- Here is a list of Top 10 Data Science Webinars Organized by Analytics Vidhya in 2020
- These Data Science Webinars are ranked on the basis of their number of registrations and quality
- By no means is this list exhaustive. Feel free to add more in the comments below
Learning data science has always been an uphill task for me whether through courses or videos on YouTube mainly because it lacked practical applications and career guidance from the industry experts. To fill this knowledge gap I have found webinars and meetups to be a perfect substitute. Since Coronavirus has disrupted meetups, webinars have completely taken over.
I have personally found Webinars to be context focussed, code-rich, and application focussed talk-sessions and that’s why I love them. In this article, I have highlighted some of the best webinars organized in the year 2020. These range from beginner-friendly career topics all the way up to advanced topics like that of Transfer Learning in NLP.
I have chosen the data science webinars according to their registration numbers and topic quality. Hope you enjoy it!
As mentioned above, data science webinars are a great way to learn the application focussed topic. If you are looking to begin your journey into the world of data science I would highly recommend you take up the comprehensive AI and ML Blackbelt+ course. Along with 14+ courses and 39+ projects, it comes with 1:1 mentorship sessions so that you are never off track from your goals!
Top 10 Data Science Webinars –
- Data Science vs. Data Engineering – Can you really separate them? – 4500
- Going Beyond your First ML Project – 1535
- How to Deploy your ML Model? – 1405
- Storytelling using Visualizations – 1240
- Career Transition Into Data Science – 1232
- Getting Started with Recommendation Engines – 875
- Problem Solving In Business Analytics and Data Science – 834
- Business Analytics Isn’t Just About Model Building – 698
- An Introduction to Transfer Learning in NLP using HuggingFace – 646
- Getting Started with Natural Language Processing – 595
“A data scientist is only as good as the data he/she has access to.”
Most aspirants in the data science field want to land the coveted role of a “Data Scientist”. But did you know that tech giants such as Netflix, Facebook, Amazon, etc are hiring Data Engineers like never before to process the massive amount of data they are gathering!
Surprising, isn’t it? On second thought, not really. After all, “A data scientist is only as good as the data he/she has access to.”
A majority of people don’t even know what is data engineering and what is the role of data engineers. This is the perfect webinar to understand the difference between a data scientist and a data engineer and their industries. More than 4,000 people registered for this webinar!
This webinar is a great opportunity for you to hear from eminent industry experts who have seen both the data science and data engineering industries up close. Hear and learn from Kunal Jain (Founder & CEO, Analytics Vidhya), Ujjyaini Mitra (Head of Data for Zee5), K. Sankaran (Director, Data Science, LatentView Analytics) and Sachin Arora (Partner and Head of Lighthouse KMPG in India), as they draw upon their experience to help you navigate through these questions. See you at the webinar!
Oh, you’ve picked machine learning as your future career. You successfully completed your first machine learning project as well. Great! But what’s next? How do you go beyond the basics and take that next step, the big leap, that will make you industry-ready?
How can you build your profile in machine learning that will take you beyond the basics and into the realm of what the industry wants?
This super-exciting and interesting Webinar recording will help you in navigating your way through the bookish machine learning concepts to practical project learnings!
There is no real value generated to the business until your machine learning model is deployed and servicing real-world traffic no matter how good your model is!
One of the biggest challenges in the enterprise today is to integrate the developed machine learning model into a decision process. There is no real value generated to the business until your machine learning model is deployed and servicing real-world traffic no matter how good your model is!
If you have this gap in your data science portfolio on the model deployment part then you must watch this webinar.
In this webinar, Srivatsan Srinivasan will discuss moving data science from research to production through some real-world use cases. You will learn about various techniques and patterns to deploy and integrate the model with your business process.
Most often, Data Scientists get so involved with the process of model building that they forget the most crucial part – Turning insights into the form of stories!
Anand S, Founder and CEO of Gramener, is frequently approached by professionals asking questions like – where should they source the data from. But once you’ve got the data, the next questions to ask are:
- How do you get interesting stories out of this data?
- And how do you narrate these stories?
Are there patterns of questions that we can pose to the data and is there a systematic and structured way by which we can explore it? This talk by Anand S will answer these questions and more.
As organizations are realizing the potential of data science and machine learning, they are adapting to the trend by rapidly hiring prospective talent.
- Can non-technical people transition to Data Science?
- Will an experienced professional be treated as a relative fresher when he/she makes a switch to data science?
- Which Data Science role should they consider?
- Will their existing skill-set be useful in Data Science?
- and more such questions.
If you are facing these questions, this is the perfect webinar recording for you! This webinar features speakers from Analytics Vidhya and eminent personalities from KPMG.
From Amazon to Netflix and Google to Goodreads, recommendation engines are one of the most widely used applications of machine learning techniques. Excited?
In today’s world, every customer has multiple choices. For example, If we’re looking for a book to read without any specific idea of what we want, there’s a wide range of possibilities how our search might pan out. We might waste a lot of time browsing around on the internet and trawling through various sites hoping to strike gold. We might look for recommendations from other people.
But if there was a site or app which could recommend books based on what we have read previously, that would be a massive help. Instead of wasting time on various sites, we could just log in, and voila!
In this webinar hosted by Dr. Sarabjot Singh Anand, an industry veteran who brings an immensely rich machine learning background, you will learn all about how recommendation engines work and how to get started with them as an analyst or data science professional.
Problem Solving is undoubtedly the most important skill in business analytics and data science. A structured thinking approach will not only help in constructing a clear and crisp problem statement but also help in communicating the results with the stakeholders.
In this webinar, Madhukar will talk about the following challenges and provide people with frameworks and best practices on structured thinking:
- How to take ambiguous business problems and then break them into structured data science problems?
- How to present your analysis and business insights in an impactful manner?
- How to do clear and structured communications which people can easily understand?
Models don’t solve business problems, people do.
One of the biggest presumptions amongst data scientists is that machine learning is all about the state of the art machine learning models but of course that’s totally false!
Business actions can’t be achieved alone, it requires collaboration along with in-depth domain knowledge. It is not the models you build but the business actions they translate to that create your impact as a data professional.
In this webinar, Eric will focus on how to maximize your impact by focusing less on the models you build and by focusing more on translating those models to definitive business actions.
Interested in NLP? I am sure must have come across the recent developments in the field of transformer architectures and Transfer Learning.
The NLP field has come leaps and bounds in the last 3-4 years. And HuggingFace has been at the forefront of bringing the state-of-the-art NLP libraries to the NLP community. So who better to hear from about this than HuggingFace’s Co-Founder Thomas Wolf?
In this webinar, Thomas will start by introducing the recent breakthroughs in NLP that resulted from the combination of Transfer Learning schemes and Transformer architectures.
Just starting out on your journey in the field of Natural Language Processing? This is the perfect webinar for you!
In the past couple of years, Natural Language Processing (NLP) or processing of textual data has seen great interest and research. Text is not just another unstructured type of data, it has a lot more to it than what meets the eye. Textual data is a representation of our thoughts, ideas, knowledge, and even communication.
In this webinar, Raghav Bali will discuss the basics of natural language processing, creating word embedding, and developing models to perform various NLP tasks such as sentiment analysis, auto-correction, and much more.
I have listed down the Top 10 Data Science and Machine Learning Webinars for the year 2020. These range from basic career guidance by experts to the advanced technical topic of transfer learning in NLP. You can skip to the webinar which suits your interest.
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