Complete Guide to Generative AI for Data Analysis and Data Science

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Dan Sullivan

8:03:15

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01 - Introduction

01 - Getting started.mp4

00:42

02 - 1. Demystifying Data Data Analysis and Data Science

01 - Asking questions.mp4

09:03

02 - Collecting and obtaining data.mp4

04:53

03 - Cleaning and preparing data.mp4

03:18

04 - Analyzing data.mp4

03:18

05 - Predictive modeling.mp4

01:27

06 - Machine learning.mp4

04:11

07 - Interpret the results.mp4

02:23

03 - 2. Tools of the Trade

01 - Problem-solving.mp4

04:19

02 - Statistics.mp4

05:41

03 - Machine learning algorithms.mp4

03:35

04 - Spreadsheets.mp4

01:42

05 - Python.mp4

02:27

06 - SQL and relational databases.mp4

04:14

07 - Statistics platforms.mp4

02:37

08 - Machine learning libraries.mp4

01:59

04 - 3. Thinking About Data

01 - Quantitative and qualitative data.mp4

01:45

02 - Discrete vs. continuous data.mp4

02:03

03 - Categorical data.mp4

03:08

05 - 4. Techniques for Describing Data

01 - Measures of central tendency.mp4

03:23

02 - Measures of spread.mp4

04:25

03 - Visualizing data distribution.mp4

02:14

04 - Describing a dataset using generative AI.mp4

05:02

05 - Challenge Describing data.mp4

00:50

06 - Solution Describing data.mp4

03:16

06 - 5. Distributions of Data

01 - Distributions of data.mp4

07:27

02 - Visualizing a normal distribution in a spreadsheet.mp4

03:29

03 - Jupyter Notebook and Colab.mp4

03:51

04 - Generating a normal distribution.mp4

06:23

05 - Visualizing a normal distribution in Python.mp4

04:56

06 - Visualizing a uniform distribution in Python.mp4

03:00

07 - Visualizing a bimodal distribution in Python.mp4

05:54

08 - Challenge Distributions of data.mp4

00:40

09 - Solution Distribution of data.mp4

04:07

07 - 6. Sampling Data

01 - Sampling and large populations.mp4

06:31

02 - Creating samples.mp4

06:01

03 - Saving samples to a file.mp4

02:32

04 - Comparing population to sample statistics.mp4

04:02

05 - Challenge Sampling data.mp4

00:34

06 - Solution Sampling data.mp4

02:34

08 - 7. Making Inferences from Data

01 - Inferential statistics.mp4

04:25

02 - Hypothesis testing methodology.mp4

04:17

03 - Analyzing customer preferences.mp4

11:20

04 - Type I and type II errors.mp4

01:30

05 - ANOVA tests for comparing means.mp4

01:55

06 - Generating Python scripts for ANOVA.mp4

03:45

07 - Testing independence of categorical variables.mp4

01:53

08 - Generating Python Scripts for Chi-squared tests.mp4

03:33

09 - Correlation analysis.mp4

07:12

10 - Testing for normality.mp4

02:25

11 - Generating Python for testing normality.mp4

03:46

12 - Generating Python for correlation analysis.mp4

02:12

13 - Challenge Making inferences from data.mp4

00:24

14 - Solution Making inferences from data.mp4

03:17

09 - 8. Visualizing Data

01 - Visualizing data.mp4

01:52

02 - Visualizing trends.mp4

04:43

03 - Visualizing correlations.mp4

02:34

04 - Visualizing composition.mp4

03:40

05 - Visualizing distributions.mp4

02:53

06 - Challenge Visualizing data.mp4

00:33

07 - Solution Visualizing data.mp4

02:17

10 - 9.Regression

01 - Linear regression.mp4

07:44

02 - Evaluating linear regression models.mp4

02:37

03 - Visualizing sales data.mp4

01:56

04 - Building a linear regression model.mp4

04:16

05 - Evaluating a sales linear regression model.mp4

02:46

06 - Challenge Building a regression model.mp4

00:48

07 - Solution Building a regression model.mp4

04:32

11 - 10. Analyzing Data in Files

01 - Data files.mp4

04:09

02 - Using spreadsheets with CSV files.mp4

02:43

03 - Reviewing an example JSON file.mp4

04:29

04 - Using jq with JSON files.mp4

06:23

05 - Generating jq commands using AI.mp4

06:01

06 - Dataframes in Python.mp4

08:20

07 - Loading CSV data into dataframes.mp4

03:44

08 - Loading JSON into dataframes.mp4

06:17

09 - Inspecting dataframes.mp4

04:12

10 - Data quality and data cleansing.mp4

06:28

11 - Using AI for data quality and data cleansing.mp4

05:06

12 - Challenge Missing data.mp4

00:35

13 - Solution Missing data.mp4

04:00

12 - 11. Analyzing Data in Databases

01 - Relational databases.mp4

15:15

02 - NoSQL databases.mp4

10:21

03 - Extraction, transformation, and loading data into databases.mp4

05:46

04 - Introduction to SQL.mp4

05:45

05 - Creating tables and inserting data.mp4

08:02

06 - Querying data with SQL.mp4

10:28

07 - Joining data with SQL.mp4

06:57

08 - Descriptiive statistics in SQL.mp4

04:55

09 - Generating synthetic data sets for a relational database.mp4

07:12

10 - Generating a star schema, synthetic data, and queries.mp4

03:41

11 - Challenge Generate a relational data model.mp4

01:12

12 - Solution Generate a relational data model.mp4

04:32

13 - 12. Introduction to Machine Learning

01 - Supervised and unsupervised learning.mp4

12:27

02 - Classification.mp4

06:41

03 - Regression.mp4

02:56

04 - Clustering.mp4

03:20

05 - Machine learning lifecycle.mp4

05:37

06 - Feature engineering.mp4

08:04

07 - Model evaluation.mp4

06:54

14 - 13. Building Machine Learning Models Classification

01 - Simple classification model.mp4

08:34

02 - Handling missing data.mp4

05:00

03 - Comparing multiple algorithms.mp4

06:43

04 - Classification with neural networks.mp4

14:22

05 - Hyperparameter tuning.mp4

06:32

06 - Evaluating feature importance.mp4

02:24

07 - Challenge Predicting consumer intent.mp4

00:41

08 - Solution Predicting consumer intent.mp4

07:26

Description

GenAI has the potential to enable many more people to work with and analyze data, but to succeed, you need a solid foundation in data management, statistics, and machine learning. This course provides that foundation. Instructor Dan Sullivan teaches how to break down business questions and data science questions into components that can be addressed programmatically and then how to use genAI to create programs and scripts to implement a solution. This course focuses on the three pillars needed to be a successful data analyst or data scientist: problem solving skills, an understanding of statistics and machine learning, and practical experience with data management procedures.

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Data Science

Dan Sullivan

Instructor's Courses

Cloud and data architect with extensive experience in data architecture, data science, machine learning, stream processing, and cloud architecture. Capable of starting with vague initiatives and formulating precise objectives, strategies, and implementation plans. Regularly works with C-level and VP executives while also mentoring and coaching software engineers. Adapts well to unforeseen challenges. He is the author of the official Google Cloud study guides for the Professional Architect, Professional Data Engineer, and Associate Cloud Engineer.

Linkedin Learning

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LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.