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The key differentiator for high-performance data scientists is not technical depth, but the ability to act as a translator between the business and the technology practice. In this talk, Jeremy Adamson will share five tips for how practitioners can move their careers to the next level by leveraging relationships, reconsidering their role as data scientists, and controlling the data narrative in their organizations.

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Everyone seems to be talking about responsible AI these days—but what does “responsible” actually mean, and how should AI/ML product teams incorporate ethics into the development lifecycle? This talk will focus on the organizational processes that support the development of responsible AI systems. 

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Data engineering is the unsung hero of the analytics revolution. While machine learning algorithms get all the spotlight, the quality of data can make or break an analytics project. Missing data or entry errors are just the tip of the iceberg: Thoughtfully creating new variables that are tailor-made for the business problem at hand will pay long-lasting dividends in terms of model accuracy and effectiveness. 

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In this session, Oscar Baruffa will demystify the hiring process, highlight why job hunting is so difficult, focus on where he thinks most candidates can make the biggest difference, give examples of what improvements to make, and briefly describe a strategy he uses to maintain his energy and good emotional state when looking for work.

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This webinar is focused to make you capable of getting started with the new generation data warehouse i.e. Snowflake. You will be understanding Snowflake architecture, its user interface, and the data caching feature of Snowflake. During the webinar, there will be a lot of instructor-led demos to provide you with a pragmatic experience regarding the Snowflake Platform.

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Data scientists at the start of their careers often have the misconception that their job will be largely focused on training and improving models in a Jupyter notebook. The reality is that most data science value is created outside of a notebook. 
This crash course is intended for data scientists with basic knowledge of developing machine learning models in a Jupyter notebook setting. 

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Tree-based models such as decision trees, random forests, and boosted trees provide powerful predictions and are fast to compute. There are many different ways to fit these models in R, including the rpart, randomForest, and xgboost packages. 

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In this talk Daniel will present a systematic process where ML is an input to improve our ability to make better decisions, thereby taking us closer to the prescriptive ideal.  In a nutshell, this process starts by clearly identifying the KPI or metric that we want to improve (eg. revenue).  This metric itself may not be actionable, so we may have to decompose it into actionable metrics. 

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Want to ace your upcoming Data Science job interview? Join Nick Singh, author of the best-selling book, Ace the Data Science Interview, to learn how to solve SQL, probability, ML, coding, and case interview questions asked by FAANG + Wall Street. He'll also share the contrarian job hunting tips that led him to work at Facebook, Google, and an ML startup.

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In this session, we'll explore how to incorporate data science and AI/ML into Kubernetes development workflows, taking advantage of the platform's openness and rich ecosystem. Using well-known open-source tools for Data Science such as Jupyter Notebooks and TensorFlow, we will explore different strategies to accelerate and automate ML workloads with Kubernetes.

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As data scientists and machine learning professionals make the transition from theory to applied data science, they naturally expand their skill set beyond Python or Pandas.  They need to understand how to leverage a key-value store or database to store their embeddings or features, and how to load and fetch them very quickly for online predictions or to perform complex operations in milliseconds for real-time use cases. 

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The number of websites on the internet is estimated to be around 2 billion. Web scraping turns the entire world wide web into your data set. In this webinar, we will introduce how to scrape a website using the BeautifulSoup package in Python. We will discuss how to navigate the HTML DOM to find data that interests you, some best practices, the legality of web scraping, and briefly touch on how to build and automate a web scraper on the cloud using Azure Functions.

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If you’re an Excel user looking to level up your analytics skills, Python is a great choice for repeatable processes, compelling visualizations, and robust data analysis. And while learning to code may seem too difficult, your Excel knowledge gives you a significant head start.

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This crash course is intended for beginners with no prior experience in SQL. By the end of the session, you will know what a database is and the difference between SQL and NoSQL, what an RDBMS is & the difference between MySQL, Oracle, PostgreSQL, SQL Server, and SQLite, how to see what’s in a database by looking at a data model, how to find data in a database by writing a SQL query, and how to use SQL along with Python, R, Excel, etc.

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In this talk, we will discuss the practice of collecting and annotating data for your computer vision models and making sure the dataset you are using is representative and free of harmful biases.

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In this talk, we'll introduce discrete choice models of agent behaviors with a focus on consumer demand modeling. We will talk about two different ways of modeling consumer heterogeneity (discrete vs. continuous), as well as how this individual-level model (i.e. varying parameters at the individual level) can be estimated by using simulated individuals when you only have aggregate sales data. 

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All the other STEM arts have well-established methodologies. Is there such a set of methodologies for data science and machine learning? YES! Join Thom and Ghaith as they walk you through a high-level coverage of these methods.

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The Online Data Science Dojo platform is carefully designed to cater the needs of aspiring data scientists who are only beginning to learn about developing and deploy predictive models on the cloud applications. In the video, we will walk you through creating an account, uploading data, and importing the data.

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PyTorch Lightning reduces the engineering boilerplate and resources required to implement state-of-the-art AI. Organizing PyTorch code with Lightning enables seamless training on multiple GPUs, TPUs, CPUs as well as the use of difficult to implement best practices such as model sharding, 16-bit precision, and more, without any code changes.

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we'll show you how to build an accurate sales forecast using Prophet and Python. The Prophet package is built by Facebook and makes it ridiculously simple to get up and running with Time Series forecasting.