Data Science Training

This Advanced Analytics for Structured Data using AWS 2 day course provides a technical introduction to the understanding, creation and digital data supply chains for advanced analytics with AWS.

Data Science for Marketing Analytics training course covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments.The course starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you’ll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation.

This Applied Data Science and Big Data Analytics intensive training course provides theoretical and technical aspects of Data Science and Business Analytics.  The course covers the fundamental and advanced concepts and methods of deriving business insights from big” and/or “small” data.  This training course is supplemented by hands-on labs that help attendees reinforce their theoretical knowledge of the learned material.

Technology and the data that it both collects and makes accessible is now interwoven with businesses and lives. The era of "big data" has exploded due to the rise of cloud computing, which provides an abundance of computational power and storage, allowing organizations of all sorts to capture and store data. Leveraging that data effectively can provide timely insights and competitive advantage.Analyzing data to find issues, insights and opportunities, is now a critical part of many job roles. Beyond the analysis, data analysts in all job roles must be able to effectively present and communicate their findings in visually compelling ways.

Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and features of R that enable you to understand your data better and get answers to your most pressing business questions. 

This course begins with the most important and commonly used method for unsupervised learning - clustering - and explains the three main clustering algorithms - k-means, divisive, and agglomerative. Following this, you’ll study market basket analysis, kernel density estimation, principal component analysis, and anomaly detection. By working through interesting activities, you’ll explore data encoders and latent variable models.

The ability to identify and respond to changing trends is a hallmark of a successful business. Whether those trends are related to customers and sales, or to regulatory and industry standards, businesses are wise to keep track of the variables that can affect the bottom line. In today's business landscape, data comes from numerous sources and in diverse forms. By leveraging data science concepts and technologies, businesses can mold all of that raw data into information that facilitates decisions to improveand expand the success of the business.

This Data Science for Business Professionals training course is also designed to assist learners in preparing for the CertNexus® DSBIZ™ (Exam DSZ-110) credential.

Public Courses coming soon to Stockholm, Copenhagen and Oslo - Register Your Interest here

This Data Science with Python training course provides theoretical and practical aspects of using Python in the realm of Data Science, Business Analytics, and Data Logistics. The coverage of the related core concepts, terminology, and theory is provided as well. This intensive training course is supplemented by a variety of hands-on labs (the list of which is provided at the bottom of this outline) that help attendees reinforce their theoretical knowledge of the learned material.

Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning.
This intensive R Programming Introduction training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice.

This Advanced Analytics for Structured Data using AWS 2 day course provides a technical introduction to the understanding, creation and digital data supply chains for advanced analytics with AWS.

This Snowflake training course provides a comprehensive introduction to Snowflake, a cloud-based data platform designed for data warehousing, analytics, and data sharing. This Snowflake course covers the core features, architecture, and functionalities of Snowflake, including its unique multi-cluster architecture and its advantages over traditional databases and cloud data warehouses.

By the end of this one-day beginner-level course, participants will understand how to use Snowflake for managing data, optimizing query performance, and leveraging advanced features for data sharing and analytics.

The ability to identify and respond to changing trends is a hallmark of a successful business. Whether those trends are related to customers and sales, or to regulatory and industry standards, businesses are wise to keep track of the variables that can affect the bottom line. In today's business landscape, data comes from numerous sources and in diverse forms. By leveraging data science concepts and technologies, businesses can mold all of that raw data into information that facilitates decisions to improveand expand the success of the business.

This Data Science for Business Professionals training course is also designed to assist learners in preparing for the CertNexus® DSBIZ™ (Exam DSZ-110) credential.

Data Science for Marketing Analytics training course covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments.The course starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you’ll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation.

Technology and the data that it both collects and makes accessible is now interwoven with businesses and lives. The era of "big data" has exploded due to the rise of cloud computing, which provides an abundance of computational power and storage, allowing organizations of all sorts to capture and store data. Leveraging that data effectively can provide timely insights and competitive advantage.Analyzing data to find issues, insights and opportunities, is now a critical part of many job roles. Beyond the analysis, data analysts in all job roles must be able to effectively present and communicate their findings in visually compelling ways.

Data Science Projects with Python training course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. This Data Science with Python course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You’ll discover how to tune the algorithms to provide the best predictions on new and, unseen data.

This Data Science for Solution Architects training course helps Solution Architects and other IT practitioners understand the value proposition, methodology and techniques of the emerging discipline of Data Science.  The class also introduces the students to a number of existing production-ready technologies and capabilities that enable enterprises to build cost-efficient Big Data processing solutions.

This Python Data Science training course takes a detailed look at the most popular Python libraries for numeric processing, statistical analysis, machine learning, and visualization. We also show how to make use of common Python data types and algorithms to achieve real-world tasks.

Python has emerged as a popular and effective language in the world of data science. The dynamic nature of the language, the relative simplicity of the syntax, and the abundance of fast and powerful libraries have all been important contributory factors in this growth.

Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning.
This intensive R Programming Introduction training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice.

The ability to identify and respond to changing trends is a hallmark of a successful business. Whether those trends are related to customers and sales, or to regulatory and industry standards, businesses are wise to keep track of the variables that can affect the bottom line. In today's business landscape, data comes from numerous sources and in diverse forms. By leveraging data science concepts and technologies, businesses can mold all of that raw data into information that facilitates decisions to improveand expand the success of the business.

This Data Science for Business Professionals training course is also designed to assist learners in preparing for the CertNexus® DSBIZ™ (Exam DSZ-110) credential.

Kafkas focus on performance and persistence enables companies to rethink their IT architecture. This introduces new challenges for architects and operators.

In this instructor-led course you will not only experience what makes Kafka such a successful streaming platform but also how to identify challenges and obstacles and how to resolve them as easily as possible.

This Applied Data Science and Big Data Analytics intensive training course provides theoretical and technical aspects of Data Science and Business Analytics.  The course covers the fundamental and advanced concepts and methods of deriving business insights from big” and/or “small” data.  This training course is supplemented by hands-on labs that help attendees reinforce their theoretical knowledge of the learned material.

This two-day Apache Kafka for Operators training course is for administrators and DevOps-engineers who want to set up and operate Kafka successfully in production. In addition to the most important aspects of operation, participants will also deepen their expertise on how to monitor Kafka and how to avoid catastrophic situations.

In this two-day Apache Kafka for Developers training course you will experience and learn Kafka in a way that enables you to use it successfully in your company directly afterwards the training. Not only will you gain necessary theoretical knowledge but also practical experience through numerous exercises.

This Advanced Analytics for Structured Data using AWS 2 day course provides a technical introduction to the understanding, creation and digital data supply chains for advanced analytics with AWS.

This Snowflake training course provides a comprehensive introduction to Snowflake, a cloud-based data platform designed for data warehousing, analytics, and data sharing. This Snowflake course covers the core features, architecture, and functionalities of Snowflake, including its unique multi-cluster architecture and its advantages over traditional databases and cloud data warehouses.

By the end of this one-day beginner-level course, participants will understand how to use Snowflake for managing data, optimizing query performance, and leveraging advanced features for data sharing and analytics.

Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and features of R that enable you to understand your data better and get answers to your most pressing business questions. 

This course begins with the most important and commonly used method for unsupervised learning - clustering - and explains the three main clustering algorithms - k-means, divisive, and agglomerative. Following this, you’ll study market basket analysis, kernel density estimation, principal component analysis, and anomaly detection. By working through interesting activities, you’ll explore data encoders and latent variable models.

Public Courses coming soon to Stockholm, Copenhagen and Oslo - Register Your Interest here

This Data Science with Python training course provides theoretical and practical aspects of using Python in the realm of Data Science, Business Analytics, and Data Logistics. The coverage of the related core concepts, terminology, and theory is provided as well. This intensive training course is supplemented by a variety of hands-on labs (the list of which is provided at the bottom of this outline) that help attendees reinforce their theoretical knowledge of the learned material.

In this two-day Apache Kafka for Developers training course you will experience and learn Kafka in a way that enables you to use it successfully in your company directly afterwards the training. Not only will you gain necessary theoretical knowledge but also practical experience through numerous exercises.

This Advanced Analytics for Structured Data using AWS 2 day course provides a technical introduction to the understanding, creation and digital data supply chains for advanced analytics with AWS.

This Python Data Science training course takes a detailed look at the most popular Python libraries for numeric processing, statistical analysis, machine learning, and visualization. We also show how to make use of common Python data types and algorithms to achieve real-world tasks.

Python has emerged as a popular and effective language in the world of data science. The dynamic nature of the language, the relative simplicity of the syntax, and the abundance of fast and powerful libraries have all been important contributory factors in this growth.

Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and features of R that enable you to understand your data better and get answers to your most pressing business questions. 

This course begins with the most important and commonly used method for unsupervised learning - clustering - and explains the three main clustering algorithms - k-means, divisive, and agglomerative. Following this, you’ll study market basket analysis, kernel density estimation, principal component analysis, and anomaly detection. By working through interesting activities, you’ll explore data encoders and latent variable models.

Public Courses coming soon to Stockholm, Copenhagen and Oslo - Register Your Interest here

This Data Science with Python training course provides theoretical and practical aspects of using Python in the realm of Data Science, Business Analytics, and Data Logistics. The coverage of the related core concepts, terminology, and theory is provided as well. This intensive training course is supplemented by a variety of hands-on labs (the list of which is provided at the bottom of this outline) that help attendees reinforce their theoretical knowledge of the learned material.

This two-day Apache Kafka for Operators training course is for administrators and DevOps-engineers who want to set up and operate Kafka successfully in production. In addition to the most important aspects of operation, participants will also deepen their expertise on how to monitor Kafka and how to avoid catastrophic situations.

Data Science Projects with Python training course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. This Data Science with Python course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You’ll discover how to tune the algorithms to provide the best predictions on new and, unseen data.

Data Science Projects with Python training course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. This Data Science with Python course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You’ll discover how to tune the algorithms to provide the best predictions on new and, unseen data.

This Python Data Science training course takes a detailed look at the most popular Python libraries for numeric processing, statistical analysis, machine learning, and visualization. We also show how to make use of common Python data types and algorithms to achieve real-world tasks.

Python has emerged as a popular and effective language in the world of data science. The dynamic nature of the language, the relative simplicity of the syntax, and the abundance of fast and powerful libraries have all been important contributory factors in this growth.

Public Courses coming soon to Stockholm, Copenhagen and Oslo - Register Your Interest here

This Data Science with Python training course provides theoretical and practical aspects of using Python in the realm of Data Science, Business Analytics, and Data Logistics. The coverage of the related core concepts, terminology, and theory is provided as well. This intensive training course is supplemented by a variety of hands-on labs (the list of which is provided at the bottom of this outline) that help attendees reinforce their theoretical knowledge of the learned material.