Master Generative AI for Data Science: Complete 2024 Guide
Focused View
8:03:15
01 - Getting started.mp4
00:42
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
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
01 - Quantitative and qualitative data.mp4
01:45
02 - Discrete vs. continuous data.mp4
02:03
03 - Categorical data.mp4
03:08
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
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
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
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
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
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
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
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
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
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
More details
Course Overview
This comprehensive course provides the essential foundation for leveraging Generative AI in data analysis and data science. Learn to transform business questions into actionable solutions using GenAI tools while mastering core statistics, machine learning, and data management principles.
What You'll Learn
- Break down complex data problems into AI-solvable components
- Implement statistical analysis and machine learning with Python
- Generate scripts for ANOVA, Chi-squared tests, and regression models
Who This Is For
- Aspiring data scientists seeking GenAI integration skills
- Analysts transitioning to AI-powered data workflows
- Professionals needing practical SQL/Python for database analysis
Key Benefits
- Hands-on experience with Jupyter, Colab, and AI-generated code
- Master data visualization, sampling, and inferential statistics
- Build complete ML models from data cleaning to evaluation
Curriculum Highlights
- Data analysis foundations with GenAI integration
- Statistical inference & hypothesis testing automation
- End-to-end machine learning lifecycle implementation
Focused display
- language english
- Training sessions 108
- duration 8:03:15
- English subtitles has
- Release Date 2025/06/07
