Emerging Tech - Big Data Trunk

https://project.bigdatatrunk.com Quality Corporate and Classroom Training in Bay Area CA Tue, 28 Oct 2025 12:18:15 +0000 en-US hourly 1 https://wordpress.org/?v=7.0 Develop Mobile App using Android https://project.bigdatatrunk.com/courses/develop-mobile-app-using-android/ https://project.bigdatatrunk.com/courses/develop-mobile-app-using-android/#respond Tue, 26 Nov 2024 13:32:48 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=54803 This comprehensive training introduces participants to the fundamentals of Android app development and extends their knowledge to build more functional and interactive applications.

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Overview
Prerequisites
Audience
Curriculum

Description:

This comprehensive training introduces participants to the fundamentals of Android app development and extends their knowledge to build more functional and interactive applications. Over two days, attendees will learn how to use Android Studio, design user interfaces, integrate app components, and handle data storage. The training includes a mix of theory and hands-on sessions, culminating in the creation of a complete Android application. By the end, participants will have a solid foundation in Android development, ready to take their app ideas to the next level.

Duration: 1 Day

Course Code: BDT42

Learning Objectives:

By the end of this training, participants will be able to:

Set up the Android development environment using Android Studio.
Design user interfaces using XML layouts.
Implement basic app functionality with Java/Kotlin.
Connect app components using activities and intents.
Test and deploy an Android app on an emulator or device.

Basic programming knowledge in Java or Kotlin is recommended. Familiarity with object-oriented programming concepts is helpful.

Aspiring mobile app developers exploring Android development.
IT professionals seeking to expand their skills into mobile development.
Students and hobbyists interested in creating Android applications.

Course Outline:

Day 1: Fundamentals of Android Development

Module 1: Introduction to Android Development

Overview of the Android Platform and Ecosystem
Setting Up Android Studio and Emulator
Structure of an Android Application

Module 2: User Interface Design

Designing Layouts with XML (LinearLayout, RelativeLayout, ConstraintLayout)
Working with Views and Widgets (Buttons, Text Fields, Images)
Hands-On: Creating a User Interface for a Basic App

Module 3: App Logic and Core Components

Introduction to Activities and Intents
Event Handling and Interactivity
Hands-On: Adding Navigation and Functionality to the App

Day 2: Advanced Features and App Deployment

Module 4: Data Management in Android

Introduction to Local Storage (Shared Preferences, SQLite)
Working with Data Binding and RecyclerView
Hands-On: Implementing Data Storage in the App

Module 5: Enhancing App Features

Introduction to Fragments and Multi-Screen Apps
Integrating Basic Animations and Themes
Hands-On: Adding Advanced Features to the App

Module 6: Testing, Debugging, and Deployment

Debugging Tools in Android Studio
Testing Apps on Emulators and Physical Devices
Generating and Deploying APK Files

Module 7: Wrap-Up and Best Practices

Best Practices for Android Development
Tips for Continued Learning and Growth
Q&A and Feedback

Training material provided: Yes (Digital format)

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]]> https://project.bigdatatrunk.com/courses/develop-mobile-app-using-android/feed/ 0 DevOps Toolkit: Git, Docker, Kubernetes, and CI/CD https://project.bigdatatrunk.com/courses/devops-toolkit-git-docker-kubernetes-and-ci-cd/ https://project.bigdatatrunk.com/courses/devops-toolkit-git-docker-kubernetes-and-ci-cd/#respond Sat, 30 Oct 2021 09:57:47 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=22897 In this course you will understand the fundamentals of DevOps and the patterns to develop, build and deploy applications.

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Overview
Prerequisite
Audience
Curriculum

Description:

Unlock the World of DevOps: Master the Fundamentals and Patterns for Application Development, Building, and Deployment. Dive into Virtual Machines, Containers, Git, CI/CD, Microservice Architecture, and More with Hands-On Labs in this Comprehensive Course

Course Code/Duration:

BDT170 / 3 Days

Learning Objectives:

After this course, you will be able to:

Understand the steps involved in DevOps methodology
Learn DevOps tools like Git, Docker, Jenkins, and Kubernetes
Maintain versions and source code using Git
Use Docker in your daily developer and/or sysadmin roles
Lead your team into the future with the latest Docker container skills!
Deploy apps to Kubernetes
Make Kubernetes YAML manifests and deploy using infrastructure-as-code method
Understand how to build microservices with Docker and Kubernetes
Design continuous integration pipelines using Jenkins

Basic Understanding of Linux/Unix and some basic programming with Python, Java, JavaScript, C#, etc. is assumed.

This course is designed for anyone interested to get started with the domain of DevOps including Data Engineers, DevOps Engineer, Release Engineer, Database Administrator, Infrastructure Engineers, Software Engineers, or System Administrator.

Course Outline:

Understand principal concepts and practices in DevOps methodology.

DevOps Overview and Tools

Principles of continuous software development, integration, and deployment
DevOps Goals
Automation
Version Control
Testing
Deployment
Cloud
Introduction to Tools (e.g., Git, Docker, Jenkins)
Repositories, artifacts

Version control using Git

Importance and need of version control
Version control options
Git Overview
Understand Git commands
Exercise: Managing Source Code and Versions with Git

Docker

Docker Overview
Docker Desktop
Docker Commands
Understand Dockerfile
Building Docker Containers
How to use Docker, Compose and Kubernetes on your machine for better software building and testing.
Hand’s-on with best practices for making Dockerfiles and Compose files

Kubernetes

Introduction to Kubernetes
Role: container/ service orchestration
Architecture, clusters
Automated deployment and operations of containerized applications
Kubernetes networking
CNI
Networks policies, workflow
Examples: Flannel, Calico
Automation based on helm charts (optional)
Container/service image (registry and distribution)
Introduction to service meshes
Fundamentals: definition, drivers and use cases
Example: Envoy and Istio

Continuous Integration/Continuous Deployment (CI/CD)

Introduction to CI/CD concepts
Continuous Integration Pipelines
Creating Pipelines
Automating Deployments
Exercise: Creating CI/CD Pipelines with Jenkins

Use the practices on Cloud – GCP

References and Next steps

Training material provided:

Hands-on Lab: Instructions will be provided to install Docker, & Kubernetes on personal machine prior to the start of class.

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]]> https://project.bigdatatrunk.com/courses/devops-toolkit-git-docker-kubernetes-and-ci-cd/feed/ 0 Byte-Sized Agile Series: Agile Techniques for Managing Risk https://project.bigdatatrunk.com/courses/byte-sized-agile-series-agile-techniques-for-managing-risk/ https://project.bigdatatrunk.com/courses/byte-sized-agile-series-agile-techniques-for-managing-risk/#respond Mon, 26 Jul 2021 11:21:39 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=22593 There are many built-in risk mitigation techniques as part of the agile frameworks.

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Overview
Prerequisite
Audience
curriculum

Description:

Enhance Agile Risk Management: Explore Additional Techniques for Improved Project Success. Discover Strategies Beyond Built-in Frameworks to Mitigate Risks and Ensure Successful Delivery.

Course Code/Duration:

BDT158 / 90 minutes

Learning Objectives:

Learn how to mitigate risk involved in your deliveries

Defining risk
How do the agile frameworks manage risk?

Techniques:

Success Sliders
Risk Factor
Prioritizing Risk
Risk Adjusted Backlog

Let’s practice:

Define Success
Identify Risks
Generate the Risk Factor
Adjust your Backlog
Summary Recap
References and next steps

Learners should have a basic knowledge of programming.

Project managers, Product Owners, Scrum Masters, Team members, Product manager Anyone dealing with, or interested in, mitigating risk through agile delivery

Course Outline:

Learn how to mitigate risk involved in your deliveries

Defining risk
How do the agile frameworks manage risk?

Techniques:

Success Sliders
Risk Factor
Prioritizing Risk
Risk Adjusted Backlog

Let’s practice:

Define Success
Identify Risks
Generate the Risk Factor
Adjust your Backlog
Summary Recap
References and next steps

Training material provided: Yes (Digital format)

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]]> https://project.bigdatatrunk.com/courses/byte-sized-agile-series-agile-techniques-for-managing-risk/feed/ 0 Byte-Sized Agile Series: Estimating and Forecasting https://project.bigdatatrunk.com/courses/byte-sized-agile-series-estimating-and-forecasting/ https://project.bigdatatrunk.com/courses/byte-sized-agile-series-estimating-and-forecasting/#respond Mon, 26 Jul 2021 11:09:57 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=22588 Estimating is hard, and we are often wrong. Let's review different techniques for estimating and learn how to avoid some common bad practices that can render forecasts useless.

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Overview
Prerequisite
Audience
Curriculum

Description:

Master the Art of Estimation: Discover Effective Techniques and Learn How to Avoid Pitfalls in Forecasting. Enhance Your Accuracy and Avoid Common Estimation Mistakes.

Course Code/Duration:

BDT157 / 90 minutes

Learning Objectives:

Gain a working knowledge of how to apply User Story maps and improve the quality of your Product Backlogs

What’s wrong with estimating with time?
What’s a Story Point?
Who should estimate and why?
Common mistakes

Tools:

Planning Poker
Affinity Mapping
Cycle Time

Let’s practice:

Exercise with Planning Poker
Exercise with Affinity Mapping
How do we know how long something will take to complete?
Calculating Velocity
Charting Velocity to derive a date
Summary Recap
References and next steps

Training material provided: Yes (Digital format)

Learners should have a basic knowledge of programming.

Agile team members
Project Managers
Anyone interested in how Story Points work and how to use them to forecast.

Course Outline:

Exercise with Planning Poker
Exercise with Affinity Mapping
How do we know how long something will take to complete?
Calculating Velocity
Charting Velocity to derive a date
Summary Recap
References and next steps. (ADD THIS IN CURRICULUM AND REMOVE FROM OVERVIEW PAGE )

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]]> https://project.bigdatatrunk.com/courses/byte-sized-agile-series-estimating-and-forecasting/feed/ 0 Byte-Sized Agile Series: User Story Mapping https://project.bigdatatrunk.com/courses/byte-sized-agile-series-user-story-mapping/ https://project.bigdatatrunk.com/courses/byte-sized-agile-series-user-story-mapping/#respond Mon, 26 Jul 2021 11:04:53 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=22585 The flat product backlog is great way to indicate build order but doesn't give you the holistic view of what you're building.

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Overview
Prerequisite
Audience
Curriculum

Description:

The flat product backlog is great way to indicate build order but doesn’t give you the holistic view of what you’re building. Story Mapping is a visual way to represent the bigger picture which helps you identify gaps in value.

Course Code/Duration:

BDT156 / 90 minutes

Learning Objectives:

Gain a working knowledge of how to apply User Story maps and improve the quality of your Product Backlogs

What is a User Story Map?
When should you use it?
How to do it

Let’s practice:

Defining the steps/activities
Fleshing out the details
Identify now versus later
Walk the map to validate it
Summary Recap
References and next steps

Training material provided: Yes (Digital format)

Learners should have a basic knowledge of programming.

Anyone on an agile team who is involved in defining User Stories.

Course Outline:

Introduction
Challenges of a flat product backlog.
Overview of Story Mapping for a holistic view.
User Story Map Basics
Defining User Story Map components.
Examples showcasing its effectiveness.
When to Use User Story Mapping
Scenarios benefiting from User Story Mapping.
Real-world case studies.
Creating a User Story Map
Step-by-step guide.
Hands-on exercises for practice.
Improving Backlogs
Enhancing backlog quality with User Story Maps.
Integration into Agile workflows

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]]> https://project.bigdatatrunk.com/courses/byte-sized-agile-series-user-story-mapping/feed/ 0 Byte-Sized Agile Series: Writing Great User Stories https://project.bigdatatrunk.com/courses/byte-sized-agile-series-writing-great-user-stories/ https://project.bigdatatrunk.com/courses/byte-sized-agile-series-writing-great-user-stories/#respond Mon, 26 Jul 2021 10:51:04 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=22583 User Stories are an essential tool in creating great requirements.

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Overview
Prerequisite
Audience
Curriculum

Description:

User Stories are an essential tool in creating great requirements.

Course Code/Duration:

BDT155 / 90 minutes

Learning Objectives:

Improve your understanding of user stories

It’s about the user!
User Story Format
Acceptance Criteria
Techniques:
Tell a story
Specification By Example (SBE)
Gherkin
SPIDR
Let’s practice:
Split an Epic
Critical review of stories
Summary Recap
References and next steps

Learners should have a basic knowledge of programming.

Product Owners, Scrum Masters, Team members, Product managers. Anyone involved in writing user stories

Course Outline:

Defining the steps/activities
Fleshing out the details
Identify now versus later
Walk the map to validate it
Summary Recap
References and next steps

Training Material Provided: Yes (Digital Format)

The post Byte-Sized Agile Series: Writing Great User Stories first appeared on Big Data Trunk.

]]> https://project.bigdatatrunk.com/courses/byte-sized-agile-series-writing-great-user-stories/feed/ 0 Byte-Sized Deep Learning Series: Applied Deep Learning for Natural Language Understanding https://project.bigdatatrunk.com/courses/byte-sized-deep-learning-series-applied-deep-learning-for-natural-language-understanding/ https://project.bigdatatrunk.com/courses/byte-sized-deep-learning-series-applied-deep-learning-for-natural-language-understanding/#respond Mon, 21 Jun 2021 07:48:35 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=22548 This 90-minute session will explore the application of deep learning models known as transformers to solve common natural language understanding tasks.

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Overview
Prerequisite
Audience
Curriculum

Description:

This 90-minute session will explore the application of deep learning models known as transformers to solve common natural language understanding tasks (e.g., question answering, sentiment classification, text summarization, text generation). Learners will use the popular Hugging Face library.

Course Code/Duration:

BDT148 / 90 minutes

Learners should have a basic knowledge of Python programming.

This course is for those who would like to understand how to apply deep learning to common natural language understanding tasks.

Course Outline:

During this course, you will have the opportunity to:

Understand the transformer model and why it is superior to previous approaches to natural language understanding.
Learn how to use the popular Hugging Face library to solve common use cases.
Apply pre-trained models to answer questions related to a corpus of text, summarize text, generate novel text, and several other use cases including paraphrasing, sentiment classification, and text completion.

Training material provided: Yes (Digital format)

The post Byte-Sized Deep Learning Series: Applied Deep Learning for Natural Language Understanding first appeared on Big Data Trunk.

]]> https://project.bigdatatrunk.com/courses/byte-sized-deep-learning-series-applied-deep-learning-for-natural-language-understanding/feed/ 0 Byte-Sized Deep Learning Series: Understanding Language https://project.bigdatatrunk.com/courses/byte-sized-deep-learning-series-understanding-language/ https://project.bigdatatrunk.com/courses/byte-sized-deep-learning-series-understanding-language/#respond Mon, 21 Jun 2021 07:44:58 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=22546 This 90-minute session will explore the use of deep learning and the role of recurrent neural networks in language understanding.

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Overview
Prerequisite
Audience
Curriculum

Session Description:

Course Code/Duration:

BDT151 / 90 minutes

Learning Objectives:

During this course, you will have the opportunity to:

Understand the transformer model and why it is superior to previous approaches to natural language understanding.
Learn how to use the popular Hugging Face library to solve common use cases.
Apply pre-trained models to answer questions related to a corpus of text, summarize text, generate novel text, and several other use cases including paraphrasing, sentiment classification, and text completion.

Training material provided: Yes (Digital format)

Learners should have a basic knowledge of Python programming.

This course is for those who would like to understand how to apply deep learning to common natural language understanding tasks.

The post Byte-Sized Deep Learning Series: Understanding Language first appeared on Big Data Trunk.

]]> https://project.bigdatatrunk.com/courses/byte-sized-deep-learning-series-understanding-language/feed/ 0 Byte-Sized Deep Learning Series: Image Recognition https://project.bigdatatrunk.com/courses/byte-sized-deep-learning-series-image-recognition/ https://project.bigdatatrunk.com/courses/byte-sized-deep-learning-series-image-recognition/#respond Mon, 21 Jun 2021 07:41:28 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=22544 This course is for those who are interested in gaining an understanding of how deep learning is used to recognize and classify images

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Overview
Prerequisite
Audience
Curriculum

Description:

This 90-minute session will explore the use of deep learning and the role of convolution neural networks in computer vision and image classification. Learners will use TensorFlow to build and train deep learning models to classify images.

Course Code/Duration:

BDT149 / 90 minutes

Learners should have a basic knowledge of Python programming.

This course is for those who are interested in gaining an understanding of how deep learning is used to recognize and classify images (e.g., medical imaging, face recognition, image classification).

Course Outline:

During this course, you will have the opportunity to:

Explore deep learning and the advantages of deep learning models.
Understand convolutional neural networks and how they learn to recognize images.
Use TensorFlow to build and train a deep neural network to classify images.

Training Material Provided: Yes (Digital Format)

The post Byte-Sized Deep Learning Series: Image Recognition first appeared on Big Data Trunk.

]]> https://project.bigdatatrunk.com/courses/byte-sized-deep-learning-series-image-recognition/feed/ 0 Byte-Sized Deep Learning Series: Introducing Neural Networks https://project.bigdatatrunk.com/courses/byte-sized-deep-learning-series-introducing-neural-networks/ https://project.bigdatatrunk.com/courses/byte-sized-deep-learning-series-introducing-neural-networks/#respond Mon, 21 Jun 2021 07:18:53 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=22543 This course is for those who would like to understand how to apply deep learning to common natural language understanding tasks.

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Overview
Prerequisites
Audience
Curriculum

Session Description:

Dive into Neural Networks and Deep Learning in 90 Minutes! Discover how they work, and harness the power of TensorFlow to build and train your own neural network for data-driven insights.

Course Code/Duration:

BDT148 / 90 minutes

Learning Objectives:

During this course, you will have the opportunity to:

Explore machine learning, the uniqueness of neural networks,and how data enables machines to learn.
Understand how deep learning is different from traditional machine learning.
Understand how neural networks learn and their role in deep learning.
Apply your learning by using TensorFlow to build and train a neural network to learn from data.

Learners should have a basic knowledge of Python programming.

This course is for those who are interested in gaining an understanding of machine learning and neural networks, foundational to the use of deep learning.

Course Outline:

Exploring Machine Learning and Neural Networks
Understanding the Difference between Deep Learning and Traditional Machine Learning
Learning about Neural Networks and Their Role in Deep Learning
Hands-on Lab: Building and Training a Neural Network with TensorFlow

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