Skip to main content

80 Courses

Time Series Analysis with the KNIME Analytics Platform
Tutorials
View Course

Tutorials

Time Series Analysis with the KNIME Analytics Platform

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.

Introduction to C# Language Integrated Query (LINQ)
Tutorials
View Course

Tutorials

Introduction to C# Language Integrated Query (LINQ)

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.

The T Shaped Data Scientist - How going deep and broad can be your secret to success
Tutorials
View Course

Tutorials

The T Shaped Data Scientist - How going deep and broad can be your secret to success

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.

Building a Personalised Attrition Recommendation System
Tutorials
View Course

Tutorials

Building a Personalised Attrition Recommendation System

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.

Crash Course on Naive Bayes Classification
Tutorials
View Course

Tutorials

Crash Course on Naive Bayes Classification

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.

High-Performing Data Scientist: leveraging the non-technical skills
Tutorials
View Course

Tutorials

High-Performing Data Scientist: leveraging the non-technical skills

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.

Building Responsible AI: best practices across the product development lifecycle
Tutorials
View Course

Tutorials

Building Responsible AI: best practices across the product development lifecycle

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. 

The Behavioral Edge: level up your data skills with behavioral science
Tutorials
View Course

Tutorials

The Behavioral Edge: level up your data skills with behavioral science

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. 

Job Hunting for Data Analysts
Tutorials
View Course

Tutorials

Job Hunting for Data Analysts

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.

Crash Course in Modern Data Warehousing Using Snowflake Platform
Tutorials
View Course

Tutorials

Crash Course in Modern Data Warehousing Using Snowflake Platform

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.

MLOps Crash Course for Beginners
Tutorials
View Course

Tutorials

MLOps Crash Course for Beginners

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. 

Finding the Tallest Tree: comparing tree-based models
Tutorials
View Course

Tutorials

Finding the Tallest Tree: comparing tree-based models

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. 

Translating Data into Effective Decisions
Tutorials
View Course

Tutorials

Translating Data into Effective Decisions

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. 

Ace the Data Science Interview with Nick Singh
Tutorials
View Course

Tutorials

Ace the Data Science Interview with Nick Singh

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.

AI & ML Workflows with Kubernetes
Tutorials
View Course

Tutorials

AI & ML Workflows with Kubernetes

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.

Redis Crash Course for Artificial Intelligence & Machine Learning
Tutorials
View Course

Tutorials

Redis Crash Course for Artificial Intelligence & Machine Learning

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. 

Web Scraping with Python and BeautifulSoup
Tutorials
View Course

Tutorials

Web Scraping with Python and BeautifulSoup

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.


Python Crash Course for Excel Users
Tutorials
View Course

Tutorials

Python Crash Course for Excel Users

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.


SQL Crash Course for Beginners
Tutorials
View Course

Tutorials

SQL Crash Course for Beginners

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.


Getting High-Quality Data for Your Computer Vision Models
Tutorials
View Course

Tutorials

Getting High-Quality Data for Your Computer Vision Models

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.