Complete Guide to Generative AI for Data Analysis and Data Science
With Dan Sullivan
Liked by 116 users
Duration: 10h 21m
Skill level: Intermediate
Released: 9/27/2024
Course details
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.
Skills you’ll gain
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4.6 out of 5
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Contents
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Distributions of data7m 27s
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Visualizing a normal distribution in a spreadsheet3m 29s
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Jupyter Notebook and Colab3m 51s
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Generating a normal distribution6m 23s
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Visualizing a normal distribution in Python4m 56s
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Visualizing a uniform distribution in Python3m
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Visualizing a bimodal distribution in Python5m 54s
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Challenge: Distributions of data40s
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Solution: Distribution of data4m 7s
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Inferential statistics4m 25s
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Hypothesis testing methodology4m 17s
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Analyzing customer preferences11m 20s
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Type I and type II errors1m 30s
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ANOVA tests for comparing means1m 55s
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Generating Python scripts for ANOVA3m 45s
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Testing independence of categorical variables1m 53s
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Generating Python Scripts for Chi-squared tests3m 33s
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Correlation analysis7m 12s
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Testing for normality2m 25s
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Generating Python for testing normality3m 46s
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Generating Python for correlation analysis2m 12s
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Challenge: Making inferences from data24s
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Solution: Making inferences from data3m 17s
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Linear regression7m 44s
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Evaluating linear regression models2m 37s
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Visualizing sales data1m 56s
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Building a linear regression model4m 16s
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Evaluating a sales linear regression model2m 46s
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Challenge: Building a regression model48s
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Solution: Building a regression model4m 32s
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Data files4m 9s
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Using spreadsheets with CSV files2m 43s
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Reviewing an example JSON file4m 29s
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Using jq with JSON files6m 23s
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Generating jq commands using AI6m 1s
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Dataframes in Python8m 20s
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Loading CSV data into dataframes3m 44s
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Loading JSON into dataframes6m 17s
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Inspecting dataframes4m 12s
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Data quality and data cleansing6m 28s
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Using AI for data quality and data cleansing5m 6s
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Challenge: Missing data35s
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Solution: Missing data4m
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Relational databases15m 15s
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NoSQL databases10m 21s
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Extraction, transformation, and loading data into databases5m 46s
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Introduction to SQL5m 45s
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Creating tables and inserting data8m 2s
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Querying data with SQL10m 28s
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Joining data with SQL6m 57s
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Descriptiive statistics in SQL4m 55s
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Generating synthetic data sets for a relational database7m 12s
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Generating a star schema, synthetic data, and queries3m 41s
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Challenge: Generate a relational data model1m 12s
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Solution: Generate a relational data model4m 32s
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Simple classification model8m 34s
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Handling missing data5m
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Comparing multiple algorithms6m 43s
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Classification with neural networks14m 22s
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Hyperparameter tuning6m 32s
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Evaluating feature importance2m 24s
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Challenge: Predicting consumer intent41s
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Solution: Predicting consumer intent7m 26s
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Introduction to graph theory5m 54s
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NetworkX4m 27s
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Analyzing a social network7m 15s
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Supply chains and network analysis3m 20s
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Generating a synthetic supply chain4m 5s
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Visualizing a complex supply chain3m 37s
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Finding highest betweenness scores4m 36s
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Advanced topics in supply chain analysis6m 26s
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Challenge: Analyzing a social network19s
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Solution: Analyzing a social network2m 35s
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Introduction to simulations2m 42s
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Types of simulations10m 3s
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Modeling inventory management7m 13s
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Agent-based modeling9m 43s
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Modeling the spread of infectious diseases4m 29s
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Agent-base infectious diseases modeling5m 21s
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Challenge: Simulating forest fires55s
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Solution: Simulating forest fires5m 49s
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What’s included
- Learn on the go Access on tablet and phone
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