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Onboard
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- Reduce initial time to productivity.
- Increase employee tenure.
- Plug-and-play into HR onboarding and career pathing programs.
- Customize for ad-hoc and cohort-based hiring approaches.
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Upskill
For Tech Teams
- Upgrade and round out developer skills.
- Tailor to tech stack and specific project.
- Help teams, business units, centers of excellence and corporate tech universities.
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Reskill
For Tech Teams
- Offer bootcamps to give employees a running start.
- Create immersive and cadenced learning journeys with guaranteed results.
- Supplement limited in-house L&D resources with all-inclusive programs to meet specific business goals.
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Design
For Tech Teams
- Uplevel your existing tech learning framework.
- Extend HR efforts to provide growth opportunities within the organization.
- Prepare your team for an upcoming tech transformation.
Get your team started on a custom learning journey today!
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Instructor-led Data Scientist Courses
Customized, role-based, expert-led Data Scientist Training
DevelopIntelligence specializes in delivering highly-customized, dedicated, role-based Data Scientist Training courses to technical teams and organizations.
Of course, if you can't find the Data Scientist training course you're looking for, give us a call or contact us and we'll design one just for you and your team.
Data Scientist, you are a statistician or data analyst who lives in San Francisco 😜. In all seriousness, you live for the thrill of extracting insights and a story out of raw data. You love to cut your teeth on the newest libraries, constantly stay up to data by reading white papers, and keep your tools sharp. You blend math, stats, algorithms, scraping, analysis, and visualization tools to help companies understand their businesses (and the world) better.
Onboard
For Tech Teams
- Reduce initial time to productivity.
- Increase employee tenure.
- Plug-and-play into HR onboarding and career pathing programs.
- Customize for ad-hoc and cohort-based hiring approaches.
Upskill
For Tech Teams
- Upgrade and round out developer skills.
- Tailor to tech stack and specific project.
- Help teams, business units, centers of excellence and corporate tech universities.
Reskill
For Tech Teams
- Offer bootcamps to give employees a running start.
- Create immersive and cadenced learning journeys with guaranteed results.
- Supplement limited in-house L&D resources with all-inclusive programs to meet specific business goals.
Design
For Tech Teams
- Uplevel your existing tech learning framework.
- Extend HR efforts to provide growth opportunities within the organization.
- Prepare your team for an upcoming tech transformation.
Get More Information
Chat with one of our tech experts to create a custom on-site or online training program.
Introduction to Artificial Intelligence -
Learn the foundational and practical knowledge of artificial intelligence.
Introduction to Artificial Intelligence and Machine Learning in Azure -
Learn how to implement AI and ML techniques on Azure.
Machine Learning and Natural Language Processing -
Learn to implement ML techniques for Natural Language comprehension, sentiment analysis, topic discovery, etc.
Learn how to use Spark internals for working with NoSQL databases as well debugging and troubleshooting.
Learn about and build end-to-end SML pipelines for gaining actionable insights.
Learn the concepts for establishing security standards in AWS.
Learn to build end-to-end data applications using Microsoft Azure and understand which tools are best suited to certain problems and use-cases.
Learn how to build, deploy, and maintain applications in Azure Cloud.
Introduction to Artificial Intelligence and Machine Learning in Azure -
Learn how to implement AI and ML techniques on Azure.
Learn a deep understanding of the logical query processing aspects of both traditional and the most advanced, modern constructs of the SQL language.
Learn about the strengths, weaknesses, opportunities and risks surrounding data-based solutions.
Learn about Data Warehousing from an AWS perspective examining tools specifically underneath AWS.
Functional Programming for Java Developers -
Learn to utilize functional programming when creating code.
Learn the core concepts of SQL.
Learn to build end-to-end data applications using Microsoft Azure and understand which tools are best suited to certain problems and use-cases.
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.
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.
Learn the foundational concepts of distributed computing, distributed data processing, data management and data pipelines.
Learn about the strengths, weaknesses, opportunities and risks surrounding data-based solutions.
Learn how to identify the right context for analysis, perform the analysis and tell a story to drive action.
Learn about and compare four different data visualization tools.
Learn about Data Warehousing from an AWS perspective examining tools specifically underneath AWS.
Learn advanced Tableau knowledge and skills.
Learn both R and Python programming languages by providing comparisons and recommendations between both.
Learn the fundamentals of Tableau.
Learn the basic concepts of data engineering and how to make the shift to the cloud.
Learn how to use R as a tool to perform data science, machine learning or statistics on large data sets.
Learn to build end-to-end data applications using Microsoft Azure and understand which tools are best suited to certain problems and use-cases.
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 Artificial Intelligence and Machine Learning in Azure -
Learn how to implement AI and ML techniques on Azure.
Communication in the Business Context -
Learn how to communicate effectively with staff, peers, customers, and/or managers.
Effective Communication and Email Etiquette -
Learn how to improve verbal and written communication.
Writing Technical White Papers -
Learn how to write persuasive white papers that obtain results.
Learn to build end-to-end data applications using Microsoft Azure and understand which tools are best suited to certain problems and use-cases.
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.
Learn the foundational concepts of distributed computing, distributed data processing, data management and data pipelines.
Learn about the strengths, weaknesses, opportunities and risks surrounding data-based solutions.
Learn both R and Python programming languages by providing comparisons and recommendations between both.
Learn the basic concepts of data engineering and how to make the shift to the cloud.
Introduction to SageMaker for Data Analysts -
Learn how to choose the right questions to ask and how to answer them with ML.
Learn the knowledge and use cases for software engineers to transition to Machine Learning for Search.
Data Analysis & Visualizations with Excel -
Learn how to use Excel for data analysis to present data into visual insights.
Learn how to identify the right context for analysis, perform the analysis and tell a story to drive action.
Learn about and compare four different data visualization tools.
Learn about the strengths, weaknesses, opportunities and risks surrounding data-based solutions.
Learn about Data Warehousing from an AWS perspective examining tools specifically underneath AWS.
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.
Learn the essentials of Atlassian JIRA.
Understanding Git, Github, Gitlab & Bitbucket -
Learn the details and advanced usage of Git and review the Cloud-based tools and repositories.
Learn the essentials of JIRA.
Understanding Git, Github, Gitlab & Bitbucket -
Learn the details and advanced usage of Git and review the Cloud-based tools and repositories.
Learn about Data Warehousing from an AWS perspective examining tools specifically underneath 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.
Functional Programming for Java Developers -
Learn to utilize functional programming when creating code.
Learn how to enable continuous integration in a distributed environment with Jenkins.
Learn the essentials of Atlassian JIRA.
Learn the essentials of JIRA.
Learn how to help any team thrive in the process of building greater diversity and inclusion.
Learn how to achieve strong results from negotiations, better prepare for multiple potential outcomes, and address the issues of great negotiations that end with gory solution execution.
Leading Teams to Better Decision Making -
Learn how to make strong decisions, design execution of solutions to better solve problems, and maintain strong relationships with stakeholders.
Learn to build end-to-end data applications using Microsoft Azure and understand which tools are best suited to certain problems and use-cases.
Learn advanced techniques for managing data and tuning models.
Learn how to improve analytical ability and the ability to provide data insights.
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.
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.
Learn advanced Tableau knowledge and skills.
Introduction to Artificial Intelligence -
Learn the foundational and practical knowledge of artificial intelligence.
Introduction to Artificial Intelligence and Machine Learning in Azure -
Learn how to implement AI and ML techniques on Azure.
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.
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 ML.
Learn the fundamentals of Tableau.
Machine Learning and Natural Language Processing -
Learn to implement ML techniques for Natural Language comprehension, sentiment analysis, topic discovery, etc.
Learn the knowledge and use cases for software engineers to transition to Machine Learning for Search.
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.
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.
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.
Learn how to use Spark internals for working with NoSQL databases as well debugging and troubleshooting.
Learn how to help any team thrive in the process of building greater diversity and inclusion.
Communication in the Business Context -
Learn how to communicate effectively with staff, peers, customers, and/or managers.
Learn how to develop a smooth change process within a team.
Learn about different decision styles and how they contribute to team decision making.
Learn how to make better decisions as a team.
Learn how to achieve strong results from negotiations, better prepare for multiple potential outcomes, and address the issues of great negotiations that end with gory solution execution.
Effective Communication and Email Etiquette -
Learn how to improve verbal and written communication.
Learn the proven, simple, easy to implement tools and strategies to ensure that ethics and integrity guide actions.
Leading Teams to Better Decision Making -
Learn how to make strong decisions, design execution of solutions to better solve problems, and maintain strong relationships with stakeholders.
Learn how to assess initiation and employ planning tools and techniques to create high functioning teams that start projects in the best form to help ensure success.
Project Management Success Principles -
Learn how to develop comprehensive project plans including scope, schedule, and budget.
Learn the easy to implement tools and strategies to improve teamwork.
Time Management and Prioritization -
Learn the easy to implement tools and strategies to improve time management.
Writing Technical White Papers -
Learn how to write persuasive white papers that obtain results.
Learn how to develop a smooth change process within a team.
Learn about different decision styles and how they contribute to team decision making.
Learn how to make better decisions as a team.
Learn the proven, simple, easy to implement tools and strategies to ensure that ethics and integrity guide actions.
Learn how to assess initiation and employ planning tools and techniques to create high functioning teams that start projects in the best form to help ensure success.
Project Management Success Principles -
Learn how to develop comprehensive project plans including scope, schedule, and budget.
Learn the easy to implement tools and strategies to improve teamwork.
Time Management and Prioritization -
Learn the easy to implement tools and strategies to improve time management.
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.
Learn both R and Python programming languages by providing comparisons and recommendations between both.
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.
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.
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.
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.
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.
Learn how to use R as a tool to perform data science, machine learning or statistics on large data sets.
Learn the knowledge and use cases for software engineers to transition to Machine Learning for Search.
Learn about the technical relationship between code, culture, and architecture and how to model and implement software from a business perspective.
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.
Learn a deep understanding of the logical query processing aspects of both traditional and the most advanced, modern constructs of the SQL language.
Learn the core concepts of SQL.
Learn how to improve analytical ability and the ability to provide data insights.
Learn how to identify the right context for analysis, perform the analysis and tell a story to drive action.
Learn advanced Tableau knowledge and skills.
Learn the fundamentals of Tableau.
Software Engineering in Python -
Learn software engineering techniques using Python.
Customized Technical Learning Solutions to Help Attract and Retain Talented Developers
Talk to one of our Learning Solution Architects today
Let DI help you design solutions to onboard, upskill or reskill your software development organization. Fully customized. 100% guaranteed.
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