Generative AI for data analytics

Explore deep learning fundamentals and the role of ChatGPT, developed by OpenAI. Learn how to access ChatGPT online and interact with it by the end of the section.

Summarizes the history and core mechanics of deep learning from artificial neurons and backpropagation to large language models like ChatGPT and generative models like DALL-E 2.

Explore the evolution and impact of language models, from Eliza to transformer architectures like GPT and BERT, trained on vast data with attention, and discuss safety, ethics, and societal implications.

Explore ChatGPT from OpenAI, its GPT-3 to GPT-4 parameter growth, and training on hundreds of terabytes. Understand it as a narrow AI, a next-word predictor you access for free.

Access ChatGPT by visiting openai.com, sign up or log in, and complete phone verification. Use the browser interface to select GPT-4, manage chats, and interact via the main input window.

Explore ChatGPT as a serious data analytics tool through hands-on demos, including writing rap lyrics, jokes, riddles, and short stories, while building interface familiarity and practical intuition.

Explore unstructured text data from polling sources and websites, and learn to leverage ChatGPT to summarize, integrate, and visualize qualitative and quantitative insights with clear graphs.

Analyze unstructured text from student polls with ChatGPT to summarize feedback, rank top course topics, and gauge sentiment for data analytics decisions; visualize results with bar or pie charts.

Learn to use ChatGPT to analyze Amazon chocolate reviews with time series, sentiment trends, and NLP clustering. Interpret headers, perform keyword and frequency analyses, and optimize seo.

Apply generative AI to analyze a Wikipedia page on human overpopulation, perform sentiment analysis on references, and generate a word cloud of key terms such as environment and education.

Use ChatGPT to read a medical journal article, translate its title, extract PDF text, and answer questions about a 24-hour fast’s effect on immune responses of mice and human relevance.

Invite learners to experiment with chat style customization, from nursery rhyme mode to cartoonish politician persona, while examining how intermittent fasting research in mice applies to humans for data analytics.

Explore the scope and purpose of data science and learn how to leverage ChatGPT strengths to learn and apply data science, preparing you for the upcoming application sections.

Define data science as using data to support decision making; data are numbers reflecting reality, and ChatGPT can aid data tasks but cannot replace the core data science work.

ChatGPT is a tool with specific uses, not a replacement for a human data scientist, excelling at writing and summarizing, while lacking lookups and functioning as a next word predictor.

Discover three uses of Chatgpt in data science: coding help, private tutoring for learning, and data communication to present findings to varied audiences.

Discover a new way of coding and doing data science by using ChatGPT to generate usable code for data science projects.

Master Google Colab for Python data science in the cloud with ready-to-use libraries. Learn how to open, save, and import notebooks from Google Drive or GitHub.

Learn to access and run the course's data analytics code from a GitHub repository in Google Colab. Save copy to Drive or download as a notebook or raw Python file.

Explore how ChatGPT generates Python code to import the wine quality data into pandas, visualize with seaborn, remove outliers by z-scores, and perform median-split quality with t-tests.

Explore how chatgpt helps identify and fix bugs in Python code through three examples and a mystery case. Learn prompts that guide debugging and code completion in data analytics.

Explore how generative ai for data analytics uses chatgpt to explain dense code, rewrite it for readability, add meaningful comments, and demonstrate dbscan clustering on simulated two-cluster data.

Explore translating code across Matlab, Python, and R with ChatGPT, compare libraries from Matlab to Python to R, and understand eigen decomposition differences for data analytics.

Learn practical prompt engineering tips to interact with ChatGPT effectively. Keep prompts simple and contextual, break large tasks into steps, and use natural language.

Learn to access ChatGPT via a Python API, install the OpenAI library in Colab with pip, and securely manage API keys while connecting to the OpenAI server.

Design a concrete set of rules for a number guessing game with ChatGPT, translate it into code, then translate Matlab to Python and explain the code and data science concepts.

Explore how generative AI for data analytics, including tools like ChatGPT, enables AI-assisted learning and scales one-on-one instruction while evaluating its impact.

Explore decision tree analysis in AI-assisted learning by examining its purpose, evaluation methods with constraints and objectives, and practical Python code using scikit-learn with the iris dataset.

Use ChatGPT as a tutor to critique your data science explanations, correcting partial correlation analysis and PCA with step-by-step feedback on concepts like control, F statistic, eigendecomposition, and covariance matrices.

Color-code Google Sheets automatically by highlighting positive numbers in green and negative numbers in red, using ChatGPT guided step-by-step instructions and handling zeros.

Leverage generative ai to brainstorm analyses and hypotheses for a bikeshare dataset, refine time-based insights with chatgpt, and test ideas like age groups and weather effects.

Examine what soft skills mean for data science and how to internalize them beyond generic advice, including communication, teamwork, and time management strategies such as the Eisenhower Matrix.

Apply prompt engineering tips for ChatGPT usage: set goals and context, request varied detail levels, specify length, iterate with examples and analogies, and verify outputs critically.

Engage with two learning exercises in data analytics: curation of outlier cleaning with a 5% trim and drafting 200-word blog on equal variance in a two-sample t test, via ChatGPT.

Show how Chatgpt can improve the written communication of data science findings, guiding you from coding insights to clearer data analytics reports.

Discover how to use ChatGPT to rewrite drafts and tailor writing for different audiences, from formal scientific prose to engaging general-audience style, to enhance your own writing.

Turn bullet point lists into flowing prose with a journalistic flair using ChatGPT, then convert prose back into concise bullet points for data analytics workflows.

Learn to use ChatGPT to interpret anova table from a two-way mixed design, explain p-values and partial eta squared, and produce audience-appropriate summaries about coffee effects on cognitive task performance.

Explore how Chatgpt translates with context from mathematics and linear algebra texts. Compare with Google Translate and discuss audience aware translations for data science communication.

Write a polite, assertive email to your boss about the accelerated widget timeline to protect quality; use a ChatGPT-inspired template and subject lines to push back effectively.

Set clear goals for ChatGPT with context and audience, request variants, and refine specific clauses; specify output style and detail; verify claims and cite peer-reviewed sources to guard against hallucinations.

Describe how the course creator integrates ai with human work, using ChatGPT for demos and script writing, mainly to refine ideas rather than replace them.

Explore the potential impacts of large language models on jobs, education, economy, inequality, politics, and media, and engage in thoughtful reflection and q&a discussions.

Generative ai tools like chatgpt augment analytics by handling repetitive tasks, freeing you for creative and strategic work, while regulation and market shifts may create new jobs or displace workers.

Examine how ChatGPT reshapes education by personalizing learning, fostering critical thinking, and lowering the entry bar into data science, while educators adapt to maximize benefits.

Explore how ChatGPT and large language models can complement education, data literacy, programming, and creative media, enabling future-proofing and responsible AI adoption.