The Top 10 Data Science Webinars of 2020 You Don‘t Want to Miss

Introduction

2020 was a challenging year that forced many in-person events and learning opportunities to shift to a virtual format. However, this opened the doors for more people than ever to access high-quality webinars on data science topics from the comfort of their homes. Webinars provide a unique opportunity to learn directly from industry experts, ask questions, and gain practical knowledge – often for free and without travel.

As an aspiring or current data scientist looking to upskill, webinars should absolutely be part of your learning toolkit. To give you a head start, I‘ve curated a list of the top 10 data science webinars from 2020. This diverse selection covers a range of valuable topics for data scientists of all skill levels, from beginner career advice to advanced technical skills.

So grab a coffee, find a comfy chair, and get ready to learn from some of the best data science webinars of the year!

1. Data Science vs. Data Engineering – Can you really separate them?

There‘s no question the data scientist is the "sexiest job of the 21st century." But data engineers play an equally important role by building the data infrastructure that makes data science possible. In fact, the most effective data science can‘t happen without high-quality, well-engineered data.

In this popular webinar with over 5,000 registrants, industry leaders came together to tackle the key question: what‘s the difference between data science and data engineering, and can you really separate the two?

Speakers included:

• Abishek Shivkumar, Senior Data Scientist at Walmart
• Vinay Hanumaiah, Chief Architect at ArangoDB
• Wes Noonan, Data Engineer at Dynata

Key takeaways:

• Data engineers and data scientists have different but complementary skill sets
• Data engineers focus on developing the "data plumbing" – the pipelines and infrastructure for collecting, storing, and making data available
• Data scientists focus on analysis, modeling, and extracting insights from the data
• The two roles need to collaborate closely for data science to be successful
• Many companies are shifting to "hybrid" roles and teams that combine data science and data engineering skills

As Vinay Hanumaiah explained, "Today, with the overlap between research and
production blurring, it is becoming more and more important for data scientists to be aware of production considerations, scalable code, and general engineering best practices."

Screenshots from the "Data Science vs. Data Engineering" webinar by Analytics Vidhya

If you‘re an aspiring data scientist, don‘t neglect data engineering! Having an understanding and appreciation of data engineering concepts will make you a more well-rounded and effective data scientist. And if you‘re a data engineer, stay connected to the "why" of what you‘re building by interfacing with your data science colleagues.

2. Telling Stories with Data

As a data scientist, your job isn‘t just to analyze data and build models. Inevitably, you‘ll need to communicate your findings to stakeholders and business leaders to drive decisions and actions. That‘s where data storytelling comes in – it‘s the linchpin skill that connects raw data to real impact.

"Data storytelling is a structured approach for communicating data insights, and it involves a combination of three key elements: data, visuals, and narrative." – Brent Dykes

In this compelling webinar led by Brent Dykes, author of the book "Effective Data Storytelling," you‘ll learn frameworks and techniques for turning data into impactful stories.

Key skills covered:

• Understand your audience and tailoring your message
• Choosing the best visual formats for your data
• Crafting a compelling data narrative
• Combining the right data, visuals, and narrative
• Common pitfalls to avoid in data storytelling

Effective data storytelling can supercharge your impact as a data scientist. You can have the best data analysis in the world, but it‘s meaningless if you can‘t communicate it to drive insights and actions. Dial up your data storytelling skills with this webinar and you‘ll be on your way to becoming an indispensable data science storyteller!

3. How to Deploy Machine Learning Models

So you‘ve built an awesome machine learning model – now what? Turning that model into a production-ready system that can deliver real business value is a whole other ballgame. In fact, many data scientists struggle with the challenges of deploying models, like:

• Integrating with production data systems and pipelines
• Automating and monitoring model performance
• Scaling model training and inference
• Maintaining and updating models over time

In this tactical webinar, expert Vin Vashishta breaks down the key steps for taking models from prototype to production. You‘ll learn:

• An overview of the model deployment workflow
• Tools and frameworks for serving models in production (e.g. Flask, Docker, Kubernetes)
• Best practices for model versioning, monitoring, and maintenance
• Common deployment architectures and when to use them
• Real-world case studies and demos of model deployment

As Vin explains, "The most time consuming, technically complex, and failure-prone aspects of data science all live in the deployment process. Mastering this step is critical for ensuring your models are delivering real value."

Especially with the rise of MLOps, deployment skills are becoming a must-have for data scientists. Get ahead of the curve and beef up your deployment chops with this insightful webinar!

Diagram breaking down the steps of ML model deployment from the "How to Deploy Machine Learning Models" webinar.

4. Getting Started with Recommender Systems

Recommender systems are one of the most pervasive and impactful applications of data science and machine learning. From Netflix suggesting shows to Amazon recommending products to Spotify personalizing playlists, recommendations power many of our favorite apps and services.

Building effective recommender systems requires an understanding of techniques and algorithms like:

• Collaborative filtering
• Content-based filtering
• Matrix factorization
• Deep learning

In this beginner-friendly webinar, data science educator Daniel Bourke walks you through the foundations of how recommender systems work and how to start building them yourself in Python.

You‘ll learn:

• An intuitive, high-level overview of recommender system approaches
• How to build a basic collaborative filtering model
• Evaluating and testing recommender systems
• More advanced techniques like factorization machines and autoencoders
• Practical tips for feature engineering, optimization, and scaling

Recommender systems are a fantastic (and lucrative) specialization for data scientists. Get your feet wet with this intro webinar and you‘ll be on your way to building recommenders of your own!

5. Solving Business Problems with Analytics

As a data scientist, your ultimate goal is to solve real business problems, not just build the most sophisticated models. You need to deeply understand the business context, translate ambiguous problems to concrete analytics questions, and communicate your insights to stakeholders.

This webinar brings together analytics leaders to share their experiences and advice for solving business problems:

• Emily Robinson, Data Scientist at Warby Parker
• Dan Mallinger, Head of Data Science at Wish
• Robert Chang, Director of Business Analytics at Airbnb

Key themes:

• Collaborating with stakeholders to understand business objectives
• Breaking down business problems into analytics use cases
• Balancing technical skills with business and domain knowledge
• Managing a data science project from problem scoping to communicating results
• Building trust and relationships with stakeholders

"You can have the most elegant analysis, but if it doesn‘t align with solving the business problem, it‘s not useful. The analysis only matters to the extent it influences decisions and drives impact." – Emily Robinson

Don‘t fall into the trap of being a hammer in search of a nail. Hone your skills for understanding, framing, and solving business problems with this essential webinar.

Conclusion and Looking Ahead

2020 saw a boom in high-quality, accessible data science webinars and there‘s never been a better time to learn. I hope this curated list gives you a jumping off point to upskill with some of the best webinars of the year.

The webinars here covered a diverse set of valuable topics like:

• Data science vs. data engineering
• Data storytelling
• Machine learning model deployment
• Recommender systems
• Solving business problems with analytics

And that‘s really just the tip of the iceberg. What data science topics and skills would you like to learn about in 2021? Some trends I‘m anticipating are:

• MLOps and productionizing machine learning
• Graph machine learning
• Data science for social good
• Reproducibility and data science tooling

I‘m excited to see how the data science webinar landscape continues to evolve and provide new opportunities for learning. If 2020 is any indication, there will be an explosion of even more webinars to help you stay on the cutting edge of data science.

Remember, becoming a data scientist is a continuous learning journey. Webinars are an invaluable way to learn from experts, keep up with the latest techniques, and uplevel your skills. I‘ll be keeping an eye out for the best webinars in 2021 – maybe I‘ll see you there!