Hi, I'm Andrew, founder and lead instructor at Data Science Infinity...
On this page you'll find the complete Data Science, Analytics & AI curriculum, built to help you learn the right skills, in the right order, and apply them properly.
You’ll build from the must-have fundamentals, including SQL, Python, Statistics, A/B Testing, data preparation, Tableau, GitHub, Machine Learning, and project best practices.
Then you’ll layer on the latest industry-relevant skills, including Deep Learning, AWS & The Cloud, Generative AI, LLMs, RAG, agentic AI, coding agents, Data Storytelling, and more.
This is not random content.
The curriculum is built around what hiring teams actually care about, informed by ongoing conversations with leaders, hiring managers, and recruiters, plus my own experience screening and interviewing thousands of candidates.
Inside, you’ll find 400+ carefully created tutorials, quizzes, and resources, all designed to help you build real understanding, real skills, and real confidence.
And because the field keeps moving, DSI keeps moving too.
You get lifetime access to the curriculum and all future updates, so your learning can continue to evolve with Data Science, Analytics, and AI.
Everything you need is here, all in one place.
Welcome - let's get you set up for success!
Available in
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- Welcome to DSI! Let's Do This... (2:29)
- Join The DSI Community For My Dedicated 1:1 Support & Guidance
- How To Get The Most From The DSI Private Community
- What makes a GREAT Data Scientist? (2:57)
- Self Confidence & Imposter Syndrome (3:46)
- Course Overview (3:19)
- Advice From A Student Who Has Completed DSI
- Get The Teachable App!
- Your DSI Downloadable Resources Library
SQL for Data Science
Available in
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- 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!)
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- 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!
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- 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
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- 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
- Add the pre-built project to your portfolio!
- 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
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- 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
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- 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
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- 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!
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- 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
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Github for Data Science
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Get The ABC Grocery Data!
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AB Testing - Theory & Application
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- 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)
- Add the pre-built project to your portfolio!
- 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
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Preparing & Cleaning Data for Machine Learning
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- 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
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Linear Regression
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- 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)
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Random Forests for Regression
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- 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)
- Add the pre-built project to your portfolio!
- Quiz Time! Random Forest (Regression) Concepts
Machine Learning for Classification Tasks
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Logistic Regression
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- 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)
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Random Forests for Classification
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K-Nearest-Neighbours (KNN) for Classification
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- 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)
- Add the pre-built project to your portfolio!
- Quiz Time! KNN Concepts
Advanced applications of scikit-learn
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Unsupervised Learning: K-Means Clustering
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- 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)
- Add the pre-built project to your portfolio!
- Quiz Time! K-Means Concepts
Unsupervised Learning: Principal Component Analysis (PCA)
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Unsupervised Learning: Association Rule Learning
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Causal Impact Analysis
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Machine Learning Model Deployment
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- 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
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Acing The Data Science & Analytics Hiring Process!
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- 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
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Deep Learning - Artificial Neural Networks
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- 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
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- 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)
- Add the pre-built project to your portfolio!
Deep Learning - Image Search Engine
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Deep Learning Resource
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Generative AI + LLMs + Agentic AI
Available in
days
days
after you enroll
- 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 (12:18)
- 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)
- Getting Started With OpenAI Codex For Data Science (86:35)
Cloud Fundamentals Using AWS
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- 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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days
after you enroll
Extension: Data Storytelling (Gilbert Eijkelenboom)
Available in
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- 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)
Available in
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days
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- 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
Available in
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- 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
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