Data Scientist Training

Introduction to AI and Machine Learning in Azure -

Learn to implement AI and ML techniques on Azure.

Introduction to Artificial Intelligence -

Learn the foundational and practical knowledge of artificial intelligence.

Machine Learning and Natural Language Processing -

Learn to implement ML techniques for Natural Language comprehension, sentiment analysis, topic discovery, etc.

Advanced Spark -

Learn how to use Spark internals for working with NoSQL databases as well debugging and troubleshooting.

Scalable Machine Learning -

Learn about and build end-to-end SML pipelines for gaining actionable insights.

AWS Security Fundamentals -

Learn the concepts for establishing security standards in AWS.

Advanced Analytics in Azure -

Learn to build end-to-end data applications using Microsoft Azure and understand which tools are best suited to certain problems and use-cases.

Azure in a Day -

Learn how to build, deploy, and maintain applications in Azure Cloud.

Introduction to AI and Machine Learning in Azure -

Learn to implement AI and ML techniques on Azure.

Advanced SQL -

Learn a deep understanding of the logical query processing aspects of both traditional and the most advanced, modern constructs of the SQL language.

Functional Programming for Java Developers -

Learn to utilize functional programming when creating code.

Introduction to SQL -

Learn the core concepts of SQL.

Advanced Analytics in Azure -

Learn to build end-to-end data applications using Microsoft Azure and understand which tools are best suited to certain problems and use-cases.

Advanced Tableau -

Learn how to improve analytical ability and the ability to provide data insights.

Data Analysis & Visualizations with Excel -

Learn how to use Excel for data analysis to present data into visual insights.

Data Analytics -

Learn how to locate, manipulate, and analyse data with Python, no matter the size of the data set.

Data Architecture Fundamentals -

Learn techniques and tools for data collection, usage, processing, storage, and integration with different systems.

Data Engineering Practices -

Learn the foundational concepts of distributed computing, distributed data processing, data management and data pipelines.

Data Primer -

Learn about the strengths, weaknesses, opportunities and risks surrounding data-based solutions.

Data Storytelling -

Learn how to identify the right context for analysis, perform the analysis and tell a story to drive action.

Data Visualization Tools -

Learn about and compare four different data visualization tools.

Intermediate Tableau -

Learn advanced Tableau knowledge and skills.

Introduction to R & Python -

Learn both R and Python programming languages by providing comparisons and recommendations between both.

Introduction to Tableau -

Learn to be familiar with Tableau.

Working with R -

Learn how to leverage R to perform data analytics.

Advanced Analytics in Azure -

Learn to build end-to-end data applications using Microsoft Azure and understand which tools are best suited to certain problems and use-cases.

AWS Security Fundamentals -

Learn the concepts for establishing security standards in AWS.

Google Cloud for Data Scientists -

Learn how to create and deploy high-performance data science and machine learning systems on Google Cloud for regression and classification use cases leveraging both structured and unstructured datasets.

Intermediate Google Cloud For Data Analysts -

Learn how to analyze large scale, distributed, and real-time datasets with MapReduce and Apache Beam based capabilities of Google Cloud and practice identification and analysis of effective data features for predictive analytics with BigQuery ML and TensorFlow.

Intermediate Google Cloud For Data Scientists -

Learn how to implement statistical and machine learning models using TensorFlow, for example for recommendation engines, and how to improve their performance based on the students’ understanding of underlying mathematical principles.

Introduction to AI and Machine Learning in Azure -

Learn to implement AI and ML techniques on Azure.

Introduction to SageMaker for Data Analysts -

Learn how to choose the right questions to ask and how to answer them with Machine Learning.

Advanced Analytics in Azure -

Learn to build end-to-end data applications using Microsoft Azure and understand which tools are best suited to certain problems and use-cases.

Data Analytics -

Learn how to locate, manipulate, and analyse data with Python, no matter the size of the data set.

Data Architecture Fundamentals -

Learn techniques and tools for data collection, usage, processing, storage, and integration with different systems.

Data Engineering Practices -

Learn the foundational concepts of distributed computing, distributed data processing, data management and data pipelines.

Data Primer -

Learn about the strengths, weaknesses, opportunities and risks surrounding data-based solutions.

Introduction to R & Python -

Learn both R and Python programming languages by providing comparisons and recommendations between both.

Working with R -

Learn how to leverage R to perform data analytics.

Data Analysis & Visualizations with Excel -

Learn how to use Excel for data analysis to present data into visual insights.

Data Storytelling -

Learn how to identify the right context for analysis, perform the analysis and tell a story to drive action.

Data Visualization Tools -

Learn about and compare four different data visualization tools.

Applied Computer Vision -

Learn to understand, design, implement and assess the impact of deep learning techniques for a diverse range of visual recognition tasks.

Deep Learning with TensorFlow and Keras -

Learn Deep Learning concepts and popular tools.

Working with Deep Reinforcement Learning -

Learn about deep reinforcement learning, what it is, how it works and how you can apply it to real-world problems.

JIRA Learning Spike -

Learn the essentials of Atlassian JIRA.

Understanding of Git, Github, Gitlab & Bitbucket -

Learn the details and advanced usage of Git and review the Cloud-based tools and repositories.

Working with JIRA -

Learn the essentials of JIRA.

Understanding of Git, Github, Gitlab & Bitbucket -

Learn the details and advanced usage of Git and review the Cloud-based tools and repositories.

Google Cloud for Data Scientists -

Learn how to create and deploy high-performance data science and machine learning systems on Google Cloud for regression and classification use cases leveraging both structured and unstructured datasets.

Intermediate Google Cloud For Data Analysts -

Learn how to analyze large scale, distributed, and real-time datasets with MapReduce and Apache Beam based capabilities of Google Cloud and practice identification and analysis of effective data features for predictive analytics with BigQuery ML and TensorFlow.

Intermediate Google Cloud For Data Scientists -

Learn how to implement statistical and machine learning models using TensorFlow, for example for recommendation engines, and how to improve their performance based on the students’ understanding of underlying mathematical principles.

Functional Programming for Java Developers -

Learn to utilize functional programming when creating code.

Advanced Jenkins -

Learn how to enable continuous integration in a distributed environment with Jenkins.

JIRA Learning Spike -

Learn the essentials of Atlassian JIRA.

Working with JIRA -

Learn the essentials of JIRA.

Advanced Analytics in Azure -

Learn to build end-to-end data applications using Microsoft Azure and understand which tools are best suited to certain problems and use-cases.

Advanced Machine Learning -

Learn advanced techniques for managing data and tuning models.

Advanced Tableau -

Learn how to improve analytical ability and the ability to provide data insights.

Applied Computer Vision -

Learn to understand, design, implement and assess the impact of deep learning techniques for a diverse range of visual recognition tasks.

Applied Data Science & Machine Learning -

Learn intermediate data analysis techniques and how programming languages can be used to further analyze data.

Building a Recommendation Engine Using Python -

Learn to design, implement and evaluate various recommendation engines.

Data Storytelling -

Learn how to identify the right context for analysis, perform the analysis and tell a story to drive action.

Deep Learning with TensorFlow and Keras -

Learn Deep Learning concepts and popular tools.

Google Cloud for Data Scientists -

Learn how to create and deploy high-performance data science and machine learning systems on Google Cloud for regression and classification use cases leveraging both structured and unstructured datasets.

Intermediate Google Cloud For Data Analysts -

Learn how to analyze large scale, distributed, and real-time datasets with MapReduce and Apache Beam based capabilities of Google Cloud and practice identification and analysis of effective data features for predictive analytics with BigQuery ML and TensorFlow.

Intermediate Google Cloud For Data Scientists -

Learn how to implement statistical and machine learning models using TensorFlow, for example for recommendation engines, and how to improve their performance based on the students’ understanding of underlying mathematical principles.

Intermediate Tableau -

Learn advanced Tableau knowledge and skills.

Introduction to AI and Machine Learning in Azure -

Learn to implement AI and ML techniques on Azure.

Introduction to Artificial Intelligence -

Learn the foundational and practical knowledge of artificial intelligence.

Introduction to Data Science & Machine Learning -

Learn how data can be gathered to improve the overall needs of the business.

Introduction to Machine Learning -

Learn various Machine Learning algorithms to evaluate and productize models.

Introduction to R & Python -

Learn both R and Python programming languages by providing comparisons and recommendations between both.

Introduction to SageMaker for Data Analysts -

Learn how to choose the right questions to ask and how to answer them with Machine Learning.

Introduction to Tableau -

Learn to be familiar with Tableau.

Machine Learning and Natural Language Processing -

Learn to implement ML techniques for Natural Language comprehension, sentiment analysis, topic discovery, etc.

Machine Learning for Search -

Learn the knowledge and use cases for software engineers to transition to Machine Learning for Search.

Python for Data Scientists -

Learn how to use Python to explore and analyze data, run basic regression models, visualize data, and apply some basic machine learning models to data.

PyTorch -

Learn how to implement statistical and machine learning models using PyTorch and how to improve their performance based on an understanding of underlying mathematical principles.

Scalable Machine Learning -

Learn about and build end-to-end SML pipelines for gaining actionable insights.

Working with Deep Reinforcement Learning -

Learn about deep reinforcement learning, what it is, how it works and how you can apply it to real-world problems.

Advanced Spark -

Learn how to use Spark internals for working with NoSQL databases as well debugging and troubleshooting.

Writing Technical White Papers -

Learn how to write persuasive white papers that obtain results.

Advanced Machine Learning -

Learn advanced techniques for managing data and tuning models.

Applied Data Science & Machine Learning -

Learn intermediate data analysis techniques and how programming languages can be used to further analyze data.

Building a Recommendation Engine Using Python -

Learn to design, implement and evaluate various recommendation engines.

Introduction to Data Science & Machine Learning -

Learn how data can be gathered to improve the overall needs of the business.

Introduction to Machine Learning -

Learn various Machine Learning algorithms to evaluate and productize models.

Introduction to R & Python -

Learn both R and Python programming languages by providing comparisons and recommendations between both.

Machine Learning for Search -

Learn the knowledge and use cases for software engineers to transition to Machine Learning for Search.

Python for Data Scientists -

Learn how to use Python to explore and analyze data, run basic regression models, visualize data, and apply some basic machine learning models to data.

PyTorch -

Learn how to implement statistical and machine learning models using PyTorch and how to improve their performance based on an understanding of underlying mathematical principles.

Software Engineering in Python -

Learn software engineering techniques using Python.

Advanced Machine Learning -

Learn advanced techniques for managing data and tuning models.

Applied Data Science & Machine Learning -

Learn intermediate data analysis techniques and how programming languages can be used to further analyze data.

Building a Recommendation Engine Using Python -

Learn to design, implement and evaluate various recommendation engines.

Introduction to Data Science & Machine Learning -

Learn how data can be gathered to improve the overall needs of the business.

Introduction to Machine Learning -

Learn various Machine Learning algorithms to evaluate and productize models.

Machine Learning for Search -

Learn the knowledge and use cases for software engineers to transition to Machine Learning for Search.

Python for Data Scientists -

Learn how to use Python to explore and analyze data, run basic regression models, visualize data, and apply some basic machine learning models to data.

PyTorch -

Learn how to implement statistical and machine learning models using PyTorch and how to improve their performance based on an understanding of underlying mathematical principles.

Introduction to SageMaker for Data Analysts -

Learn how to choose the right questions to ask and how to answer them with Machine Learning.

Domain Models in Practice -

Learn about the technical relationship between code, culture, and architecture and how to model and implement software from a business perspective.

Domain Models in Practice -

Learn about the technical relationship between code, culture, and architecture and how to model and implement software from a business perspective.

Software Engineering in Python -

Learn software engineering techniques using Python.

Machine Learning for Search -

Learn the knowledge and use cases for software engineers to transition to Machine Learning for Search.

Advanced SQL -

Learn a deep understanding of the logical query processing aspects of both traditional and the most advanced, modern constructs of the SQL language.

Introduction to SQL -

Learn the core concepts of SQL.

Advanced Tableau -

Learn how to improve analytical ability and the ability to provide data insights.

Data Storytelling -

Learn how to identify the right context for analysis, perform the analysis and tell a story to drive action.

Intermediate Tableau -

Learn advanced Tableau knowledge and skills.

Introduction to Tableau -

Learn to be familiar with Tableau.

Writing Technical White Papers -

Learn how to write persuasive white papers that obtain results.

Deep Learning with TensorFlow and Keras -

Learn Deep Learning concepts and popular tools.

Software Engineering in Python -

Learn software engineering techniques using Python.