This course teaches you to use Large Language Models (LLMs) in your corporate analytics role. After covering the basics, this course teaches you a realistic use case that you can implement in your organisation.
Large Language Models (LLMs) are new. And they can be used for advanced analytics in business. But you need analytics experience to know what to do with them. So the best way to learn prompt engineering for analytics, is from an experienced data scientist.
There are now several prompt engineering courses on the internet. However, you will notice that they use trivial examples. They might explain concepts, but they don’t show a complete and commercially useful project. It’s because the creators of those courses don’t have any real commercial experience.They try their best, but all of their examples are trivial. They don’t have analytics industry experience.
With a generic course, you have connect generic examples to your analytics use cases. And then you need to figure out how to test and refine them. You can either waste a lot of your time. Or you can stand on the shoulders of an experienced data scientist. You can take an analytics focused prompt engineering course, taught by an experienced professional.
This course is taught by Slava Razbash, who has worked in data science roles since 2011. His resume includes Commonwealth Bank of Australia (Australia’s largest bank), Sportsbet (Flutter Entertainment), Tabcorp (multinational gambling business), Coles (one of Australia’s two largest supermarket chains) and AGL (one of Australia’s largest energy companies).
He’s solved a lot of data science and machine learning engineering problems. He’s also mastered a lot of new technologies along the way. In this course, we will follow Slava’s step-by-step approach to mastering another new technology, LLMs.
What is prompt engineering
Prompt Engineering is the skill of ﬁnding the right prompt to get the right results from your LLM. With expert level prompt engineering skills, you can implement a commercially profitable LLM solution. With average skills, the average prompt might not lead to a working solution at all.
We predict that prompt engineering will become the most demanded skill in the analytics industry within 12 - 24 months.
How this course works
This course starts with the basics and then becomes very involved and analytics focused. By the end of the course, you will be working on a commercially useful analytics use case. You might even be able to directly implement this use case in your company.
Large Language Models are another tool in your data science toolkit. They open up new analytics use cases, and improve the ones that you are already working on. In this course, you will be learning from an experienced data scientist. You will be learning from someone who has real world commercial data science experience.
This course uses the OpenAI suite of LLM products. However the skills that you learn will be applicable to all Large Language Models.
Two ways to access this course
The First Way: An AI Upskill Membership
The AI Upskill membership gives you access to all AI Upskill courses. You can subscribe to an AI Upskill membership at learn.aiupskill.io
The AI Upskill membership comes with email support. So you can always ask Slava for help via email. We are not aware of any other prompt engineering course that offers this level of dedicated support. Other courses appear to just have a forum where “learners help each other”. With AI Upskill, you are directly supported by the course creator.
The Second Way: Purchase this course only
You can purchase access this course course for one year. Scroll down to enrol now.
Bonus Option: A cheaper version on Udemy
A subset of this course is on Udemy. It doesn't contain all of the content of the full course - that's why it's cheaper. If you prefer a cheaper version, grab the cheaper version here.
How to get started
If you are already an AI Upskill member, you can start learning right now. AI Upskill members get access to all courses, and email support.
Prompt engineering will make you more valuable to your employer. AI skills are hard to hire and your company needs to leverage AI to stay competitive in the next 12 - 24 months. It’s a simple business decision for your employer to cover the cost of your AI Upskill membership. As well as boosting your skillset, you will also have an experienced data scientist answering your emails.
Subscribe to an AI Upskill membership at learn.aiupskill.io
In the realm of career decisions, you hold the reins to your own destiny. The choices that you make today have the potential to shape your future for many years. In terms of learning how to use prompt engineering in your analytics role, we see four distinct choices.
First Choice: Do nothing. Watch the AI boom pass by. If you choose the “do nothing” option, then you will miss out on the career boost from the AI boom. We estimate that the AI boom has the potential to boost your career by five years.
Second Choice: Completely switch industries away from anything to do with data and analytics. Maybe you always wanted to become a pilot?
Third Choice: Learn Prompt Engineering elsewhere. From instructors who have much less (if any) commercial experience. It will take longer. The courses and books will not be tailored to advanced analytics use cases. You will spend lots of time writing prompts about counting apples and other trivial applications. You will have to figure out how to transfer your new generic knowledge to the corporate analytics setting. You also won’t learn Slava’s techniques.
Fourth Choice: Take this Prompt Engineering for Analytics with LLMs course, become better in your current role, and give yourself a career boost. Because this is the fastest path to LLM mastery in the corporate data analytics space.
You’re not confined to a single path. The choice is in your hands!
Slava Razbash has worked in the data science industry since 2011. Slava has a solid track record of delivering data science projects. His career includes working in the big data team of Australia's largest bank, Commonwealth Bank of Australia. Slava helped start Sportsbet’s data science and personalisation capability (Sportsbet is a subsidiary of the multinational Flutter Entertainment plc). He productionised an innovative machine learning system in one Australia's oldest and largest companies, AGL.
Slava has contributed code to the famous forecast R package - which is now likely provided in production by one of your favourite cloud vendors. The forecast R package is used by multinational organisations for demand forecasting.
Slava has given a number of public presentations. Including a presentation in front a live of audience of over 250 industry professionals. Some of Slava's presentations are in the public domain - search for his name in your favourite search engine.
When learning from Slava, you are in good hands.