23:16)
Calculating Loss (19:30)
Introduction To Back Propagation & Gradient Descent (18:56)
The Relationship Between Network Parameters & Loss (9:41)
Connecting The Dots Using The Chain Rule (5:20)
Let's Talk About Derivatives (12:49)
The Chain Rule - How Does It Work? (7:46)
Back Propagation - A Worked Example (37:18)
ANN's - Let's Summarise What We've Learned (4:40)
Good To Know: Global vs. Local Minima (4:04)
Good To Know: Optimizers (7:02)
Introduction To Keras (11:31)
Getting The Data (4:49)
Installs & Versioning (6:03)
Predicting Video Game Player Success - Part 1 (PRACTICAL) (2:45)
Predicting Video Game Player Success - Part 2 (PRACTICAL) (12:30)
Predicting Video Game Player Success - Part 3 (PRACTICAL) (14:59)
Predicting Video Game Player Success - Part 4 (PRACTICAL) (17:47)
Predicting Video Game Player Success - Part 5 (PRACTICAL) (10:15)
Deep Learning - Convolutional Neural Networks
CNN - Our Task For ABC Grocery (2:26)
Introduction To Convolutional Neural Networks (5:22)
Images: What Does A Computer See? (7:11)
CNN vs. ANN (7:15)
CNN: The Basics (11:47)
Convolutional Layers (9:04)
Pooling Layers (9:47)
Dense Layers (6:00)
Fruit Classification Project - Introduction (2:45)
Fruit Classification Project - Part 1 (PRACTICAL) (13:53)
Fruit Classification Project - Part 2 (PRACTICAL) (17:13)
Fruit Classification Project - Part 3 (PRACTICAL) (16:06)
Fruit Classification Project - Part 4 (PRACTICAL) (20:15)
Fruit Classification Project - Part 5 (PRACTICAL) (22:23)
Taking On Over-Fitting Using Dropout (8:40)
Fruit Classification Project - Dropout (PRACTICAL) (15:48)
Image Augmentation (10:21)
Fruit Classification Project - Image Augmentation (PRACTICAL) (15:24)
Transfer Learning (5:33)
Fruit Classification Project - Transfer Learning (PRACTICAL) (24:22)
Fruit Classification Project - Keras Tuner Part 1 (PRACTICAL) (18:14)
Fruit Classification Project - Keras Tuner Part 2 (PRACTICAL) (13:34)
Fruit Classification Project - Keras Tuner Part 3 (PRACTICAL) (8:27)
Good To Know: Batch Normalisation (3:55)
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Deep Learning - Image Search Engine
Image Search Engine - Our Task For ABC Grocery (5:23)
Image Search Engine - Part 1 (PRACTICAL) (11:19)
Image Search Engine - Part 2 (PRACTICAL) (4:53)
Image Search Engine - Part 3 (PRACTICAL) (9:07)
Image Search Engine - Part 4 (PRACTICAL) (21:10)
Add the pre-built project to your portfolio!
Deep Learning Resource
How To Read A Research Paper
Generative AI + LLMs + Agentic AI
GenAI Module Introduction (2:47)
A Note On The Transformers Tutorials
Transformers Under The Hood 01: Introduction (8:04)
Transformers Under The Hood 02: Inputs + Tokenisation + Positional Encoding (12:18)
Transformers Under The Hood 03: The Self-Attention Block (24:20)
Transformers Under The Hood 04: Outputs + Prediction (5:34)
Transformers Under The Hood 05: Summary + Extra Notes (9:51)
Traditional Automation vs. AI Automation vs. AI Agents (13:58)
Prompting 01: Prompts Within A GenAI System (4:37)
Prompting 02: The RICECO Framework (5:12)
RAG 01: An Overview Of RAG (11:09)
RAG 02: Document Splitting & Chunking (4:32)
RAG 03: RAG vs. Fine-Tuning (2:11)
RAG Project 01: Introduction & Overview (1:48)
RAG Project 02: Setup & Admin (4:01)
RAG Project 03: Getting Set Up With OpenAI (6:30)
RAG Project 04: An Overview Of LangChain (2:34)
RAG Project 05: Getting Set Up With LangSmith (2:52)
RAG Project 06: Setting Up Our GenAI Virtual Environment (3:51)
RAG Project 07: Getting The Data
RAG Project 08: Hands-On Coding 01 (13:47)
RAG Project 09: Hands-On Coding 02 (12:17)
RAG Project 10: Hands-On Coding 03 (9:26)
RAG Project 11: Hands-On Coding 04 (13:19)
RAG Project 12: Hands-On Coding 05 (18:13)
SQL Agent Project 01: Introduction & Overview (1:53)
SQL Agent Project 02: Setup & Admin (3:00)
SQL Agent Project 03: Hands-On Coding (24:33)
Cloud Fundamentals Using AWS
Course Introduction - What We'll Cover! (3:39)
An Overview Of Cloud Computing (4:13)
Important - Read Before Starting The Course
Good To Know - AWS Regions & Availability Zones (6:18)
Quiz Time: AWS Regions & Availability Zones
Creating & Configuring An AWS Account - Important Note
Creating & Configuring An AWS Account (7:00)
Setting Up A Billing Alarm (7:25)
Introduction To IAM (Identity & Access Management) (1:15)
IAM Users (4:54)
IAM User Groups (3:44)
IAM Roles (6:43)
IAM Policies (8:19)
Creating an IAM User & IAM User Group (Part 1) (8:22)
Creating an IAM User & IAM User Group (Part 2) (9:35)
Quiz Time! IAM - Key Concepts
An Overview of S3 (Simple Storage Service) (7:55)
Getting The Data For S3
Creating & Configuring Our First S3 Bucket! (17:30)
Quiz Time! S3 - Key Concepts
An Overview Of AWS Lambda (Part 1) (4:33)
An Overview Of AWS Lambda (Part 2) (9:21)
Creating Our First Lambda Function (Download Resources)
Creating Our First Lambda Function (15:18)
Introducing Lambda Layers (5:28)
Lambda Layers For Python Libraries + CloudShell (25:16)
Lambda Layers For Helper Scripts (11:15)
Lambda Layers For Full Deployment Packages (23:01)
Project: Lambda + S3 To Resize Images (Part 1) (1:50)
Project: Lambda + S3 To Resize Images (Part 2) (2:35)
Project: Lambda + S3 To Resize Images (Part 3) (9:16)
Project: Lambda + S3 To Resize Images (Part 4) (11:50)
Project: Lambda + S3 To Resize Images (Part 5) (14:57)
Quiz Time! Lambda - Key Concepts
Introducing AWS EC2 (Elastic Compute Cloud) (11:53)
EC2: Setting Up Our IAM Permissions (2:30)
EC2: Creating Our First Instance (17:17)
EC2: Connecting To Our Instance Using Instance Connect (7:28)
EC2: Connecting To Our Instance Using A Remote Desktop (9:35)
EC2: Connecting Via SSH (Mac & Windows)
Quiz Time! EC2 - Key Concepts
Introducing AWS SageMaker For Machine Learning (1:18)
Important - Cost Considerations For AWS SageMaker (1:32)
SageMaker: Setting Up Our IAM Permissions (3:29)
SageMaker: Creating & Configuring A SageMaker Domain (6:20)
SageMaker: An Overview of SageMaker Studio (7:20)
SageMaker: Cost Overview For SageMaker Canvas (7:04)
SageMaker: An Overview Of SageMaker Canvas (7:44)
SageMaker: Download The Loyalty Prediction Data
SageMaker: A Look At Our Loyalty Prediction Data (3:47)
SageMaker: Training Our Loyalty Prediction Model (29:49)
SageMaker: Connecting Python To AWS With boto3 (15:21)
SageMaker: An Overview Of Service Quotas (6:05)
SageMaker: Deploying Our Loyalty Prediction Model (21:19)
SageMaker: AutoML - Important Note
SageMaker: Training Our Loyalty Prediction Model Using AutoML (18:00)
SageMaker Project: Image Classification Task Using SageMaker Canvas (17:56)
SageMaker: Cleaning Up Our Canvas Resources (Important) (10:03)
SageMaker: Canvas Cost Check (6:25)
SageMaker: Notebook Instance Project - Get The Data + Notebook Files
SageMaker: Notebook Instance Project - Setup + Data Upload (7:27)
SageMaker: Notebook Instance Project - Data Prep + Preprocessing (16:22)
SageMaker: Notebook Instance Project - Training The Model (17:51)
SageMaker: Notebook Instance Project - Deploying The Model (13:30)
SageMaker: Notebook Instance Project - Resource Clean Up (Important) (4:21)
Good To Know: SageMaker Notebook Instance Costs
An Overview Of AWS Databases (11:05)
Setting Up Our IAM Permissions For RDS (1:29)
Creating An RDS Database - Download The Sample Dataset
Creating An RDS Database + Uploading Data + Querying The Data With SQL (24:17)
Setting Up Our IAM Permissions For DynamoDB (1:34)
Creating A DynamoDB Table + Uploading Data + Querying Data (21:24)
Programmatically Working With Our DynamoDB Table (13:48)
Quiz Time! AWS Databases
Final Cost Check (3:08)
Admin
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Extension: Data Storytelling (Gilbert Eijkelenboom)
Data Storytelling Mini-Course Introduction (Andrew) (1:27)
The Presentation I Messed Up (Gilbert Introduction) (6:25)
Exercise: Spot The Presentation Mistakes (1:55)
The Power Of The Story - My Cousin Who Smoked Cigarettes (2:16)
The G.A.M.E Framework (2:49)
The G.A.M.E Framework (Downloadable PDF)
The Importance Of Knowing Your Audience (2:01)
The Importance Of Knowing Your Audience (Downloadable PDF)
Getting Audience Buy-In (The IKEA Effect) (2:14)
What Makes A Good Story - Introducing The A.B.T Framework (11:08)
Create Your A.B.T Story (1:30)
Create Your A.B.T Story (PDF Workbook)
How To Visualize Your A.B.T Story (3:40)
Extension: Docker Fundamentals (Andreas Kretz)
Docker Course Introduction (Andrew) (1:05)
Docker Course Overview (Andreas) (3:17)
Docker vs Virtual Machines (6:32)
Key Docker Terminology (6:05)
Docker Desktop - Installation Instructions (4:19)
Pulling Images & Running Containers In CLI (6:44)
CLI Cheat Sheet (3:48)
Docker Compose Explained (6:44)
Building Our First Simple Docker Image (6:37)
Building An Image Requiring Dependancies (5:14)
Using The DockerHub Image Registry (4:34)
Understanding Image Layers (8:04)
Deployment Of Containers In Production (5:56)
Security Best Practices (4:19)
Managing Docker Images & Containers With Portainer (4:14)
Docker Course Conclusion (Andreas) (2:20)
Docker Course Conclusion (Andrew) (2:03)
DSI Docker Mini-Project: Get The Files
DSI Docker Mini-Project: Code Overview (6:03)
DSI Docker Mini-Project: Setting Everything Up (6:00)
DSI Docker Mini-Project: Building Our Docker Image Locally (3:48)
DSI Docker Mini-Project: Building & Pushing Our Docker Image To DockerHub (6:00)
DATA SCIENCE INFINITY Downloadable Resource Library
SQL: Introduction To SQL
SQL: Joins (A Guide For All Brains)
SQL: Order Of Execution
SQL: What To Expect In SQL Tests
SQL: 10 Common Theory Questions & Answers
Python: List Methods
Python: Set Methods
Python: Dictionary Methods
Python: Introduction To Numpy
Statistics: Four Types Of Data
Statistics: Common Statistical Distributions
Statistics: Common Hypothesis Tests
Statistics: The Central Limit Theorem
Machine Learning: AI vs. ML vs. DL vs. DS
Machine Learning: Supervised Learning vs. Unsupervised Learning
Machine Learning: Data Preparation & Cleaning
Machine Learning: Assessing Classification Accuracy
Machine Learning: Principal Components Analysis
Machine Learning: Linear Regression
Machine Learning: K-Means
Machine Learning: Precision vs. Recall
Deep Learning: Activation Functions
Career: 5 Conquer Imposter Syndrome In Data Science
Career: 13 Questions For Successful Data Science Projects
Interviewing: Questions To Ask Your Interviewer
Other Resources
Optimise Your Data Science Job Search - My Discussion with Kate McDermott (Recruitment Manager) (70:28)
The Skills Required To Get Into Data Science - My Discussion with Kate Strachnyi (57:29)
Insider Insight From 450+ Amazon Interviews - My Discussion with Gayle Gallagher (69:14)
Traditional Automation vs. AI Automation vs. AI Agents
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