Jaimin Bhavsar

Jaimin Bhavsar


Explore PlayFab with Jasson McMorris

In this video, explore the vast number of features PlayFab offers and how they could be used to bring players and the developer together.


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Explore PlayFab with Jasson McMorris

Exploration in data science : vital, risky and yet neglected?

Why do we need to explore?

Whether during a PoC before launching a new product or when looking for improvements to an existing product, exploration is an essential step in a data science project. In particular, it should help to find out whether it is reasonable to invest time and money to solve a new problem. It also aims at prioritizing the work to be done in order to achieve a valuable product.

Why is it risky?

After the exploration phase one should have sufficient insights to decide to invest or not in the project and have an idea of risks. But to get those insights, it is sometimes necessary to go through time-consuming, even potentially infinite, tasks (there is always something to explore or test). However, the longer the exploration takes, the more the delivery is postponed and the less are the chances of other products being launched.

It therefore seems necessary to establish an exploration strategy.

How to go about it ?

Let’s start with an example

Before getting started, let’s consider Glasses for you! , an imaginary company selling sunglasses through its website and mobile app.

At the very start of Glasses for you, the business team wanted to know which customers to target first in marketing campaigns. The original wording of the requirement was approximately as follows:

“We want the data team to develop a model that can predict for each customer their likelihood of conversion.”

The data team in charge of that project was voluntary and expert in machine learning, deep learning and statistics. Yet it was a failure, the exploration phase was long, the results were insufficient to validate the PoC, the project was abandoned and the company wasted both time and money.

Using this fictitious example, I’ll present some ideas to make it easier to explore data science solutions while reducing disaster risk. Many of the tools I will talk about have been inspired by blog posts and articles (see references below), as well as by my own experience as a data scientist consultant.

#data #data-driven #mlops #data-science #exploration #exploration

Jaimin Bhavsar

Jaimin Bhavsar


Explore PlayFab with Jasson McMorris

In this video, explore the vast number of features PlayFab offers and how they could be used to bring players and the developer together.


Paula  Hall

Paula Hall


Exploring Pandas GUI [List of Best Features You Should Be Aware Of]

Pandas is the favourite library for any Data Science enthusiast. It caters to all the needs of processing the Data via the structured tabular format, date-time formats, and providing the matplotlib API to instantly perform plotting within the pandas chaining operations. You can load Data from websites directly into data frames. This library also comes in very handy while performing exploratory data analysis that reveals insights about the dataset and various distributions it aligns with.

As more and more tools are built to enhance Data exploration, Pandas GUI is one of them that uses pandas as the core component and displays a windowed GUI with a lot of additional functions that are usually performed manually.

Let’s explore this utility and look at some of the best features.

Best Features of Python GUI

#data science #python gui #exploring pandas gui #pandas #list of best features you should be aware of #exploring

Dejah  Reinger

Dejah Reinger


Exploring Undernourishment: Data Exploration


This is **Part 3 **of an 8-Part research project aiming to better understand the nuances of our global nutrition. It explores this topic through the utilisation of data visualisation and data science techniques. It is complimented by a Web App: ExploringUndernourishment, which is freely available to the public.

Part 1 — Introduction and Overview

Part 2 — Literature Review

**Part 3 — **Data Exploration ← Selected page

Part 4 — Research Area 1: General Trend

Part 5 — Research Area 2: Most Successful Countries

Part 6 — Research Area 3: Surprising Trends

Part 7 — Research Area 4: Most Influential Indicator

Part 8 — Recommendations and Conclusions

Data Exploration

In order to help record and monitor progress towards addressing this goal, the FAO has set up a means of recording and monitoring this data on a yearly basis and has made this data open to the public. Their open-data platform, FAOStat (FAO 2020c) provides this data free to the public within the first couple of months of each year.

A series of visualisations have been created as pare to the Exploratory Data Analysis (EDA) phase.

The Prevalence of Undernourishment Data

Figure 1 shows how the overall distribution of the Prevalence of Undernourishment is centred around 0.05%, with a long right tail out to 0.7%.

#r-shiny #data-exploration #r #un #machine-learning

Osborne  Durgan

Osborne Durgan


How to sign in to Azure PlayFab with Unity | Azure Developer Streams

In this episode of the Azure Developer Streams series, take a quick tour with Lana Lux to discuss what Azure PlayFab has to offer and how to sign in using Unity.

#azure #playfab #unity #developer