Overview
- Here is a list of the 10 best data science webinars hosted by DataPeaker at 2020
- These data science webinars are ranked based on number of registrations and quality
- This list is by no means exhaustive.. Feel free to add more in the comments below..
Introduction
Learning data science has always been a chore for me, either through courses or videos on YouTube, mainly because it lacked practical applications and professional guidance from industry experts. To fill this void of knowledge, I have found webinars and meetings to be a perfect substitute. Since the coronavirus has disrupted meetings, webinars have completely taken over.
Personally, I have found that webinars are focused on context, rich in code and app-focused chat sessions and that's why I love them. In this post, I have highlighted some of the best webinars organized in the year 2020. These range from professional topics for beginners to advanced topics such as Transfer Learning in NLP..
I have chosen the data science webinars based on their registration numbers and the quality of the topic. I hope you enjoy it!
As mentioned previously, data science webinars are a great way to learn the application-centric topic. If you are looking to start your journey into the world of data science, I highly recommend that you do the full AI and ML Blackbelt Course +. Along with more than 14 courses and more than 39 Projects, comes with tutoring sessions 1: 1 so you never deviate from your goals.
The 10 best data science webinars:
- Data Science and Data Engineering: Can they really be separated? – 4500
- Beyond your first ML project – 1535
- How to put your machine learning model into practice? – 1405
- Narration using visualizations – 1240
- Career transition to data science – 1232
- Introduction to recommendation engines – 875
- Problem solving in business analytics and data science – 834
- Business analytics isn't just about modeling – 698
- Introduction to Transfer of Learning in NLP with HuggingFace – 646
- Introduction to natural language processing – 595
“A data scientist is only as good as the data he 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 like Netflix, Facebook, Amazon, etc. are hiring data engineers like never before to process the massive amount of data they are collecting?
Surprising, ¿no? Second thought, not really. After all, “A data scientist is only as good as the data he has access to”.
Most people don't even know what data engineering is 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 of 4.000 people signed up for this webinar!
This webinar is a great opportunity for you to hear from eminent industry experts who have taken a closer look at the data science and data engineering industries.. Hear and learn from Kunal Jain (Founder and CEO, DataPeaker), Ujjaini Mitra (Zee5 Data Chief), K. Sankaran (Director, Data science, LatentView Analytics) and Sachin Arora (Partner and Head of Lighthouse KMPG in India) , since they focus on your experience to help you navigate through these questions. See you at the webinar!
Oh, has chosen machine learning as his future career. He also successfully completed his first machine learning project.. Excellent! But, Whats Next? How do you transcend the basics and take the next step, the big jump, that will prepare you for the industry?
How can you build your profile on machine learning that takes you beyond the basics and into the realm of what the industry wants?
This super exciting and engaging webinar recording will help you navigate through bookish machine learning concepts to hands-on project learnings!!
No real value is added to the company until its machine learning model is implemented and real-world traffic is served, no matter how good your model is.
One of the biggest challenges for the company today is integrating the machine learning model developed into a decision procedure. No real value is added to the company until its machine learning model is implemented and real-world traffic is served, no matter how good your model is.
If you have this gap in your data science portfolio in the implementation part of the model, must see this webinar.
In this webinar, Srivatsan Srinivasan will discuss how to move data science from research to production through some real-world use cases. You will learn about various techniques and patterns to implement and integrate the model with your business procedure.
Most of the time, data scientists get so involved in the model building procedure that they forget the most crucial part: Turn knowledge into stories!
Anand S, founder and CEO of Gramener, is approached many times by professionals who ask questions such as: Where should they get the data? But once you have the data, the next questions to ask are:
- How do you get interesting stories from this data?
- And how do you tell these stories?
Are there patterns of questions we can ask the data and is there a systematic and structured way to explore them? This talk by Anand S will answer these questions and more.
As institutions are realizing the potential of data science and machine learning, they are catching up with the trend by quickly recruiting potential talent.
Despite this, Switching to a career in data science has its own challenges for both beginners and experienced professionals.. Some of the most common challenges faced by aspiring Data Science are:
- Can non-technical people make the transition to data science?
- Will a seasoned professional be treated as relatively cooler when switching to data science?
- What data science role should they consider?
- Will Your Existing Skill Set Be Useful In Data Science?
- and more similar questions.
If you are faced with these questions, This is the perfect webinar recording for you!! This webinar features speakers from DataPeaker 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 the actual world, each client has multiple options. As an example, if we are looking for a book to read without a specific idea of what we want, there is a wide range of possibilities of how our search could turn out. We could waste a lot of time surfing the Internet and crawling various sites in the hope of finding gold.. We could seek recommendations from other people.
But if there was a site or an app that could recommend books based on what we've previously read, It would be a great help. Instead of wasting time on multiple sites, we could just log in and voila!
In this webinar organized by Dr.. Sarabjot Singh Anand, an industry veteran who brings an immensely rich experience in machine learning, you will learn all about how recommendation engines work and how to get started as an analyst or data science professional.
Problem solving is, undoubtedly, the most important skill in business analysis and data science. A structured thinking approach will not only help build a clear and crisp statement of the problem, it will also help communicate the results to stakeholders.
In this webinar, Madhukar will speak 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 down into structured data science problems?
- How to present your analysis and business information in an impactful way?
- How to make clear and structured communications that people can easily understand?
Models do not solve business problems, people yes.
One of the biggest assumptions among data scientists is that machine learning has to do with next-generation machine learning models., but, of course, That's totally untrue!
Commercial actions cannot be achieved alone, needs collaboration along with deep domain knowledge. They are not the models you build, but the commercial actions into which those that create your impact as a data professional are translated.
In this webinar, Eric will focus on how to maximize your impact by focusing less on the models you create and focusing more on translating those models into definitive business actions..
Interested in NLP? I'm sure you must have come across recent developments in the field of transformer architectures and transfer learning.
The field of NLP has advanced by leaps and bounds in recent 3-4 years. And HuggingFace has been at the forefront in bringing state-of-the-art NLP libraries to the NLP community.. Then, Who better to hear about this than the co-founder of HuggingFace, Thomas Wolf?
In this webinar, Thomas will begin by presenting the recent advances in NLP that resulted from the combination of transfer learning schemas and transformation architectures..
Are you starting your journey in the field of natural language processing?? This is the perfect webinar for you!!
In recent years, natural language processing (PNL) o the processing of textual data has generated great interest and research. Text is not just another type of unstructured data, has much more than 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 embeds and developing models to perform various NLP tasks, like sentiment analysis, autocorrect and much more.
Final notes
I have listed the 10 Best Data Science and Machine Learning Webinars for the Year 2020. These range from basic professional guidance by experts to the advanced technical subject of transfer learning in NLP.. You can jump to the webinar that suits your interests.
Hope this helps you take a step towards achieving your goal!!
