Complete Guide to AI and Data Science for SQL: From Beginner to Advanced

Course details

You may need to use SQL in coordination with AI and data science, but if you don’t already know how, where can you learn? In this in-depth course, tech educator and the best-selling author Walter Shields starts with beginner-level concepts and projects and guides you through a series of engaging videos and Codespace challenges to more advanced concepts. Get a thorough introduction to data science, as well as AI, ML, and DL, then dive into stats and probability, linear regression, and data preparation and exploration. Explore data visualization and preprocessing, in addition to model building and evaluation. Learn about model interpretation, then demonstrate what you’ve learned in the course with a capstone project.

Concepts

0. Introduction

01 - Masterclass introduction
02 - What you should know
03 - Using the course exercise files

1. Introduction to Data Science

04 - Data science explained
05 - Data or information - What's the difference
06 - Data's increasing influence
07 - Comparing data roles
08 - Necessary skills for data science
09 - Real-world applications
10 - Challenge - Data
11 - Solution - Data

2. AI, ML, and DL

12 - The evolving face of artificial intelligence
13 - Machine learning problem classifications
14 - Machine learning approaches for business scenarios
15 - Challenge - AI
16 - Solution - AI

3. Introduction to Stats and Probability

17 - Statistics defined
18 - Statistical mindset
19 - Types of statistics
20 - Descriptive statistics
21 - Inferential statistics (probability)
22 - Python
23 - Challenge - Statistics
24 - Solution - Statistics

4. Predicting House Prices with Linear Regression

25 - Project goal
26 - Project steps
27 - Python environment setup
28 - SQL environment setup
29 - Project approach

5. Data Preparation and Exploration

30 - Importing necessary libraries and dataset overview
31 - Loading the data
32 - Checking the data info
33 - Summary statistics of the dataset
34 - Checking the distribution of the variables
35 - Applying log transformation and re-checking distribution
36 - Challenge - Preparation
37 - Solution - Preparation

6. Data Visualization and Exploration

38 - Bivariate analysis - Heat-map
39 - Visualizing relationships - Age of homes and distance to work
40 - Visualizing relationships - Highway access and property tax
41 - Checking correlation after removing outliers
42 - Visualizing relationships - Other pairs of variables
43 - Challenge - Visualization
44 - Solution - Visualization

7. Data Preprocessing

45 - Splitting the dataset into train and test sets
46 - Checking for multicollinearity using VIF
47 - Removing multicollinearity by dropping the tax feature
48 - Challenge - Preprocessing
49 - Solution - Preprocessing

8. Model Building and Evaluation

50 - Creating the linear regression model and model summary - Part 1
51 - Creating the linear regression model and model summary - Part 2
52 - Creating the linear regression model and model summary - Part 3
53 - Dropping insignificant variables and re-creating the model
54 - Checking assumptions for linear regression
55 - Assumption 1 - Checking for mean residuals
56 - Assumption 2 - Checking homoscedasticity
57 - Assumption 3 - Checking linearity
58 - Assumption 4 - Checking normality of error terms
59 - Q-Q plot for checking the normality of error terms
60 - Model performance comparison on train and test data
61 - Applying cross-validation and evaluation
62 - Challenge - Model building
63 - Solution - Model building

9. Model Interpretation and Reporting

64 - Extracting and creating a DataFrame of coefficients
65 - Writing the linear regression equation and coefficients
66 - Conclusions and business recommendations
67 - Challenge - Interpretation
68 - Solution - Interpretation

10. Final Capstone Project

69 - Final capstone project details
70 - Final capstone project solution walkthrough

Conclusion

71 - Next steps