Coding and Automation for Data Analysis with Generative AI from Coursera

This course teaches you to harness AI-powered tools for code generation, focusing on SQL, Python, and R for data analysis tasks. The target learner for this course is a seasoned data professional or an aspiring analyst. This comprehensive program empowers. The course covers the design and implementation of automated data processing pipelines, enabling you to streamline your entire data analysis workflow.

Upon completion of this course, you'll be able to:

- Generate code snippets for data analysis tasks using AI

- Create and optimize formulas for data manipulation and analysis

- Design automated data processing pipelines with generative AI

- Integrate AI capabilities into existing coding environments and workflows

What's inside

Syllabus

Code generation fundamentals

Lays the foundation for using generative AI in coding by introducing key concepts, tools, and techniques for effective code generation and prompt engineering.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Focuses on SQL, Python, and R, which are languages widely used in data analysis and are essential for data professionals

Teaches the design of automated data processing pipelines, which can significantly improve efficiency in data analysis workflows

Presented by Microsoft, which is known for its data-based products like SQL Server and its groundbreaking research in AI

Requires learners to integrate AI capabilities into existing coding environments, which may require familiarity with specific IDEs or platforms

Explores prompt engineering, which is a rapidly evolving field, so learners should stay updated on the latest advancements

Reviews summary

Applying generative ai to data analysis coding

According to learners, this course offers a highly relevant look at integrating Generative AI into data analysis workflows. Students appreciate the focus on using AI for practical coding tasks in SQL, Python, and R, finding it valuable for automating repetitive tasks and streamlining their work. Many felt it provided a strong foundation for applying AI in the data field. However, some learners noted that the course assumes a baseline of prior coding knowledge and might move at a fast pace for complete beginners. Overall, it's considered a valuable introduction to a cutting-edge topic for data professionals.

Streamline data analysis processes with AI.

"Learning to automate repetitive data tasks with generative AI is the biggest takeaway for me."

"This course shows how AI isn't just for generating text, but for making my data work faster."

"The focus on pipeline integration felt very practical for my job."

Directly apply AI for data coding tasks.

"The hands-on coding parts were great for seeing AI in action with Python and SQL."

"I immediately started using some of the prompt techniques for my own data cleaning scripts."

"Really helps bridge the gap between theory and using AI in everyday data analysis."

Course covers a highly relevant and timely topic.

"The topic is super relevant right now in the data field. Felt very current."

"Glad to see a course addressing how AI impacts data analysis directly."

"This equipped me with skills needed for future trends in data science."

Some topics could benefit from more advanced coverage.

"I was hoping for a bit more depth on advanced prompt engineering techniques."

"Felt like an excellent intro, but left me wanting more advanced use cases."

"Good overview, but doesn't cover customizing models or handling more complex scenarios deeply."

Better suited if you already know SQL/Python/R basics.

"As a beginner, I found the coding sections moved very fast and assumed prior knowledge."

"Wish there was more fundamental coding review before diving into AI applications."

"This isn't really a 'learn to code' course, it's 'use AI to help you code data analysis'."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Coding and Automation for Data Analysis with Generative AI with these activities:

Review Python Fundamentals

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Strengthen your Python foundation to better understand and apply AI-generated Python code for data analysis.

Browse courses on Python Programming

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Review basic Python syntax and data structures.
Practice writing simple Python functions.
Work through introductory Python tutorials online.

Brush Up on SQL

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Revisit SQL concepts to effectively utilize AI-generated SQL code for data querying and manipulation.

Browse courses on SQL Databases

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Review SQL syntax for querying and joining tables.
Practice writing SQL queries on sample datasets.
Explore different SQL database systems.

Read 'Automate the Boring Stuff with Python'

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Learn practical Python automation skills to complement AI-driven code generation for data analysis.

View Automate the Boring Stuff with Python, 3rd... on Amazon

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Read the chapters relevant to data manipulation and automation.
Complete the exercises at the end of each chapter.
Apply the concepts to automate a simple data task.

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Automate Data Cleaning with AI

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Apply AI code generation to automate data cleaning tasks, reinforcing your understanding of both AI and data analysis.

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Identify a dataset that requires cleaning.
Use AI to generate code for cleaning specific data issues.
Test and refine the generated code.
Document the automated cleaning process.

Blog Post: AI for Data Analysis

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Solidify your knowledge by explaining how AI can be used to enhance data analysis workflows in a blog post.

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Research different applications of AI in data analysis.
Outline the key points for your blog post.
Write and edit the blog post.
Publish the blog post on a platform like Medium or LinkedIn.

Explore AI-Powered Data Visualization Tools

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Learn how to use AI to create insightful data visualizations, expanding your data analysis toolkit.

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Research different AI-powered data visualization tools.
Follow tutorials to create visualizations using these tools.
Experiment with different visualization techniques.

Contribute to an Open Source Data Analysis Project

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Apply your AI and data analysis skills to a real-world project, gaining valuable experience and contributing to the community.

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Find an open-source data analysis project on GitHub.
Identify an area where you can contribute, such as bug fixes or new features.
Submit a pull request with your changes.
Respond to feedback and iterate on your contribution.

Career center

Learners who complete Coding and Automation for Data Analysis with Generative AI will develop knowledge and skills that may be useful to these careers:

Data Analyst

A data analyst examines data to identify trends, patterns, and insights to inform business decisions. This course's curriculum on code generation, formula creation, and automated data processing pipeline design directly addresses the daily workflow of a data analyst. Proficiency in SQL, Python, and R, coupled with the ability to integrate AI into existing coding environments, allows the data analyst to work efficiently and effectively. This makes this course particularly helpful for an aspiring or current data analyst.

See salaries and explore the career path for Data Analyst

Data Scientist

A data scientist leverages statistical methods and machine learning to extract insights from data. The ability to generate code snippets for data analysis tasks using AI, as taught in this course, directly benefits a data scientist by making coding more efficient. This course's focus on coding skills in Python and R, alongside formula optimization and the design of automated data processing pipelines, will streamline the data analysis workflow, which is central to the role. For a data scientist, this course is a good way to enhance practical skills.

See salaries and explore the career path for Data Scientist

Bioinformatician

A bioinformatician analyzes biological data using computational tools and techniques. The advanced coding and automation skills taught in this course are directly relevant, especially regarding the use of generative AI for data analysis. The bioinformatician uses Python and R, and the ability to automate tasks and work with complex datasets is invaluable. This course will assist the bioinformatician in streamlining tedious tasks.

See salaries and explore the career path for Bioinformatician

Business Intelligence Analyst

A business intelligence analyst analyzes business data with the goal of improving decision-making. The course's emphasis on creating automated data processing pipelines will help a business intelligence analyst work more efficiently. The ability to generate code using AI, learn advanced coding techniques, and integrate AI into existing coding environments allows the business intelligence analyst to improve data analysis capabilities. Learning how to leverage AI in this way is an advantage for any business intelligence analyst.

See salaries and explore the career path for Business Intelligence Analyst

Statistician

A statistician collects and analyzes data to quantify trends, and to make inferences. The ability to automate data processing workflows, as well as generate code, directly enhances the efficiency of a statistician. This course’s curriculum on using generative AI to build formulas will allow a statistician to more easily manipulate data for analysis. The course's focus on integrating AI into existing workflows also makes this course helpful for a statistician.

See salaries and explore the career path for Statistician

Machine Learning Engineer

A machine learning engineer builds and deploys machine learning models. The focus of this course on using generative AI for code creation, formula optimization, and automated data processing pipeline creation is relevant for a machine learning engineer, who often works with complex datasets. This course teaches how to integrate AI into coding environments and assists with debugging code, which can be particularly useful for a machine learning engineer. This course will help a machine learning engineer understand how to leverage AI for automation of tasks.

See salaries and explore the career path for Machine Learning Engineer

Quantitative Analyst

A quantitative analyst, often working in finance, uses mathematical and statistical models to make investment decisions. This course’s focus on generating code, building formulas, and automating data pipelines directly applies to the kind of work a quantitative analyst undertakes. The advanced coding techniques will allow a quantitative analyst to adapt and customize their own systems. This course may be particularly useful for a quantitative analyst looking to expand their technical skill set.

See salaries and explore the career path for Quantitative Analyst

Operations Research Analyst

An operations research analyst uses mathematical and analytical methods to optimize business processes. This course, which teaches coding techniques, formula building, and data automation, will directly apply to many tasks undertaken by the operations research analyst. This course will assist an operations research analyst in optimizing their workflows. An operations research analyst will find this course to be useful.

See salaries and explore the career path for Operations Research Analyst

Data Engineer

A data engineer builds and maintains systems for data storage and processing. This course's focus on designing automated data processing pipelines, along with the ability to generate code for data analysis using generative AI, helps the data engineer do their job effectively. In particular, the study of how to integrate new capabilities into existing workflows and coding environments is beneficial. A data engineer may find this course useful to become more versatile.

See salaries and explore the career path for Data Engineer

Research Scientist

A research scientist employs the scientific method to conduct experiments and analyze results. This course, with its focus on code generation and automation techniques in data analysis, will assist a research scientist in streamlining data processing and analysis tasks. The coding, formulas, and automation skills taught by this course are valuable in the research and development space. A research scientist may find this course to be helpful.

See salaries and explore the career path for Research Scientist

Market Research Analyst

A market research analyst studies market trends and consumer behavior. The automation of data processing pipelines and the ability to generate code using AI, as taught in this course, may allow a market research analyst to handle large data sets more efficiently. This course builds key skills in data cleaning and visualization, which is beneficial to a market research analyst. A market research analyst may find this course helpful.

See salaries and explore the career path for Market Research Analyst

Computational Linguist

A computational linguist develops computational models of language for applications such as natural language processing. The coding and automation skills taught in this course will help a computational linguist streamline workflows. The ability to generate code using AI and automate data processing pipelines is useful for a computational linguist, especially for large language datasets. A computational linguist may find this course to be helpful.

See salaries and explore the career path for Computational Linguist

Financial Analyst

A financial analyst analyzes financial data, prepares reports, and makes recommendations. While not a direct fit, the automation of data processing pipelines taught in this course may be useful for a financial analyst to manage large volumes of data. The course's use of SQL, Python, and R for data manipulation is useful for this role. A financial analyst looking to enhance their technical skills may find this course useful.

See salaries and explore the career path for Financial Analyst

Database Administrator

A database administrator manages databases to ensure they are secure, available, and performing efficiently. This course’s focus on SQL code generation, along with the ability to create formulas and automate data processing pipelines through the use of generative AI, may be useful for a database administrator. The course teaches how to integrate AI and new techniques into existing coding environments, which builds a foundation for growth. This course may be helpful for a database administrator.

See salaries and explore the career path for Database Administrator

Actuary

An actuary assesses risk and uncertainty, usually in the context of insurance and finance. Although this role does not require programming experience, the automation of data processing pipelines, as taught in this course, can assist them with their work. The course teaches the use of Python and R which will allow an Actuary to process large data sets and analyze them. An actuary may find this course helpful.

See salaries and explore the career path for Actuary

Reading list

We've selected one books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Coding and Automation for Data Analysis with Generative AI.

Automate the Boring Stuff with Python, 3rd Edition

Save

Provides a practical introduction to Python programming with a focus on automating everyday tasks. It's particularly useful for data professionals looking to streamline their workflows. While not directly focused on AI, it builds a strong Python foundation. This book is commonly used as a textbook at academic institutions.

Automate the Boring Stuff with Python, 3rd Edition

Kindle Edition

Coding and Automation for Data Analysis with Generative AI

What's inside

Syllabus

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Reviews summary

Applying generative ai to data analysis coding

Activities

Career center

Reading list

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