DEV Community: Ornella Altunyan

Beep Boop 8: Visualize, Analyze, and Collab on Data (No Code Required!)

Ornella Altunyan — Thu, 30 Sep 2021 15:13:00 +0000

Thanks to everyone who joined us live for the 8th and final (for now!) episode of Beep Boop with the Microsoft Reactor! We started off with a fabulous and comprehensive introduction to Power BI, and then put our new knowledge to the test by visualizing and analyzing some open source data! 👩‍💻👩🏽‍💻

If you missed the fun live, don't worry- you can watch the replay here on YouTube, and in the embedded video below! You can watch all eight episodes in the series on demand on YouTube and catch up on all of the blog posts here on Dev.to!

On this episode we cover:

6:59 What is Beep Boop, and why should you go back and watch all the episodes? 💕☺️
14:25 Chloe introduces the module we’ll be working on today, an introduction to Power BI! 📈🔋
18:58 What is Power BI? We walk through the different parts of the tool, and the core concepts we're dealing with, like datasets 🔢, reports 📄, and dashboards 📉.
31:31 Let's talk data modeling and visualizations - from donut charts 🍩 to line charts 📈, Power BI has got it all!
45:17 We’re playing basketball… again! We watch a short video about how the Miami Heat use Power BI to optimize their business. 🏀
50:25 Chloe shows off all of the visualizations she's created of the best dataset ever - squirrel census data from Central Park in New York! 🐿
1:02:10 We hear from our BBs about their favorite things about Beep Boop, and we share some final moments with Frank (for now 😢) 🐶🦴

That's all the Beep Boop we have for now! You can always replay all of the episodes on demand, revisit the blog posts, and say hi to Ornella and Chloe on Twitter! We'd love to hear what you'd like to see on the next season of Beep Boop! 🤖💕

Beep Boop 7: Making AI Chatbots (No Code Required!)

Ornella Altunyan — Fri, 24 Sep 2021 15:07:09 +0000

Thanks to everyone who joined us live for the 7th episode of Beep Boop with the Microsoft Reactor! We started off with a walk down memory lane to one of the first chatbots we've ever interacted with, SmarterChild, and made our very own intelligent chatbot using Power Virtual Agents! 👩‍💻👩🏽‍💻

If you missed the fun live, don't worry- you can watch the replay here on YouTube, and in the embedded video below!

On this episode we cover:

3:01 We discuss the success of the small business we created in the last episode of Beep Boop, Franklin’s Salami Incorporated. 🐶🥩
9:52 We talk a little bit about the Microsoft Imagine Cup and Chloe shoes off her training content about how to pitch your project! 🎤
12:00 As always, we give an overview of what we do here on Beep Boop for our new friends 💕
15:01 We recap what we did on the last episode of Beep Boop. If you'd like an introduction to the Power Platform, we highly suggest you check it out! 🔋
20:08 Chloe shows off the army of bots she has built over the years! 🤖🤖🤖🤖
31:26 We throw it back to the 90s and talk about our first introduction to chatbots, SmarterChild on AOL Instant Messenger 🖥💾
33:05 We log into Power Virtual Agents and create our SmarterTeen chatbot by following the Microsoft Learn module! 🤓
37:44 We cover the concept of Topics and add some to SmarterTeen, test out our bot, and check out how analytics work in Power Virtual Agents. 📈🧪
53:48 There are so many channels for publishing your chatbot! We encourage you to check it out and let us know how it goes! Happy beeping and booping 🤠

Did you have fun? Come back for Episode 8 of Beep Boop next week, where we'll continue learning with the Microsoft Power Platform! See you there!

Beep Boop 6: Artificial Intelligence and Machine Learning with Power Platform

Ornella Altunyan — Tue, 21 Sep 2021 20:48:14 +0000

Thanks to everyone who joined us live for the 6th episode of Beep Boop with the Microsoft Reactor- our first episode of Season 2! We started off with an Introduction to Microsoft Power Platform, set up our goals for this month's season of Beep Boop, and of course, spent some time with Franklin. 👩‍💻👩🏽‍💻

If you missed the fun live, don't worry- you can watch the replay here on YouTube, and in the embedded video below!

On this episode we cover:

6:25 For both our new viewers and our old friends, we talk about what we do here on Beep Boop, and what we're focusing on for Season 2- low-code/no-code with the Microsoft Power Platform! 💕
8:14 Previously on Beep Boop... we circle back and reflect on all of the lessons and learnings from our first season, where we covered Artificial Intelligence and Machine Learning concepts! 🤖
12:26 Chloe gives us an overview on what the Power Platform is, certifications you can get, and how we’ll be leveraging this technology on this season of Beep Boop. 🔋🖥
27:47 Chloe introduces the module we'll be working on in this episode, Introduction to the Power Platform, and refreshes us on our goals! 💪🏽
29:39 We start off with an overview - what is Microsoft Power Platform? We summarize Power Apps, Power Automate, Power BI, and Power Virtual Agents with some examples 😎📊
36:46 Let's get connected - we cover data sources, connectors, triggers and actions, and custom connectors in the Power Platform 🤓
44:30 Stay secure and compliant - next up is data loss prevention, compliance, privacy, and accessibility 🔐
47:01 Franklin joins us on-screen after hearing the word salami 🥩🐶
48:32
We pull it all together with an awesome example! 🥳
54:35 we wrap up with a knowledge check, a preview of next week, and some Franklin! 📚🦴

Did you have fun? Come back for Episode 7 of Beep Boop next week, where we'll continue learning with the Microsoft Power Platform! See you there!

Beep Boop 5: Recap and Preview of Season 2

Ornella Altunyan — Fri, 03 Sep 2021 22:11:55 +0000

Thanks to everyone who joined us live for the 5th episode of Beep Boop with the Microsoft Reactor- our last episode of Season 1! We finished up our web app that uses AI to translate text in real time, reflected on our favorite parts of Season 1, and gave a sneak peak of what's to come next month in Season 2 of Beep Boop. 👩‍💻👩🏽‍💻

If you missed the fun live, don't worry- you can watch the replay here on YouTube, and in the embedded video below!

On this episode we cover:

5:24 As usual, we refresh everyone on what Beep Boop is all about 💕
7:41 ICYMI (in case you missed it) - we refresh everyone on who Franklin is, and why he is essential to the show 🐶🦴🥰
10:04 Chloe starts off the episode with a costume change 🧢 and a musical interlude 🎶
20:00 We chat about our favorite moments of the season, like having so many awesome viewers each week, and learning about cool VS Code features 🖥
32:00 Previously on Beep Boop... we catch folks up on what they missed last episode - we show off the beginning of the web app we built using Python and Flask in VS Code 🐍
44:08 We add Azure Cognitive Services to our web app and create a translator service so that we can have real-time AI translation in our app! Be sure to set up an Azure account so that you can create the translator resource in your portal. 🤖
46:08 We interrupt this broadcast to bring you a little lesson on security - we gather our keys and endpoints to configure the service (off screen, of course) 🔐
49:00 After it's all set up and ready to go, we're able to call the translator service to integrate translation services into our web app (with a little help from our friends!) 👯‍♀️
56:30 Success! We're able to see Azure Cognitive Services providing real time AI translation in our app ‼️‼️
1:00:00 Franklin 🐶 hears his name and arrives on-screen, but is very careful not to give any spoilers about Season 2 🤫
1:01:32 We give a preview of what to expect in Season 2 of Beep Boop. Hope to see you there! 🥳

Did you have fun? Come back for Season 2 of Beep Boop where we'll learn more about Artificial Intelligence and Machine Learning, this time with Power Platform!

Beep Boop 4: Add AI to Your Web App

Ornella Altunyan — Mon, 30 Aug 2021 19:49:39 +0000

Thanks to everyone who joined us live for the 4th episode of Beep Boop with the Microsoft Reactor- we showed off our basketball game web app that we created using a Microsoft Learn module, and started a super cool new module where we're building a new web app with AI capabilities, thanks to Azure Cognitive Services! 👩‍💻👩🏽‍💻

If you missed the fun live, don't worry- you can watch the replay here on YouTube, and in the embedded video below!

On this episode we cover:

6:01 We refresh everyone on what Beep Boop is all about 💕
8:40 We talk about where/how ML and AI showed up in our lives over the past week 🤖
13:08 Previously on Beep Boop... we catch folks up on what they missed last episode - we show off the web app we built with Azure and VS Code! You can play with a version of the app online 🔁🏀
24:45 Chloe introduces the Microsoft Learn module we'll be working on in this session, building an AI web app using Python and Flask in VS Code! 🐍💻
28:13 We double check that we have VS Code and the correct version of Python installed, and create the project directory for our web app 📁
30:55 Chloe gives us an overview of Flask, we create and activate a Python virtual environment, and install Flask and other libraries inside of the environment. 👩🏽‍💻📚
37:23 Ornella shares a hot tip for productivity in VS Code 🤓😎
39:55 Chloe goes over the Flask fundamentals - routes (no relation to GPS), methods/verbs, and templates 📍🔣
46:11 We create our core Flask application and add the HTML template ⌨️🖱
52:35 We get a first glimpse of the web app we made using flask! 🥳🥰
59:05 Franklin answers the question of the day 🐶🦴

Did you have fun? Check out Beep Boop 5: Recap and Preview of Season 2 where we finish up our web app and wrap up Season 1 of Beep Boop!

To get ready for next week’s session, make sure you set up your student Azure account so you can use Azure Cognitive Services alongside us!

Beep Boop 3: Create a Web App with Data and ML

Ornella Altunyan — Fri, 20 Aug 2021 21:10:04 +0000

Thanks to everyone who joined us live for the 3rd episode of Beep Boop with the Microsoft Reactor- we started an awesome new Microsoft Learn module where we used a machine learning model to simulate basketball games with the data we cleaned up last time! 👩‍💻👩🏽‍💻

If you missed the fun live, don't worry- you can watch the replay here on YouTube, and in the embedded video below!

On this episode we cover:

5:20 Chloe tells us about an AI/ML talk she did this weekend where she mentioned Beep Boop 🤖💕
9:20 Previously on Beep Boop... we catch folks up on what they missed last episode! 🔁
14:17 We introduce what we'll be working on this week, a Microsoft Learn module where we'll learn how to create a web app that uses data to make decisions on the basketball court 🏀
15:08 One of our awesome viewers tells us about a VS Code extension that rainbow-ifies CSV files! 🌈📁
16:20 A refresher on what tools you'll need to download to follow along with this session - VS Code, an Azure account, and a few other bits and bobs ☁️🧵
18:04 Now that our data is squeaky clean 🧼, let's double check that we've set up our local environment
25:14 We merge the two datasets, and add identifiers to our DataFrame so that we know who's who 👀🐰
30:38 We start our new machine learning adventure by making a model, and calculating some standard deviations ➗🤓
43:32 First glimpse of our LIVE web app that we just made together! 😳💻
50:45 We learn about the Code Tour extension in VS Code and how it can guide you through adding the JavaScript to your web app 👩🏽‍💻
55:55 Franklin gives us the scoop on how he feels about creating basketball game simulations with the Tune Squad 🐶🦴

Did you have fun? Check out Beep Boop 4: Add AI to Your Web App where we add even more cool features to our web app - see ya then, friends!

Beep Boop 2: Use Machine Learning Models to Prep Your Data

Ornella Altunyan — Fri, 13 Aug 2021 22:21:59 +0000

Thanks to everyone who joined us live for the 2nd episode of Beep Boop with the Microsoft Reactor- we had a lovely time getting right back into our Microsoft Learn module and creating a machine learning model to clean up our data! 👩‍💻👩🏽‍💻

If you missed the fun live, don't worry- you can watch the replay here on YouTube, and in the embedded video below!

On this episode we cover:

7:36 Ornella and Chloe recap what we do on Beep Boop and the ~vibes~ we want to cultivate on the show! 🤖💕
11:24 Previously on Beep Boop... we catch folks up on what we did last time, and revisit how to set up your local environment for data science coding and download VS Code so we can pick up where we left off! ☁️🏀
16:56 Frank pops in to tell us what he thinks about AI and ML 🐶🦴
18:21 We discover data that represents more than one population and talk about why that might be useful for other scenarios 🕵️‍♀️👥
34:49 We manipulate the data so we can easily distinguish between Tune Squad players 🐰🐥 and real NBA players ⛹️‍♂️
39:38 Magically impute (add in) data to our DataFrame, and get closer to a PERfect set of data 🤓
47:27 Using Machine Learning techniques like linear regression and cross-validation to finally complete our dataset - no more NaNs! 😎

Did you have fun? Check out Beep Boop 3: Create a Web App with Data and ML where we take our data on an adventure - see ya then, friends!

Beep Boop 1: Intro to AI/ML

Ornella Altunyan — Thu, 05 Aug 2021 23:12:18 +0000

Thanks to everyone who joined us live for our 1st episode of Beep Boop with the Microsoft Reactor- it was so much fun chatting about our AI/ML experience and learning alongside y'all as we beeped and booped through some of our first Microsoft Learn module! 👩‍💻👩🏽‍💻

If you missed the fun live, don't worry- you can watch the replay here on YouTube, and in the embedded video below!

On this episode we cover:

1:36 Ornella and Chloe explain what exactly is a beep boop and what we're doing on the show! 🤖
9:23 We chat about PER (player efficiency ratings in basketball), why ball is indeed life, and intro the first Learn module that we'll be working on 🏀💯
16:25 Download VS Code, Python and the Python extension to beep boop alongside us 👯‍♀️
17:24 Making sure everything is ready to goooo by setting up your local environment for data science coding and bringing in that dataset
30:08 We're on the hunt for missing values in our dataset and we get rid of them by dropping rows and columns - NaN no thank you 🙅🏽‍♀️
40:15 We check for outliers and reminisce about the childhood magic of box 📦 and whisker 🐱 plots
49:17 We check the distribution of the data using histograms 📊 (not to be confused with historical instagrams 🎞)
59:11 A meet n greet with our show mascot, Franklin 🐶🦴

Did you have fun? Check out Beep Boop 2: Use Machine Learning Models to Prep Your Data where we continue on this PERfect journey together - see ya then, friends!

Collaborate on research papers with GitHub

Ornella Altunyan — Tue, 04 May 2021 19:57:19 +0000

GitHub is well-known as a platform where software developers host their code and collaborate with their teams on a project. In this blog post, we'll show you how you can use the GitHub model to do the same thing and collaborate seamlessly on your research papers.

This blog post is co-authored with Dmitry Soshkinov, because we believe that GitHub is a great technology and tool to be used beyond pure software development.

Git, GitHub, and how it all works

If you've never worked with GitHub before, check out the Microsoft Learn module for a step-by-step introduction.

The first thing you'll want to do is set up Git. Git is the version control system that runs behind the scenes of any GitHub project-- it's what allows you to collaborate with others, go back to previous versions of your project, and view changes made by different members of your team. You may want to use Git from a command-line, but in the beginning, it might be easier to use the GitHub Desktop client.

Projects on GitHub are organized in repositories. You'll create a new repository for your research paper, and choose who you want to have access. All your files, whether you're using Markdown, LaTeX, or another typesetting or markup language (more on that later!) will live in this repository. You'll want to clone the repository to your local machine, so that you have a copy of your files.

The source of truth for your paper will live on the main branch of your repository -- this branch is initialized when you create your repository. You can create multiple branches for different sections of your paper, and edit and merge them into your main branch when you're finished. A commit is a snapshot of your repository at a given moment, and it might contain a set of changes that you've made to the information on a specific branch.

This is just a short introduction to all the features you can take advantage of when you use GitHub to collaborate on your research papers. Keep reading for more information, and a sample workflow that you can use to get started.

What should and should not be stored in Git

It is important to understand that GitHub is not a replacement for file storage, or a convenient storage for binary files. It was originally designed to be used as a source code repository, and thus it allows you to track changes between text documents. If you are planning on collaborating on Word documents, setting up a shared OneDrive location is a much better choice. For this reason, many people don’t consider GitHub to be a convenient collaboration platform for editing documents. However, scientists often write their papers in text format, most often – TeX or LaTeX. This makes it very convenient to use GitHub as a collaboration platform. It is one of the reasons we believe that GitHub is a very beneficial collaboration platform for scientists.

Why GitHub?

Using Git will give you many advantages:

Tracking changes between different editions of a document. Text documents can be easily compared to each other using the GitHub interface. This is useful even when you are working on a paper alone, because all changes are tracked, and you can always roll back to any previous state.
Working on different branches of the document and merging branches together. There are a few different styles of using Git for collaboration, so-called Git workflows. With branches, you and your collaborators can all work on specific parts of your project without conflicts, for prolonged periods of time.
Accepting contributions to your paper/code from outside. Github has a convenient mechanism of pull requests – suggestions from other users, that you can then approve and merge into the main content. For example, the Web Development for Beginners course was developed and hosted on GitHub originally by a group of around 10 people, and now it has more than 50 contributors, including people who are translating the course into different languages.
If you are very advanced (or have some friends who are into DevOps), you can setup GitHub Actions to automatically create a new PDF version of your paper every time changes are made to the repository.

LaTeX or Markdown?

Most scientists write their papers in LaTeX, mostly because it provides easy access to a lot of workflows in academia, like paper templates. There are also some good collaboration platforms specific to TeX, for example, Overleaf. However, it won't give you full control of your versioning and collaboration features like Git.

However, writing in LaTeX also includes quite a bit of overhead, meaning that many layout features are quite verbose, for example:

\subsection{Section 1}
\begin{itemize}
  \item Item 1
  \item Item 2
\end{itemize}

In the world of software development, there is a perfect format for writing formatted text documents -- Markdown. Markdown looks just like a plain text document, for example, the text above would be formatted like this:

## Section 1

* Item 1
* Item 2

This document is much easier to read as plain text, but it is also formatted into a nice looking document by Markdown processors. There are also ways to include TeX formulae into markdown using specific syntax.

In fact, I've been writing all of my blog posts and most text content in Markdown for a few years, including posts with formulae. For scientific writing, the great Markdown processor (as well as live editing environment) integrated with TeX is madoko – I highly recommend you check it out. You can use it from the web interface (which has GitHub integration), and there's also an open-source command-line tool to convert your Markdown writing into either LaTeX, or directly to PDF.

While you may continue using LaTeX with Git, I encourage you to look into markdown-based writing options. By the way, if you have some writing in different formats, such as Microsoft Word documents, it can be converted to Markdown using a tool called Pandoc.

Sample workflow

The main thing that Git does is allow you to structure your writing (whether it is code or a scientific paper) into chunks called commits. Your code is tracked in a local repository that lives on your computer, and once you have made some changes, you commit them to save. Then, you can also synchronize your commits with others by using a remote common repository, called upstream.

Sound complicated? When using GitHub Desktop most of the tasks are completely automated for you. Below, we describe the simplest way you can collaborate on a paper with your colleagues.

Create a new repository on GitHub. I set the visibility to Private so I can decide which collaborators I’d like to invite to contribute later.
Select Set up in Desktop to quickly set up your repository in GitHub Desktop.
Next, you'll need to create a local clone of the repository on your machine. You may be prompted to reauthenticate to GitHub during this step.
I already have a couple of Markdown files that I've started working on saved to my computer. I can select View the files of your repository in Finder to open the folder where my local copy of the repository is stored, and drag in the files for my Table of Contents, Section 1, and Bibliography from my computer.
Now, when I go back to GitHub Desktop, I can see those files have been added to my repository. I want to commit those files to the main branch. I can also publish my branch to push those changes to GitHub, and make them accessible to others who I'll collaborate with.
Next, I'm going to create a new branch so I can go off and work on Section 2 of my paper. I'll automatically end up on that branch after it has been created. There are a couple of options you'll be able to select from for making changes to your file in this branch:
- You can create a Pull Request from your current branch -- if I wanted my colleague to be able to review the changes I've made in this branch, I'd use this option and send them the PR for review.
- You can also open the repository in your external editor. I use VS Code to edit my files, so I can add section 2 of my paper there, and then commit it to my section2 branch.
- If I already have section 2 of my paper saved somewhere on my computer, or if my colleague has sent me something they've worked on, I can follow the same workflow as above and check out the files in my repository on my machine, and add/remove files that way.
- If I just need to make a small change, I'd open my repository in the browser and edit from there.
I can open my repository in GitHub to check out all of the files and information. This is the link I’d send to a colleague if I wanted them to be able to clone the code onto their local machine, and help me out with some sections.

Since I’ve made my repository private, I’ll need to add collaborators in the Settings pane.
Once I’m happy with Section 2 of my paper, I can go ahead and merge it into the main branch of my repository. I switch over to the main branch, then choose a branch to merge into main, and choose section2. Then, I’ll want to push my changes back up to GitHub so that the main branch is updated with the newest changes for any future collaborators.

This is one example of a Git workflow you can use in conjunction with GitHub Desktop to collaborate on a research paper with your colleagues. There are several other ways that may serve your needs better—you may want to use the command line with VS Code, or edit your files on GitHub in the browser. Whatever method works for you is the best method, as long as you’re able to accomplish your goals.

DEV Community: Ornella Altunyan

Beep Boop 8: Visualize, Analyze, and Collab on Data (No Code Required!)

Beep Boop 7: Making AI Chatbots (No Code Required!)

Beep Boop 6: Artificial Intelligence and Machine Learning with Power Platform

Beep Boop 5: Recap and Preview of Season 2

Beep Boop 4: Add AI to Your Web App

Beep Boop 3: Create a Web App with Data and ML

Beep Boop 2: Use Machine Learning Models to Prep Your Data

Beep Boop 1: Intro to AI/ML

Collaborate on research papers with GitHub

Git, GitHub, and how it all works

What should and should not be stored in Git

Why GitHub?

LaTeX or Markdown?

Sample workflow

Further reading and useful links