Dina Bavli
Data Scientist
Haifa, Israel
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Data Scientist with experience in NLP, Graph theory, NetworkX, churn prediction, and ASR (automated speech recognition). Her Master's thesis deals with classifying and characterizing persuasion. She is a former teaching assistant for ML and an experienced international public speaker. She is a data science content writer for workshops, meetups, and online courses, and an official author of the Towards Data Science and Better Programming publications. Spend a significant part of the summer at the German aerospace data science center (DLR).
Dina is passionate about data, sharing knowledge, and contributing to society and open source.
Whenever she can't find a sufficient tutorial, she creates one.
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Stalk Your Way to a New Job- And Not in a Cringe Way
Have you ever had that one job interview that felt different?
That one time you finished that interview and you just shined like never before?
What was different?
In this lecture, I’ll offer methodological guidelines for collecting the intelligence that will make you shine on your way to your new position, and that will also allow you to better know where you’re getting yourself into.
Everybody Is an Influencer– Which Influencer Are You?
We live in an era where most human communication is online across social platforms, and the content of those platforms influences our day-to-day decisions.
But, who influences us? And how?
Researching online influence and persuasion, I answered just that.
In this lecture, I will intuitively cover basic concepts of SNA (social network analysis) and explain how they are used to define different types of influencers and quantify influence.
Sometimes research is about the definitions.
Let me show you and suggest how you can implement those concepts.
Begin at the Beginning: Data Exploration With Python
Data exploration aka exploratory data analysis or EDA. The whys and the how-to using a few lines of Python code (and a few great Python packages)
The data science pipeline begins in data exploration, also known as exploratory data analysis or EDA.
In this step of the data analysis, we better understand the data.
Understanding which variables are important and which are not.
Identifying missing values, outliers, or human error.
Understanding the correlation and relationships between variables or their absence.
EDA optimizes the observations into a dataset and reduces potential mistakes later in the process.
Deep Into the Tweet
Let’s scratch the twitter meta-data together and go below the surface with tweepy. Want to find out if the tweets you follow are trying to persuade you to do things? Have the feeling the advocates for some issues use certain emotions to push you in certain directions? Now you can find out. Delve deep into the tweet with me, I’ll take you on a journey through mining twitter API, how the Twitter meta-data is structured and how to reach a specific field, analyzing text, emojis, emotions, and more. An introduction to a variety of python packages. “I show you how deep the rabbit hole goes.” (The Matrix, Morpheus) Whether or not you go down is your choice. A practical lecture, don’t worry if you don’t follow all the code lines - access to the lecture notebooks is promised.
Deep into the Tweet: Analyzing twitter data - Hands-on using python
Twitter is a beautiful atmosphere to examine different types of users, relationships, and information. It is a fertile ground for many types of research. Delve deep into the tweet with me. I’ll take you on a journey through mining twitter API, how the Twitter meta-data is structured and how to reach a specific field, analyzing text, emojis, sentiments, geolocation, and more. An introduction to a variety of python packages. “I show you how deep the rabbit hole goes.” (The Matrix, Morpheus) Whether or not you go down is your choice.
You'll learn how to create a Twitter dataset with at least twice as many features as exciting ones.
No matter what you choose to do with them. Allow me to introduce your possibilities.
A hands-on workshop using python.
Predicting User Behavior Using Language Models
When dealing with user data logs, each user might have several logs of different events. In this lecture, I would like to introduce the concept of using NLP models to sequence those events in order to predict future events per user while explaining intuitively those models.
More Than Words — Your Emoji Says A Lot
The use of emojis is extended in everyday life.
Analyzing texts from social media that contain emojis may cause unreadability due to encoding in the parsing process. Using emoji interpreter Python packages can overcome this difficulty.
Emojis are a language of their own, which is something to consider while analyzing texts that contain them.
Besides, working with emojis is always fun.
This lecture will cover a brief overview of three emoji interpreter Python packages, the pros, cons, when, and how to use them.
Life, Death, and Shopping
A step-by-step introduction to purchase prediction. Also applicable to survival analysis and churn prediction. Including implementation in PySpark.
When dealing with survival analysis, the model's success is predicting death correctly. But it can also predict an engine failure, abandonment, or even purchases.
In purchase prediction, survival analysis, or churn prediction, the data is usually labeled or artificially labeled by a set of rules- such as inactivity for 30 days equivalent to churn. But the data structure is different from classical machine learning, and the data handling and modeling are different accordingly.
In this lecture, we will cover the data structures and aggregations for such analysis focusing on time aggregations using pyspark and what NLP got to do with any of it.
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