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In this talk, Jimmy will be telling the story of his journey to, then through Data Science, starting from his finance/accounting days (Symantec, 2013), all the way until the present day (2022, Sr. Data Scientist at LinkedIn). He will touch on highs, lows, moments of triumph and setbacks, and working full-time/doing a Masters part-time for 7 years. He hopes to share with the audience some wisdom that he has gained on this journey and leave them with some words of encouragement (that persistence and a good attitude are some of the best tools you can have in any pursuit of life).

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The process we follow when working with data is just as important as the tools we're using. In this presentation, Ben Jones of Data Literacy will give you an overview of a new tool-agnostic framework that powers his company's recently launched training program, Data Literacy Level 2: Working Effectively with Data, and the accompanying book "Read, Write, Think Data" (coming soon)! By the end of the webinar, you'll be equipped with an understanding of The WISDOM Data-Working Flow, which you can use to pose suitable questions about your data, form hypotheses that can be tested, and convert raw data into the wisdom you need to make sound decisions at work and in life. This new framework was developed by Ben Jones, CEO of Data Literacy, award-winning data visualization designer, best-selling data author, and Professor of Data Visualization at the University of Washington. This new framework is applicable to all experience levels and useful to anyone who interacts with data and must use data to make decisions.

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Recent deep learning architectures have brought us solutions to previously unsolved problems. Nevertheless, for most tools, you still need to overcome the coding barrier. In the first part of this webinar, you will learn about the main concepts behind deep learning: from the artificial neuron to back-propagation. In the second part, you will get a basic introduction to Convolutional Neural Networks and their application in Computer Vision.

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This talk will introduce the foundational business skills you'll need to deliver business value and grow your career as an analyst or data scientist. Drawing on best practices, published research, case studies, and personal anecdotes from two decades of industry experience, David Stephenson will give an overview of foundational skills related to Company, Colleagues, Storytelling, Expectations, Results, and Careers--emphasizing how each topic relates to your unique position as an analytics professional within a larger corporation. 

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One of the most common data science questions is what language beginners should learn, R or Python. This has led to a rivalry between the two languages, termed the "Language War". The purpose of this talk is to announce that this rivalry is over, and we are entering a new era. We'll go through the main defining features of both languages (influenced by their history) and how they compare between different workflows in data science (i.e., data visualization, machine learning) and data types (i.e., text, image, or time series). As a final element, I'll show what methods are available for combining both in the same workspace and demonstrate this with a case study. At the end of the talk, you'll be able to appreciate why being bilingual is essential for a modern data scientist and what are the best ways to get started.

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Skin cancer is a serious problem worldwide but luckily treatment in the early stage can lead to recovery. JavaScript together with a machine learning model can help Medical Doctors increase the accuracy of melanoma detection. During the presentation, Karol will show how to use Tensorflow.js, Keras, and React Native to build a solution that can recognize skin moles and detect if they are melanoma or benign mole. He will also show issues that they have faced during development. In summary, the session includes the pros and cons of JavaScript used for machine learning projects.

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In this session, you’ll learn about the main concepts behind Time Series: preprocessing, alignment, missing value imputation, forecasting, and evaluation. Together we will build a demand prediction application: first with (S)ARIMA models and then with machine learning models. The codeless examples are built in the KNIME Analytics Platform using the Time Series components provided for preprocessing, transforming, aggregating, forecasting, and inspecting time series data. You will also be provided example workflows to use later in your own projects.

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Language-Integrated Query (LINQ) is a feature of the C# programming language that lets you work with data with SQL-like syntax. This presentation starts with the basic syntax of a LINQ query. You'll also learn about core features like filters, joins, and grouping. Additionally, you'll see how LINQ has a comprehensive set of standard operators for whatever you need to do with data. After this presentation, you'll understand how LINQ can help with data manipulation tasks.

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Many people believe that you have to either be a specialist or a generalist. An expert or a jack-or-jill of all trades. But those who can do both, who can balance these seemingly contradictory beliefs, consistently outperform. Matt Coatney, a C-level technology executive, and AI practitioner talk about his own journey and missteps, and how a multi-disciplinary approach including deep technical expertise and core skills led him to personal and professional success.

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This webinar will cover an end-to-end study of attrition – the dwindling number of the workforce – in a company. The end goal of an attrition recommendation system is to develop retention strategies to prevent employee churn. In this session, we will discuss the process starting from data collection & preprocessing to model development and deployment. It will be presented by an award-winning HR consultant with over 17+ years of consulting in HR transformation and system implementation projects across the globe.

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Naive Bayes is a technique from machine learning, useful for making classifications. Naive Bayes has all sorts of applications ranging from facial recognition to weather prediction to medical diagnoses to news classifications among others. In this webinar, we provide an introduction to Naive Bayes methods through theory and coding examples. By the end of the webinar, students should acquire a strong understanding of this technique.

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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.