Complete Guide to Generative AI for Data Analysis and Data Science

سرفصل های دوره

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.

01 - Introduction

01 - Getting started

02 - 1. Demystifying Data Data Analysis and Data Science

01 - Asking questions

02 - Collecting and obtaining data

03 - Cleaning and preparing data

04 - Analyzing data

05 - Predictive modeling

06 - Machine learning

07 - Interpret the results

03 - 2. Tools of the Trade

01 - Problem-solving

02 - Statistics

03 - Machine learning algorithms

04 - Spreadsheets

05 - Python

06 - SQL and relational databases

07 - Statistics platforms

08 - Machine learning libraries

04 - 3. Thinking About Data

01 - Quantitative and qualitative data

02 - Discrete vs. continuous data

03 - Categorical data

05 - 4. Techniques for Describing Data

01 - Measures of central tendency

02 - Measures of spread

03 - Visualizing data distribution

04 - Describing a dataset using generative AI

05 - Challenge Describing data

06 - Solution Describing data

06 - 5. Distributions of Data

01 - Distributions of data

02 - Visualizing a normal distribution in a spreadsheet

03 - Jupyter Notebook and Colab

04 - Generating a normal distribution

05 - Visualizing a normal distribution in Python

06 - Visualizing a uniform distribution in Python

07 - Visualizing a bimodal distribution in Python

08 - Challenge Distributions of data

09 - Solution Distribution of data

07 - 6. Sampling Data

01 - Sampling and large populations

02 - Creating samples

03 - Saving samples to a file

04 - Comparing population to sample statistics

05 - Challenge Sampling data

06 - Solution Sampling data

08 - 7. Making Inferences from Data

01 - Inferential statistics

02 - Hypothesis testing methodology

03 - Analyzing customer preferences

04 - Type I and type II errors

05 - ANOVA tests for comparing means

06 - Generating Python scripts for ANOVA

07 - Testing independence of categorical variables

08 - Generating Python Scripts for Chi-squared tests

09 - Correlation analysis

10 - Testing for normality

11 - Generating Python for testing normality

12 - Generating Python for correlation analysis

13 - Challenge Making inferences from data

14 - Solution Making inferences from data

09 - 8. Visualizing Data

01 - Visualizing data

02 - Visualizing trends

03 - Visualizing correlations

04 - Visualizing composition

05 - Visualizing distributions

06 - Challenge Visualizing data

07 - Solution Visualizing data

10 - 9.Regression

01 - Linear regression

02 - Evaluating linear regression models

03 - Visualizing sales data

04 - Building a linear regression model

05 - Evaluating a sales linear regression model

06 - Challenge Building a regression model

07 - Solution Building a regression model

11 - 10. Analyzing Data in Files

01 - Data files

02 - Using spreadsheets with CSV files

03 - Reviewing an example JSON file

04 - Using jq with JSON files

05 - Generating jq commands using AI

06 - Dataframes in Python

07 - Loading CSV data into dataframes

08 - Loading JSON into dataframes

09 - Inspecting dataframes

10 - Data quality and data cleansing

11 - Using AI for data quality and data cleansing

12 - Challenge Missing data

13 - Solution Missing data

12 - 11. Analyzing Data in Databases

01 - Relational databases

02 - NoSQL databases

03 - Extraction, transformation, and loading data into databases

04 - Introduction to SQL

05 - Creating tables and inserting data

06 - Querying data with SQL

07 - Joining data with SQL

08 - Descriptiive statistics in SQL

09 - Generating synthetic data sets for a relational database

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

11 - Challenge Generate a relational data model

12 - Solution Generate a relational data model

13 - 12. Introduction to Machine Learning

01 - Supervised and unsupervised learning

02 - Classification

03 - Regression

04 - Clustering

05 - Machine learning lifecycle

06 - Feature engineering

07 - Model evaluation

14 - 13. Building Machine Learning Models Classification

01 - Simple classification model

02 - Handling missing data

03 - Comparing multiple algorithms

04 - Classification with neural networks

05 - Hyperparameter tuning

06 - Evaluating feature importance

07 - Challenge Predicting consumer intent

08 - Solution Predicting consumer intent

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تولید کننده: LinkedIn Learning (Lynda)