Welcome - let's get you set up for success!
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- 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)
- Your DSI Downloadable Resources Library
SQL for Data Science
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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)
- 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)
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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Introducing ABC Grocery - our client in desperate need of Data Science expertise!
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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)
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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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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Extension: Data Storytelling (Gilbert Eijkelenboom)
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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)
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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
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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
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