Misc. and Webinars - Big Data Trunk

https://project.bigdatatrunk.com Quality Corporate and Classroom Training in Bay Area CA Sun, 16 Feb 2025 14:51:30 +0000 en-US hourly 1 https://wordpress.org/?v=7.0 Data Science with Hadoop and Spark https://project.bigdatatrunk.com/courses/data-science-with-hadoop-and-spark/ https://project.bigdatatrunk.com/courses/data-science-with-hadoop-and-spark/#respond Sun, 20 Dec 2020 23:23:21 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=21792 This course will teach participants how to use Apache Hadoop and Apache Spark to solve sophisticated data science problems, producing valuable insights in a wide range of scenarios.

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Overview
Prerequisites
Audience
Curriculum

Description:

This course will teach participants how to use Apache Hadoop and Apache Spark to solve sophisticated data science problems, producing valuable insights in a wide range of scenarios.

Day one focuses on data science basics, including data acquisition, scrubbing and manipulation, as well as a general overview of data science applications as well as the analytics and machine learning processes typically employed. A number of practical use cases are examined during class and lab sessions.

Day two focuses on Apache Hadoop and its ecosystem along with the types of data science applications typically handled by the Hadoop platform. The course outlines the statistical methods used to produce actionable business insights with MapReduce, Python, Hive and other tools.

Day three begins with an overview of the Apache Spark platform and its machine learning library, MLlib.

Participants will learn how to perform entity ranking, implement recommendation engines and perform other common data science tasks using Spark batch, streaming, graph and machine learning capabilities.

Course Code/Duration:

BDT62 / 3 Days

Learning Objectives:

In this course, participants will:

Have a clear understanding of data science, its typical use cases and how data science is performed using a range of tools in the Apache open source ecosystem.

Python Programming Basics. Each participant will require the ability to run a 64 bit virtual machine (provided with the course).

This course is designed for Application developers, analysts and data scientists.

Course Outline:

Day 1

Data Science
- Data Science Process Overview
- Structured and Unstructured Data
- Data Acquisition and Transformation
- Data Analysis and Machine Learning
- Machine Learning Concepts

Day 2

Big Data overview
- A brief history of Big Data
- History and background of Big Data and Hadoop
- 5 V’s of Big Data
- Secret Sauce of Big Data Hadoop
- Big Data Distributions in Industry
- End-to-End Big Data Life cycle overview
- Demos and Labs
Big Data Ecosystem before Spark
- Big Data Ecosystem before Apache Spark
- Storage options – HDFS and No-SQL
- Processing options – MapReduce, Hive etc.
- Administrative tools – Zookeeper, Ozzie etc.
- Ingestion tools – Sqoop, Flume
- Demos and Labs

Day 3

Getting Started with Apache Spark
- Introduction to Spark RDD
- Spark RDD Transformation and Actions
- Spark Lifecycle
- Spark Caching
- Setup Account on Apache Spark Databricks Cloud
- Databricks Notebooks overview
- Lab – Spark RDD Transformation & Actions
- Lab – Spark RDD Advanced Transformation & Actions
- Demos and Labs
Apache Spark SQL, DataFrames, Datasets
- Introduction to Spark SQL
- SQL, DataFrames and Datasets Spark Library
- Compare the various APIs – RDD, DataFrames and Datasets
- Demos and Labs
Machine Learning using Apache Spark
- Introduction to Machine Learning and Data Science
- Machine Learning Spark Library
- Spark Machine Learning examples
- Demos and Labs
- Streaming using Apache Spark
- Need of real time processing
- Streaming Spark Library
- Spark Streaming examples
- Demos and Labs

Training material provided:

Yes (Digital format)

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]]> https://project.bigdatatrunk.com/courses/data-science-with-hadoop-and-spark/feed/ 0 AR and VR Introduction Workshop https://project.bigdatatrunk.com/courses/ar-and-vr-introduction-workshop/ https://project.bigdatatrunk.com/courses/ar-and-vr-introduction-workshop/#respond Sun, 20 Dec 2020 23:17:32 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=21790 This beginner-friendly workshop is an introduction to Augmented and Virtual Reality technologies forthose interested in learning more about the industry, and the general AR and VR creation process.

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Overview
Prerequisites
Audience
Curriculum

Description:

Embark on a beginner-friendly journey into the world of Augmented and Virtual Reality (AR and VR) with our 'AR and VR Introduction Workshop.' Designed for those eager to explore this dynamic industry, the workshop offers an overview of AR and VR technologies, the hardware involved, and prominent non-gaming applications and companies.

Discover the application development pipeline and get hands-on experience with the Unity 3D engine, enabling you to create your own XR prototypes and games. This workshop is your gateway to understanding and participating in the exciting AR and VR landscape."

Course Code/Duration:

BDT65 / 1 Day

Learning Objectives:

After this course, you will have the opportunity to:

Define Augmented and Virtual Reality.
Describe how AR and VR are being used for non-gaming and entertainment.
List the leading AR and VR hardware and describe why they’re popular.
Discuss the application development pipeline for AR and VR
Demonstrate the ability to navigate through the Unity Editor Interface and prototype environments.
Use Unity to build a virtual world complete with the ability to move around the scene.
Identify the Unity packages needed to create AR and VR experiences.

Basic computer knowledge
Computer running windows or macOS
AR compatible phone (iOS / Android)
(Optional) VR Headset (Oculus Quest)
(Optional) AR Headset (Hololens1, HoloLens 2, or Magic Leap)

This workshop is designed for:

Anyone interested in learning more about AR and VR technologies
Software Engineers interested in learning about the XR space
UX/UI Designers
Creative Directors
Executives interested in breaking into the space, and want to get up to speed with industry vocabulary

Course Outline:

Introduction to Augmented and Virtual Reality
- What is AR?
- What is VR?
- History of the Technology
Demo: First AR + VR Experiences
- (If available) Demo an app on Oculus Quest
- Demo an app on iOS / Android
- (If available) Demo an app on AR headset
Challenge: Thoughts on the first experiences?
- Which technology did you prefer?
- Given your experience, describe a few use cases that could leverage AR? VR?
XR Pipeline: How to create a VR and AR applications (1/2)
- Technologies used to create VR / AR
- VR / AR Development Workflow
Intro to Unity Workshop (¼) – Intro to the Editor
- Demo
- Challenge: Setup Unity
How are AR and VR being used in enterprise?/li>
- Major use cases
- Key companies
- Industry Trends
Intro to Unity Workshop (2/4) – Creating Virtual Worlds
- Demo
- Challenge: Create your own virtual world using Unity’s Asset Store
Intro to Unity Workshop (3/4) – Locomotion
- Demo
- Challenge: Add the ability to move around the scene
XR Pipeline: How to create a VR and AR applications (2/2)
- AR / VR Design Best Practices
- AR / VR Unity Packages
Intro to Unity Workshop (4/4) – Get started with Unity for AR and VR
- Demo
Wrap Up:
- Next Steps
- Additional Resources

Training material provided:

Yes (Digital format)

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]]> https://project.bigdatatrunk.com/courses/ar-and-vr-introduction-workshop/feed/ 0 Survey Analytics Reporting https://project.bigdatatrunk.com/courses/survey-analytics-reporting/ https://project.bigdatatrunk.com/courses/survey-analytics-reporting/#respond Mon, 21 Dec 2020 07:02:58 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=21786 This course is designed to help understand best practices for survey results analysis and reporting – dealing with missing values, making estimates, combining data from different sources, and selecting the right reporting method to share insights gained through surveys.

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Overview
Prerequisites
Audience
Curriculum

Description:

This course is designed to help understand best practices for survey results analysis and reporting – dealing with missing values, making estimates, combining data from different sources, and selecting the right reporting method to share insights gained through surveys.

Long Description:

"Enhance your survey analysis and reporting skills with our 'Survey Analytics Reporting' course. Delve into the best practices for handling missing values, making estimates, and blending data from various sources to present valuable insights gathered from surveys. By the course's conclusion, you'll be well-versed in utilizing established statistical methods to transform survey responses into actionable marketing decision support. Explore techniques such as factor analytics, cluster analysis, discriminant analysis, and multi-dimensional scaling. Elevate your survey analysis and reporting capabilities and unlock the potential of data-driven marketing."

Course Code/Duration:

BDT60 / 1 Day

Learning Objectives:

After this course, you will be able to:

Assess and improve data quality.
Effectively handle missing data in your analyses.
Understand and implement cluster sampling techniques.
Visualize survey data for better insights.
Apply basic statistical concepts, including sample mean and covariance, to your data analysis tasks.

Basic knowledge of Microsoft PowerPoint and Microsoft Excel.

Business User

Course Outline:

Building a strong hypothesis

Why hypothesis?
Visioning the final stories in the beginning

Framework of Survey Results Analysis

What a successful data collection looks like?
Questions to ask before analyzing
Quantifying potential error (metrics to test collected data)
Describing the quality of data sources
Predicting common pitfalls

Quantitative Research

Statistical models for data-driven decisions
Predicting “most-likely” outcomes

Sharing insights

The 6 Ws
Static vs. Interactive
How to present results from open-ended questions
How to present results from close-ended questions
When to sharing raw data
Sharing next steps and closing the loop

Best practices library

Statistical relevance – Survey data
Survey data visualization

Training material provided:

Yes (Digital format)

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]]> https://project.bigdatatrunk.com/courses/survey-analytics-reporting/feed/ 0 Survey Design: Best Practices for Beginners https://project.bigdatatrunk.com/courses/survey-design-best-practices-for-beginners/ https://project.bigdatatrunk.com/courses/survey-design-best-practices-for-beginners/#respond Mon, 21 Dec 2020 07:00:14 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=21783 When surveying users and customers, it is important to ask the right questions to the right people at the right time and to construct the survey so that meaningful results can be obtained, analyzed, and reported. In this class you will learn how to design an effective survey and bring actionable insights to your organization.

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Overview
Prerequisites
Audience
Curriculum

Description:

When surveying users and customers, it is important to ask the right questions to the right people at the right time and to construct the survey so that meaningful results can be obtained, analyzed, and reported. In this class you will learn how to design an effective survey and bring actionable insights to your organization.

Long Description:

Unlock the art of effective survey design in our comprehensive training program. Whether you're seeking customer feedback, conducting employee assessments, or organizing an event, asking the right questions to the right people at the right time is essential. This course delves into the fundamentals of survey science, providing valuable insights into constructing surveys for meaningful results. Learn how to design surveys that yield actionable insights for your organization. Explore best practices and online survey tips to ensure the success of your next survey project. Join us to master the science of surveying and bring valuable insights to your business or projects.

Course Code/Duration:

BDT44 / 1 Day

Learning Objectives:

After this course, you will be able to:

Why survey?
How to strategically plan your survey
Understand the best practices for survey design
Review collection techniques to get the best response rate
How to analyze, present and share the findings
Determine which online tool is best for your survey

Basic computer operations.

Beginner/Intermediate online survey creators. Businesses professionals who would like to leverage survey usage for their organization.

Course Outline:

Planning

Formulating the problem or question to be answered
Determining the research approach
Setting objectives for information collection

Design

Choosing a collection method (Qualtrics, Google Forms, or SurveyMonkey)
Tips for good visual design
Types of questions/response alternatives
Pretesting and pilot testing
Pitfalls to avoid
Good question test – is it clear, easy answerable, unbiased?

Collection

Collecting responses
Statistical significance

Qualitative vs. Quantifiable

Improving response rates and retention

Analysis & Reporting

Filtering and customizing results
Sharing results

Training material provided:

Yes (Digital format)

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]]> https://project.bigdatatrunk.com/courses/survey-design-best-practices-for-beginners/feed/ 0 Data Modeling: Basics https://project.bigdatatrunk.com/courses/data-modeling-basics/ https://project.bigdatatrunk.com/courses/data-modeling-basics/#respond Sun, 20 Dec 2020 22:55:40 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=21781 The Data Modeling : Basics course will provide basic information about organizing data per the system/ group / enterprise wide. Thus, enable and unleash the capability to capture and store any kind of data and provide analytical feasibility.

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Overview
Prerequisites
Audience
Curriculum

Description:

The Data Modeling : Basics course will provide basic information about organizing data per the system/ group / enterprise wide. Thus, enable and unleash the capability to capture and store any kind of data and provide analytical feasibility.

Long Description:

"Unlock the Power of Data Modeling: Basics Training! Dive into the world of data modeling, where you'll learn to comprehend and document data essential for operational and analytics processes. This comprehensive course guides you through the creation and validation of precise 'data models' while engaging with business and IT stakeholders. Discover the pivotal role of data models in applications development, encompassing forward and reverse engineering, as well as integration projects like business intelligence and data lakes. Our course offers a deep dive into data modeling techniques, covering conceptual, logical, and physical relational and dimensional, as well as NoSQL data models. Learn the art of capturing and modeling requirements, and apply best practices for building and validating data models with the Data Model Scorecard®. By the end, you'll not only know how to build a data model but how to do it exceptionally well. With real-world case studies and hands-on exercises, you'll be ready to apply these skills to your current projects. Enroll now for a data modeling journey that enhances your understanding and expertise."

Course Code/Duration:

BDT61 / 2 Days

Learning Objectives:

After this course, you will be able to:

Component Usage: Learn when and how to use different data modeling components.
Data Requirement Elicitation: Apply techniques for gathering data requirements.
Data Model Creation: Build relational and dimensional data models at various levels.
Model Quality Assessment: Evaluate data model quality.
Enhanced Models: Incorporate support and extensibility features.

This course assumes no prior data modeling knowledge and, therefore, there are no prerequisites. This course is designed for anyone with one or more of these terms in their job title: “data”, “analyst”, “architect”, “developer”, “database”, and “modeler”.

Data Modelers, Database administrators, ETL developers, Business Analysts, DWH/BI professionals, Data Architects.

Course Outline:

Day 1

Course Introduction
Modeling Basics
Overview to the Data Model Scorecard®
Ensuring the model captures the requirements
Validating model scope
Understanding conceptual, logical, and physical data models
Solve a real-time business problem in the class and simulate the real time working environment by yourself

Day 2

Following acceptable modeling principles
Determining the optimal use of generic concepts
Applying consistent naming standards
Arranging the model for maximum understanding
Writing clear, complete, and correct definitions
Fitting the model within an enterprise architecture
Comparing the metadata with the data
References and Next steps

Structured Activity/Exercises/Case Studies:

Day 1

Exercise 1 – Create random ER Model for a case study
Exercise 2 – Create random Dimensional Model for same case study
Exercise 3 – No SQL modeling for a case study
Exercise 4 – End to End modeling involving Conceptual, Logical and Physical modeling

Day 2

Exercise 5 – Best practice and formatting standards for the data model
Exercise 6 – Enterprise level use case and build an analytical model

Training material provided:

Yes (Digital format)

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]]> https://project.bigdatatrunk.com/courses/data-modeling-basics/feed/ 0 Explore The World Of Data Science (Lecture) https://project.bigdatatrunk.com/courses/explore-the-world-of-data-science-lecture/ https://project.bigdatatrunk.com/courses/explore-the-world-of-data-science-lecture/#respond Sun, 20 Dec 2020 22:51:03 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=21778 This lecture will give a broad overview of Data Science. We’ll clarify the relationship between Data Science and Machine Learning and explore the Data Science process.

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Overview
Prerequisites
Audience
Curriculum

Description:

This lecture will give a broad overview of Data Science. We’ll clarify the relationship between Data Science and Machine Learning and explore the Data Science process.

Long Description:

Unlock the essentials of data analysis with our comprehensive course. Learn to craft actionable data analysis questions and acquire the right data to answer them. Dive into the critical aspects of data exploration, emphasizing the significance of clean, complete, and diverse data. Explore the effective application and evaluation of Machine Learning models. Through practical demonstrations using Pandas and Scikit-learn, you'll gain valuable insights into working on Data Science projects. This course not only equips you with the skills needed for data analysis but also highlights the crucial decision points that impact a project's success. Join us to master the art of data-driven decision-making and steer your projects towards success.

Course Code/Duration:

BDT40 / 1 Day

Learning Objectives:

After this course, you will be able to:

Understand the relationship between Data Science and Machine Learning
Become familiar with the Data Science process
Identify effective data analysis questions that are actionable
Identify effective data sources
Understand the importance of clean, complete, and quantity of data
Understand how Machine Learning is applied and evaluated within the Data Science process
Become familiar with some of the tools used throughout the process

Basic programming knowledge will be helpful but not required

Developers, Business Analysts who want to start a career in or wants to learn about the exciting domain of Data Science and Machine Learning, Non-technical professionals who want to start a career in Machine Learning

Course Outline:

Introduction to lecture
Data Science vs. Machine Learning
The Data Science process
The importance of data
Exploring and transforming data
Creating and evaluating Machine Learning models
Developing an effective Data Science strategy
Demonstration of the Data Science process using pandas and scikit-learn
Next Steps

Structured Activity/Exercises/Case Studies:

Demonstration – the Data Science process using pandas and scikit-learn

Training material provided:

Yes (Digital format)

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]]> https://project.bigdatatrunk.com/courses/explore-the-world-of-data-science-lecture/feed/ 0 Introduction To Artificial Intelligence (Lecture) https://project.bigdatatrunk.com/courses/introduction-to-artificial-intelligence/ https://project.bigdatatrunk.com/courses/introduction-to-artificial-intelligence/#respond Sun, 20 Dec 2020 22:34:38 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=21772 Artificial intelligence (AI) is the ability of a computer program or a machine to think and learn.It is also a field of study which tries to make computers “smart”. … As machines become increasingly capable.

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Overview
Prerequisite
Audience
Curriculum

Description:

Artificial intelligence (AI) is the ability of a computer program or a machine to think and learn.It is also a field of study which tries to make computers “smart”. … As machines become increasingly capable.

Long Description:

Cultivate a deeper understanding of Artificial Intelligence (AI) with this engaging lecture. Our non-intimidating introduction unravels the distinctions between AI and Machine Learning, delving into the technology's core, including neural networks and Deep Learning. Explore the intricacies of how machines learn and grasp the paramount role of data. Gain insights into crafting AI projects through a guided demonstration. Discover the awe-inspiring capabilities of AI and learn to identify opportunities for its application within your organization. Join us on this illuminating journey into the world of AI and unleash its transformative potential.

Course Code/Duration:

BDT38 / 1 Day

Learning Objectives:

After this course, you will be able to:

Understand the differences among AI, Machine Learning, and Deep Learning
Understand how machines learn
Understand the importance of data and how to acquire appropriate data
Understand AI strategy and building AI projects
Recognizing opportunities for AI
Understand some of the tools employed in executing AI

Basic programming knowledge will be helpful but not required.

This course is designed for Analyst, developer, or consultant who are interested in leveraging the power of AI for business and jobs.

Course Outline:

Introduction to lecture
AI, Machine Learning and Deep Learning
How machines learn and the importance of data
Shallow vs. Deep Neural Networks
Convolutional Neural Networks and use cases
Recurrent Neural Networks and use cases
Demonstration – AI using Google Cloud Platform
Examples of AI in the real world
Identifying opportunities for AI in your world
Next steps

Structured Activity/Exercises/Case Studies:

Demonstration – AI using Google Cloud Platform

Training material provided:

Yes (Digital format)

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]]> https://project.bigdatatrunk.com/courses/introduction-to-artificial-intelligence/feed/ 0 Machine Learning Is For Everyone (Lecture) https://project.bigdatatrunk.com/courses/machine-learning-is-for-everyone/ https://project.bigdatatrunk.com/courses/machine-learning-is-for-everyone/#respond Sun, 20 Dec 2020 22:27:36 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=21770 In this Course understand Easily identifies trends and patterns,No human intervention needed (automation) ,Continuous Improvement,,Handling multi-dimensional and multi-variety data,Wide Applications.

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Overview
Prerequisites
Audience
Curriculum

Description:

In this Course understand Easily identifies trends and patterns,No human intervention needed (automation) ,Continuous Improvement,,Handling multi-dimensional and multi-variety data,Wide Applications.

Long Description:

Unlock the power of Machine Learning in our comprehensive course. Seamlessly identify trends and patterns, all without the need for human intervention through automation. Embrace continuous improvement as you gain proficiency in handling multi-dimensional and multi-variety data, opening doors to a wide array of applications. Our Machine Learning Lecture is your gateway to understanding the real-world applications of AI and Machine Learning, including patterns and use cases. Explore concepts such as Supervised and Unsupervised learning, demystify AI vs ML vs DL, and broaden your AI vocabulary with techniques like Classification, Clustering, and Regression. Join us for a transformative journey that culminates in a hands-on ML demonstration, equipping you with the tools and knowledge for what comes next

Course Code/Duration:

BDT37 / 1 Day

Learning Objectives:

After this course, you will be able to:

Describe Supervised and Unsupervised learning techniques and usages
Compare AI vs ML vs DL
Understand techniques like Classification, Clustering and Regression
Discuss how to identify which kinds of technique to be applied for specific use case
Understand the popular Machine offerings like Amazon Machine Learning, TensorFlow, Azure Machine Learning, Spark mlib, Python and R etc.
Understand usage of tools through a ML Demo

Familiarity with Java(or a similar object oriented language), XML is required.

Developers,Business Analysts who want to start a career in or wants to learn about the exciting domain of Data Science and Machine Learning, Non-technical professionals who waant to start a career in Machine Learning.

Course Outline:

Course Introduction
History and Background of AI and ML
Compare AI vs ML vs DL
Describe Supervised and Unsupervised learning techniques and usages
Machine Learning patterns
- Classification
- Clustering
- Regression
Gartner Hype Cycle for Emerging Technologies
Machine Learning offerings in Industry
Demo – ML using Azure ML studio
Demo – ML using Scikit-learn
References and Next steps

Structured Activity/Exercises/Case Studies:

Demo – ML using Azure ML studio
Demo – ML using Scikit-learn

Training material provided:

Yes (Digital format)

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]]> https://project.bigdatatrunk.com/courses/machine-learning-is-for-everyone/feed/ 0 Beginner’s Guide to Mobile App Development https://project.bigdatatrunk.com/courses/beginners-guide-to-mobile-app-development/ https://project.bigdatatrunk.com/courses/beginners-guide-to-mobile-app-development/#respond Sun, 20 Dec 2020 22:22:48 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=21768 The course will provide an overview of various mobile application development technologies with a focus into Android development framework. It will cover the setup of Android Studio, the IDE for developing Android apps and the implementation of a simple Android app.

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Overview
Prerequisites
Audience
Curriculum

Description:

The course will provide an overview of various mobile application development technologies with a focus into Android development framework. It will cover the setup of Android Studio, the IDE for developing Android apps and the implementation of a simple Android app.

Long Description:

Discover the dynamic world of mobile applications in our comprehensive training program. Mobile apps have become an integral part of our daily lives, with a multitude of uses ranging from productivity and entertainment to sports and navigation. As mobile device usage surpasses that of desktop computers, the industry is transitioning to a 'mobile-first' approach. This course offers an in-depth exploration of the three primary types of mobile apps - native, hybrid, and web-based, providing insights into the technologies underpinning their development. With Android as the dominant mobile platform, our session focuses on Android app setup and development using Android Studio, offering hands-on experience in creating, testing, and navigating Android apps.

Course Code/Duration:

BDT41 / 1 Day

Learning Objectives:

After this course, you will be able to:

Relate to the significance of mobile apps in the present-day world.
Draw highlights of the various mobile app development platforms.
Setup development environment for Android apps.
Understand high-level architecture of Android framework.
Get an outline of the different Android application components.
Build simple UI layouts in Android apps.
Deploy and interact with the sample app on an emulator.

Familiarity with Java(or a similar object oriented language), XML is required.

This course is designed for all Developers, Analyst and Architects who want to learn how to expand their skills. Non-technical entrepreneurs who want to start a business building mobile apps

Course Outline:

Course Introduction
Discuss briefly about the increasing usage of mobile apps.
Talk about different mobile application platforms.
Review different kinds of mobile apps and the development technologies involved.
Dive deep into Android platform.
Introduction to different components in Android framework.
Install Android Studio.
Setup emulator using Android Studio.
Build an Android app with couple of screens and simple UI layouts.
Run the app on emulator and test it.
References to learn more.

Structured Activity/Exercises/Case Studies:

Exercise 1 – Install Android Studio.
Exercise 2 – Setup emulator using Android Studio.
Exercise 3 – Create a new project in Android Studio and build an Android app, run the app on the emulator.

Training material provided:

Yes (Digital format)

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]]> https://project.bigdatatrunk.com/courses/beginners-guide-to-mobile-app-development/feed/ 0 Introduction to the Art & Science of Data Management https://project.bigdatatrunk.com/courses/introduction-to-the-art-science-of-data-management/ https://project.bigdatatrunk.com/courses/introduction-to-the-art-science-of-data-management/#respond Sun, 20 Dec 2020 22:15:32 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=21766 The Introduction to the Art & Science of Data Management course will expose you to the data practices that allow you to get the most value from your data. Good data management is necessary to find the delicate balance between data enablement and data security and privacy.

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Overview
Prerequisites
Audience
Curriculum

Description:

The Introduction to the Art & Science of Data Management course will expose you to the data practices that allow you to get the most value from your data. Good data management is necessary to find the delicate balance between data enablement and data security and privacy.

Long Description:

Explore Data Management with 'Introduction to the Art & Science of Data Management' Training. Covering the data management lifecycle and industry best practices, this course is based on DAMA.org's DMBOK, enriched with global data management experiences. Learn to apply metadata, build business glossaries, manage reference data, and use data-cleaning tools. Interactive activities reinforce learning. Elevate your data practices - enroll today

Course Code/Duration:

BDT36 / 1 Day

Learning Objectives:

After this course, you will be able to:

Learn the fundamental principles that guide effective data management.
Clarify the scope and limitations of data management within organizations.
Gain a comprehensive understanding of standard terms and definitions in data management, including roles and deliverables.
Help data stewards and professionals comprehend their vital roles and responsibilities in data management.
Learn how to assess the effectiveness and maturity of data management practices.
Understand the diverse range and types of metadata that enhance data utility and value.
Identify and implement best practices for identifying, managing, and sharing reference data.
Apply data cleansing and standardization techniques to enhance overall data quality.

Experience as a data manager or provider is useful.

Business user

Course Outline:

Course Introduction
Reference to DMBOK (Data Management Book of Knowledge) and DAMA.org
Data management lifecycle and key roles in the data management world
Data management organization and role expectations
Introduction to core data management area and concepts
Data management maturity assessment overview
Data Security and data handling overview
Understand how data management relates to Big Data and Data Science
Metadata management overview & application
Reference and data overview & application
Data quality overview & application
Exercise 1 – Role play for a typical Data Management team
Exercise 2 – Exploration of tools for data cleansing and exploration of data
Exercise 3 – Craft business-centered data definitions
References, certification information, and next steps

Structured Activity/Exercises/Case Studies:

Exercise 1 – Role play for a typical Data Management team
Exercise 2 – Exploration of tools for data cleansing and exploration of data
Exercise 3 – Craft business-centered data definitions

Training material provided:

Yes (Digital format)

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