Demystifying Data Science: Predictive Modeling
What is Predictive Modeling? Why is it useful? How do you leverage Predictive Modeling in a practical sense for your business?
Users will end this session with a better idea of how they might be able to apply predictive modeling to their own potential use cases.
In this webinar, we explore, an introduction to Predictive Modeling, a practical help as to how business and analytics users can best leverage predictive modeling tools, ways to challenge and test predictive models to ensure confidence in performance, and best practice examples of how to deploy the results from predictive.
Training - Machine Learning Techniques For CAE Applications Using Altair Knowledge Works
Machine Learning Techniques For CAE Applications Using Altair Knowledge Works
Retaining High Value For Your Business Using Data Analytics
A profound shift in the world economy means that businesses will rely more on analytics to predict a behavior down to a particular confidence interval. Loss of revenue streams and mitigating other risks highlights the importance of using data to drive decisions around strategies that are intended to maintain revenue, reduce costs, and navigate through other challenges.
Using Altair Data Analytics To Track Down Fraudulent Activities
Altair Data Analytics helps the Intelligence Community implement and streamline advanced interoperability through smart data preparation and predictive analytics, allowing security analysts to rapidly access, clean, and transform difficult data from disparate sources into a leverageable, shared asset for efficient reporting.
During the 20-minute session presented at the recently-held Intelligence Analytics Virtual Summit, we discussed how to leverage data preparation and predictive analytics to automate repetitive, error-prone processes associated with accessing and reconciling data, eliminating manual work, and fostering smart decision-making across the board.
Data Preparation, Data Science and Machine Learning
Future of Data Analytics for Financial Services using Altair Knowledge Works. The sessions aim to upgrade and improve your skills in key aspects of Data Analytics – Data Preparation, Machine Learning and Visualization.
Stream Processing and Visualization
Future of Data Analytics for Financial Services using Altair Knowledge Works. The sessions aim to upgrade and improve your skills in key aspects of Data Analytics – Data Preparation, Machine Learning and Visualization. Webinars,Knowledge Hub,Knowledge Studio,Data Analytics,Financial Services,Corporate,Knowledge Works
Predictive Analytics and Machine Learning with Knowledge Studio
Accurately predicting future consumer behavior allows credit risk analysts, financial marketing analysts, and fraud detection teams to better deploy strategies to capitalize on opportunities while deploying strategies that act as preventative measures against disruptive forces to their business models.
Altair Predictive Analytics and Machine Learning
Altair Knowledge Works: Driving down costs using Predictive Maintenance in a Manufacturing Environment Reliant on Robotic Arms
Altair Knowledge Studio R Language Integration
Knowledge Studio predictive analytics software enables users to efficiently find insight from large amounts of disparate data sources and removing complexity often encountered when creating sophisticated statistical models.
Cross Selling to Increase Loyalty, Revenue in Telecom
In a crowded market, a telecom provider's growth generally comes from consumers moving from one provider to another. It’s a zero-sum game: when one provider wins a customer, another will lose one.
Current Use of Python in Data Science: Challenges
Altair Knowledge Studio is the best solution that addresses Python coding challenges. Data scientists can rely on Altair to efficiently build powerful and insightful predictive models to make better business decisions.
An Analytical Approach to Financial Crimes Rules Creation and Optimization
Recent studies from leading financial institutions show that financial fraud continues to rise at alarming rates. According to Experian, credit and debit card fraud has risen over 60% in the first half of 2019. Although Millennials are highly targeted (an 80% chance of being a target of fraud), no one is immune from criminal activity.
Home grown fraud rules, based on in-house subject matter expertise and observations, are commonly used to counter fraud attacks as well as other financial crimes, such as Anti-Money Laundering.
Altair offers an industry leading approach to augment/adjust these rules based on actual analytical models. The benefit realized is that fraud analysts can update rules without needing to turn to highly utilized resources such as data scientists.
Creating Value Using Machine Learning
Altair released the Knowledge Works platform which is used for data preparation, data prediction and data visualization. In this webinar an introduction to the Machine Learning capabilities of Knowledge Works is presented. The webinar contains:
A brief introduction to the concept of Machine Learning
A live demo solving a problem using Predictive Maintenance
Methods for transforming the Machine Learning algorithms to actual profit
AI & The Future of Product Lifecycles
This presentation discusses the changes in product lifecycles in the last decade or so, introduces artificial intelligence and machine learning, and how it fits with simulation & engineering. Simulation and machine learning product lifecycles, and current trends will be discussed, along with Altair's vision of simulation with machine learning.
Using Machine Learning and Optimization to Develop e-Motor
The Altair Multiphysics platform provides a broad portfolio of solvers and tools to help engineers develop e-motor design requirements by using simulation and optimization methods. This presentation provides examples, using Altair Machine Learning and optimization solutions, of the e-motor requirements by leveraging in data available, which is key for e-motor designers to reduce time-to-market.
Altair Knowledge Works Product Offerings Datasheet
Knowledge Works supports deep data analysis that leads to quick results. Fast, agile analytics allows people across the entire organization to wrangle and with data and then share their findings, enabling everyone to make informed, data-driven decisions.
Insight derived from clean, consistent data leads to better decisions. Knowledge Works allows people of all skill levels to access data on demand and quickly make smart decisions that drive your business forward.
Professional Services Datasheet
Altair Professional Services offer affordable, high-return options for our customers who are striving to foster better data intelligence throughout the enterprise. The Altair Professional Services team is fully staffed with experienced data scientists and analytics experts to help you select, deploy and manage solutions that fit optimally into your organization. Whether you are looking for light mentoring services, or full outsourcing, our team can assist you – we want to be your trusted advisor. We want our customers to be trained and enabled to get the most out of Altair solutions. Therefore, we offer a broad set of training options in flexible formats to match any learning style.
Machine Learning Applications in Engineering
Machine Learning Applications in Engineering
Dr. Shidan Murphy, Director APAC Solutions Specialists,
Data Intelligence, Altair
Altair Knowledge Works Full Platform Brochure
Knowledge Works data intelligence solutions allow individuals and organizations to incorporate more data, unite more minds with agility, and engender more trust in analytics and data science. The platform enables users to quickly and accurately capture and prepare data for any project, use data to accurately predict outcomes and produce insight and foresight, and visualize trends and insights that helps to decipher and communicate across the business.
Altair Knowledge Studio Datasheet
Knowledge Studio is an open, flexible predictive analytics and machine learning platform designed for data scientists and business analysts alike.
Altair Knowledge Hub for Data Scientists
The tight integration between Knowledge Studio and Knowledge Hub streamlines data preparation to be used in the production and enterprise-wide adoption of valuable, predictive models. Governance and lineage protocols are followed. Data science groups can easily find curated and trusted data sets to build their predictive models from.
Altair Knowledge Studio for Predictive Maintenance and Machine Learning
Altair predictive analytics and machine learning platform helps manufacturers perform preventative or corrective actions using insight found in data generated directly from their equipment. Data science teams can deliver optimized maintenance routines that will minimize unexpected downtime and add efficiencies to regular operations, without the need to manually create sophisticated algorithms from scratch, even when experience in advanced analytics programming is limited.
Altair Knowledge Studio for Apache Spark
Knowledge Studio for Apache Spark is unique because it allows users scale up, scale-wide and scale-down. It not only leverage’s Apache Spark’s ability to operate on datasets with very large numbers of records, it is also capable of generating improved SparkSQL queries on datasets that have thousands of columns. Further, Knowledge Studio for Apache Spark provides the ability to scale down by avoiding the overhead costs of parallelization when datasets are very small.
Altair Knowledge Studio Python Integration
The Python Code Node allows a user to bring data from a Python routine into Knowledge Studio for further modeling. Users can also use this node to natively code a Python script. All data manipulation functions and data mining algorithms available in Python can be used within Knowledge Studio. This eliminates the need to use other tools to leverage Python as part of an existing Knowledge Studio model, or for new predictive models.
Transforming Design & Decision Making by Applying Simulation Throughout Product Lifecycles
Seen here presenting at the UK Altair Technology Conference 2019, James R. Scapa brings more than 35 years of engineering experience to his dual role of Chairman and CEO of Altair Engineering, Inc., a title he has held since the company’s inception. In 1985, Mr. Scapa and two partners founded a small consulting activity in the new field of computer-aided-engineering. Today, the company employs over 2,000 employees with more than 82 offices throughout 25 countries.
Through Mr. Scapa’s leadership, the company is now a leading global provider of simulation technology and engineering services that empower client innovation and decision-making. With over 5,000 clients, Altair serves the automotive, aerospace, government and defense, heavy equipment industry sectors as well as the consumer products, shipbuilding, energy, electronics, life sciences, and architecture engineering and construction markets. Prior to establishing Altair, Scapa served as an engineering consultant to the automotive industry, beginning his career with Ford Motor Company in 1978. Scapa holds a Bachelor’s degree in Mechanical Engineering from Columbia University and a Master of Business Administration from the University of Michigan.
Altair Knowledge Studio Decision Trees
Predictive Analytics is commonly defined as leveraging technology that learns from data analysis to predict the future behavior of people or processes to drive better decision making. To effectively deploy large scale operations that involve multiple product offerings and distribution channels, predictive analytics places a probability against the outcome.
Altair Knowledge Studio Strategy Trees
Decision Trees from Knowledge Studio provides the ability to segment, profile, identify and apply business treatments to the variables used in the modelling.
Using Scorecards to Minimize Risk and Increase Revenue Opportunities
Scorecards are used by credit lenders to determine the creditworthiness of borrowers, and by marketers to better understand consumer interest in product or service offerings. To help make decisions about an applicant, data from 3rd-party providers that includes delinquency scores, failure scores, and payment ratings; demographic attributes; and the history of current account activity is often used to create a model that predicts the probability of a loan default.