Principles, Techniques, and Applications to Harness the Power of Prompts in LLMs as a Data Analyst — Large Language Model (LLM) is on the rise, driven by the popularity of ChatGPT by OpenAI which took the internet by storm. As a practitioner in the data field, I look for ways to best utilize this technology in my work, especially for insightful-yet-practical work as a Data Analyst. LLMs…

How data model deployment differs from other analytics projects — So you have created an ML model which seems to have reliable results. Congratulations! I know from experience that oftentimes it’s not easy to get reliable ML models, especially with limited ML exposure as a Data Analyst. …

Ways to calculate Customer Lifetime Value (CLV) and adjust your product priorities. — Customer Lifetime Value (CLV) is one of the most important metrics in marketing analytics. It calculates the monetary value which we gain from the customer over their time on the platform. Exploring this metric can help us in understanding: The perceived value of our products, as this metric reflects the…

..and make yourself relevant and competitive in the professional market — “I’ve been in this role for quite some time already. I wonder if I’m still relevant to the job market.” — a tenured PM friend That was a snippet of my conversation with an old friend a few weeks ago. This statement surprised me (knowing him and his astonishing career…

Common to-not-do-lists often overlooked in product experimentation causing poor and unreliable results — We all know product experimentation is important, and its benefits have largely been proven by organizations, enabling data-driven decisions on products, features, and processes. Google was testing 40 shades of blue on a link in the search results, and the right blue shade led to 200M in revenue. Booking.com …

It starts with following the curious mind — Research shows that average professionals spend roughly 80,000 hours at work throughout their entire careers. From Monday to Friday, we spend (at least) around a quarter of our time at work. With this huge time spent, no wonder our work impacts our lives to a large extent. …

Exploring the metadata and color-related features of a photo image for further use in Analytics and ML — When working with image data, object recognition and its application have always been on top of mind for Data Analysts and Data Scientists. There has been very little focus on utilizing the metadata from these images. Truth is, we can get quite a lot of insights from the image metadata…

Tips to manage ad-hoc requests and keep you focused on the projects that matter — As a Data Analyst, especially in an organization in which the team is not data-savvy yet, we often need to deal with numerous ad-hoc data requests from various stakeholders. One by one by itself these requests seem small, so you can quickly take a time from your projects to work…

Help the data/analytics team ensure your product development team build the best data-driven products for your business. — We all know data is an essential aspect of product development. It is used in deciding the product direction (with data analytics), operations (with data engineering/ business intelligence), and feature improvement (data sciences). Given its importance, we’ve seen organizations invest significantly in their data/analytics teams, primarily to support product development.