Databricksのトレーニングコース

Analytic Functions are a powerful feature in SQL that enables efficient and concise data analysis within a relational database. 

This instructor-led, live training (online or onsite) is aimed at intermediate-level database administrators, SQL developers, and data analysts who wish to enhance their SQL skills by mastering the use of Analytic Functions.

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

• Understand the concept and importance of Analytic Functions.
• Apply various Analytic Functions to solve complex analytical problems.
• Combine Analytic Functions with other SQL features for comprehensive data analysis.
• Optimize queries for better performance using Analytic Functions.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Apache Arrow is an open-source in-memory data processing framework. It is often used together with other data science tools for accessing disparate data stores for analysis. It integrates well with other technologies such as GPU databases, machine learning libraries and tools, execution engines, and data visualization frameworks.

In this onsite instructor-led, live training, participants will learn how to integrate Apache Arrow with various Data Science frameworks to access data from disparate data sources.

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

• Install and configure Apache Arrow in a distributed clustered environment
• Use Apache Arrow to access data from disparate data sources
• Use Apache Arrow to bypass the need for constructing and maintaining complex ETL pipelines
• Analyze data across disparate data sources without having to consolidate it into a centralized repository

Audience

• Data scientists
• Data engineers

Format of the Course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

• To request a customized training for this course, please contact us to arrange.

AWS Glue is a serverless service for data integration from multiple sources that allows users to easily discover, prepare, move, integrate, and analyze data.

This instructor-led, live training (online or onsite) is aimed at data engineers and analysts who wish to learn beginner to intermediate-level knowledge of using AWS Glue to set up a data pipeline.

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

• Understand the fundamentals of AWS Glue.
• Set up an AWS Glue pipeline.
• Set up AWS Glue crawlers and jobs.
• Learn how to use AWS Glue transformations.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Data analysis refers to the process of obtaining, cleaning, and analyzing data, often times in a visual manner, to derive insights for better decision making. More and more data is being stored in databases in the cloud so it is important to have a strategy for understanding, managing and accessing such data. One popular tool for accessing and visualizing data is Power Bi.

This instructor-led, live training (online or onsite) is aimed at technical persons who wish to gain a practical understanding of available cloud solutions, the data analysis processes needed to work with data in the cloud, and the hands-on practice to apply tools such as Power BI to analyze data.

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

• Install and configure Power BI.
• Evaluate the various data solutions offered by cloud providers such as Azure.
• Gain an understanding of the different structures, modeling approaches, and data warehouse designs used to store, manage and access Big Data.
• Apply tools and techniques to clean data in preparation for analysis.
• Build reporting and analytics solutions based on on-premise as well as cloud data.
• Integrate data analytics solutions with a data warehouse.
• Mitigate data security risks and ensure data privacy.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Participants who complete this instructor-led, live training will gain a practical, real-world understanding of Big Data and its related technologies, methodologies and tools.

Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class.

The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability.

Format of the Course

• Part lecture, part discussion, hands-on practice and implementation, occasional quizing to measure progress.

Data analytics is a crucial tool in business today. We will focus throughout on developing skills for practical hands on data analysis. The aim is to help delegates to give evidence-based answers to questions: 

What has happened?

• processing and analyzing data
• producing informative data visualizations

What will happen?

• forecasting future performance
• evaluating forecasts

What should happen?

• turning data into evidence-based business decisions
• optimizing processes

The course itself can be delivered either as a 6 day classroom course or remotely over a period of weeks if preferred. We can work with you to deliver the course to best suit your needs.

Dask is a flexible and high-performance Python library for parallel computing. It scales and accelerates big data processing with other Python-based data science libraries, such as Pandas, Numpy, and Scikit-Learn.

This instructor-led, live training (online or onsite) is aimed at data scientists and software engineers who wish to use Dask with the Python ecosystem to build, scale, and analyze large datasets.

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

• Set up the environment to start building big data processing with Dask and Python.
• Explore the features, libraries, tools, and APIs available in Dask.
• Understand how Dask accelerates parallel computing in Python.
• Learn how to scale the Python ecosystem (Numpy, SciPy, and Pandas) using Dask.
• Optimize the Dask environment to maintain high performance in handling large datasets.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

A business' marketing strategy is one of its most essential tools to ensure success. The rise in technology and advances in data gathering and analysis has led to a continuous transformation of marketing. Data analysis is now considered to be a crucial skill for marketers to learn. Likewise, applying data science to marketing principles is also important in helping data science professionals maximize their contributions.

In this instructor-led, live training, participants will learn how to gain valuable insights from large data sets and how to use these for marketing strategies.

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

• Install and configure tools for data-driven marketing tools (Tableau, Google Analytics, Zarget, etc)
• Measure, test, and optimize the performance of their marketing strategies using data
• Use data to segment their customers and prospects
• Use predictive modeling to improve response rates
• Implement step-by-step data analytics to drive better marketing decisions

Audience

• Marketers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

• To request a customized training for this course, please contact us to arrange.

R is a very popular, open source environment for statistical computing, data analytics and graphics. This course introduces R programming language to students.  It covers language fundamentals, libraries and advanced concepts.  Advanced data analytics and graphing with real world data.

Audience

Developers / data analytics

Duration

3 days

Format

Lectures and Hands-on

Datameer is a business intelligence and analytics platform built on Hadoop. It allows end-users to access, explore and correlate large-scale, structured, semi-structured and unstructured data in an easy-to-use fashion.

In this instructor-led, live training, participants will learn how to use Datameer to overcome Hadoop's steep learning curve as they step through the setup and analysis of a series of big data sources.

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

• Create, curate, and interactively explore an enterprise data lake
• Access business intelligence data warehouses, transactional databases and other analytic stores
• Use a spreadsheet user-interface to design end-to-end data processing pipelines
• Access pre-built functions to explore complex data relationships
• Use drag-and-drop wizards to visualize data and create dashboards
• Use tables, charts, graphs, and maps to analyze query results

Audience

• Data analysts

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Python is a versatile programming language known for its simplicity and readability. Pandas is a Python package that provides data structures for working with structured (tabular, multidimensional, potentially heterogeneous) and time series data. Numpy provides fundamental support for numerical computing with its array operations. Together, they form a robust ecosystem for efficient data handling and analysis in Python.

This instructor-led, live training (online or onsite) is aimed at intermediate-level Python developers and data analysts who wish to enhance their skills in data analysis and manipulation using Pandas and NumPy.

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

• Set up a development environment that includes Python, Pandas, and NumPy.
• Create a data analysis application using Pandas and NumPy.
• Perform advanced data wrangling, sorting, and filtering operations.
• Conduct aggregate operations and analyze time series data.
• Visualize data using Matplotlib and other visualization libraries.
• Debug and optimize their data analysis code.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Participants who complete this training will gain a practical, real-world understanding of Data Science and its related technologies, methodologies and tools.

Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class.

The course starts with an introduction to elemental concepts of Data Science, then progresses into the tools and methodologies used in Data Science.

Audience

• Developers
• Technical analysts
• IT consultants

Format of the Course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

• To request a customized training for this course, please contact us to arrange.

dbt (data build tool) is an open-source command-line interface (CLI) tool used to manage and transform data in SQL-based data warehouses. dbt Cloud is the cloud-hosted version of the dbt tool that provides a user-friendly interface for managing and deploying dbt projects.

This instructor-led, live training (online or onsite) is aimed at beginner-level data engineers, analysts, and scientists who wish to understand how to use dbt Cloud to manage their data warehousing and transformation projects in the cloud.

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

• Create and manage projects on dbt Cloud.
• Use the dbt Cloud interface to schedule and run data transformations.
• Collaborate on projects with team members.
• Deploy their dbt projects to production.
• Debug and troubleshoot dbt projects.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Dremio is an open-source "self-service data platform" that accelerates the querying of different types of data sources. Dremio integrates with relational databases, Apache Hadoop, MongoDB, Amazon S3, ElasticSearch, and other data sources. It supports SQL and provides a web UI for building queries.

In this instructor-led, live training, participants will learn how to install, configure and use Dremio as a unifying layer for data analysis tools and the underlying data repositories.

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

• Install and configure Dremio
• Execute queries against multiple data sources, regardless of location, size, or structure
• Integrate Dremio with BI and data sources such as Tableau and Elasticsearch

Audience

• Data scientists
• Business analysts
• Data engineers

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Notes

• To request a customized training for this course, please contact us to arrange.

Elasticsearch is an open-source, distributed search engine. It is commonly used together with Logstash (data-collection and log-parsing engine) and Kibana (analytics and visualization platform) to make up the "ELK stack".

This instructor-led, live training is aimed at software developers who wish to build search and analytics solutions using Elasticsearch.

The training starts with a discussion of the Elasticsearch architecture, including its distributed model and search API. This is followed by an explanation of Elasticsearch's functionality and how to best integrate it into an existing application.

Hands-on exercises make up an important part of the training, and give participants a chance to put into practice their knowledge while receiving feedback on their implementation and progress.

Format of the Course

• Heavy emphasis on live practice. Most of the concepts are learned through samples, exercises and hands-on development.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.