Welcome - let's get you set up for success!
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- Welcome to DATA SCIENCE INFINITY! (1:17)
- Join the private Slack channel - and start getting *dedicated* support & guidance (Career Plan Only)
- How to get the most out of the private Slack channel (Career Plan Only)
- What makes a GREAT Data Scientist? (3:04)
- Self Confidence & Imposter Syndrome (3:31)
- Course Overview (3:31)
- Your DSI Downloadable Resources Library
SQL for Data Science
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- Introduction to SQL for Data Science (8:49)
- Connecting to the DATA SCIENCE INFINITY cloud database (PRACTICAL) (10:06)
- Introduction to SQL Workbench/J (6:58)
- SQL Workbench/J Troubleshooting 1 - Connection Issues
- SQL Workbench/J Troubleshooting 2 - Frequent Disconnection From Database
- The SELECT statement (PRACTICAL) (9:43)
- Applying selection conditions using the WHERE statement (PRACTICAL) (8:48)
- Aggregation functions and the GROUP BY statement (PRACTICAL) (10:11)
- Conditional rules using CASE WHEN (PRACTICAL) (9:12)
- The use of WINDOW functions (PRACTICAL) (11:38)
- Joining tables using JOIN (PRACTICAL) (19:23)
- The only SQL Joins Cheatsheet you'll ever need (image)
- Stacking data using UNION and UNION ALL (PRACTICAL) (4:34)
- Executing multiple queries using TEMP TABLES and CTE (9:50)
- Other useful TIPS & TRICKS! (PRACTICAL) (20:50)
- 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)
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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- Statistics Section Overview (1:08)
- The Different "Types" Of Data (3:52)
- Quiz Time! Statistics - Types Of Data
- What Is A Distribution? (13:31)
- Working With A "Normal Distribution" (5:40)
- Types Of Distributions (7:27)
- Quiz Time! Statistics - Distributions
- The Central Limit Theorem (9:08)
- Get Confident With Confidence Intervals (10:09)
- Quiz Time! Statistics - Dealing With Uncertainty
- Introduction to Hypothesis Testing (9:54)
- Hypothesis Testing: One Sample T-Test (9:13)
- Hypothesis Testing: Independent Samples T-Test (9:39)
- Hypothesis Testing: Paired T-Test (8:38)
- Hypothesis Testing: Chi Square Test For Independence (10:01)
- Note: Chi-Square Test vs. Z-Test For Proportions
- P-Values: What They Area - And What They Are Not (3:39)
- Quiz Time! Statistics - Hypothesis Testing & P-Values
- Symbols and Notation (4:11)
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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- A Checklist for Data Cleaning & Preparation (10:30)
- Dealing with Missing Values (THEORY) (8:41)
- 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) (7:14)
- Dealing with Categorical Variables - One Hot Encoder (PRACTICAL) (10:50)
- Dealing with Outliers (THEORY) (9:25)
- Dealing with Outliers (PRACTICAL) (13:34)
- Feature Scaling for Machine Learning (THEORY) (7:17)
- Feature Scaling for Machine Learning (PRACTICAL) (8:18)
- Feature Selection in Machine Learning (THEORY) (10:59)
- 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) (7:54)
- 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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From Learning to Earning: A Framework for Success!
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- Section Introduction (4:17)
- The 3 key areas for Learning to Earning in Data Science (5:00)
- BRAND - Small changes to make your CV or Resume stand out (8:27)
- BRAND - What hiring managers want to see from Projects & Portfolios (6:31)
- BRAND - Small changes that can have a big impact (3:16)
- APPLYING - Understanding the role (3:49)
- APPLYING - Speaking to a Recruiter or HR (3:24)
- APPLYING - An overview of Data Science Interviews (3:31)
- INTERVIEWING - Keep the human connection in mind: Simple ways to build rapport (2:38)
- INTERVIEWING - Effectively answering questions you don't know the answer to... (3:44)
- INTERVIEWING - Effectively answering questions about mistakes you've made... (2:33)
- INTERVIEWING - Tips for Take-Home Assignments (7:20)
- INTERVIEWING - Tips for Coding Tests (6:36)
- INTERVIEWING - Questions to ask (and not ask) your interviewer (2:09)
- Let's talk about rejections... (2:38)
C.R.A.I.G - your system for tackling the Data Science hiring process!
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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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Admin
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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
Other Resources
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