Data Analysis Training Courses

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  2. Data Analysis Training Courses
Data Analysis Training Courses

Τα τοπικά μαθήματα κατάρτισης δεδομένων που βασίζονται σε ανάλυση δεδομένων Ανάλυση Δεδομένων (Data Analysis ή Data Analysis), καθοδηγούμενα από εκπαιδευτές, καταδεικνύουν μέσω της συζήτησης και της πρακτικής άσκησης τις γλώσσες προγραμματισμού και τις μεθοδολογίες που χρησιμοποιούνται για την ανάλυση δεδομένων. Η εκπαίδευση Ανάλυσης Δεδομένων είναι διαθέσιμη ως "επιτόπου ζωντανή εκπαίδευση" ή "απομακρυσμένη ζωντανή εκπαίδευση". Η επιτόπια κατάρτιση σε πραγματικό χρόνο μπορεί να πραγματοποιηθεί σε τοπικό επίπεδο στις εγκαταστάσεις του πελάτη Ελλάδα ή σε εταιρικά κέντρα κατάρτισης NobleProg στο Ελλάδα . Η απομακρυσμένη ζωντανή προπόνηση πραγματοποιείται μέσω μιας διαδραστικής, απομακρυσμένης επιφάνειας εργασίας. NobleProg - Ο τοπικός παροχέας εκπαίδευσης

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Data Analysis Subcategories

Business Intelligence Training CoursesBusiness Intelligence
Data Visualization Training CoursesData Visualization
KNIME Training CoursesKNIME
Microsoft Power Platform Training CoursesMicrosoft Power Platform
Business Analysis Training CoursesBusiness Analysis
System Monitoring Training CoursesSystem Monitoring
Data Cleaning Training CoursesData Cleaning
Spatial Analysis Training CoursesSpatial Analysis
Web Analytics Training CoursesWeb Analytics

Data Analysis Course Outlines

Course Name
Duration
Overview
Course Name
Duration
Overview

BPMN 2.0 for Business Analysts

21 hours
Overview
This course has been created for business analysts who want to use BPMN 2.0 extensively in their projects.

It focuses on practical aspects of all BPMN 2.0 specification as well as implementations of common patterns.

It is a series of short lectures followed by exercises: the delegates will have a problem described in English, and will have to create a proper diagram for each problem. After that, the diagrams will be discussed and assessed by the group and the trainer.

This course doesn't cover execution part of BPMN, it focuses on analysis and process design aspects of BPMN 2.0.

Excel For Statistical Data Analysis

14 hours
Overview
Audience

Analysts, researchers, scientists, graduates and students and anyone who is interested in learning how to facilitate statistical analysis in Microsoft Excel.

Course Objectives

This course will help improve your familiarity with Excel and statistics and as a result increase the effectiveness and efficiency of your work or research.

This course describes how to use the Analysis ToolPack in Microsoft Excel, statistical functions and how to perform basic statistical procedures. It will explain what Excel limitation are and how to overcome them.

Python Programming

28 hours
Overview
This course is designed for those wishing to learn the Python programming language. The emphasis is on the Python language, the core libraries, as well as on the selection of the best and most useful libraries developed by the Python community. Python drives businesses and is used by scientists all over the world – it is one of the most popular programming languages.

The course can be delivered using the latest Python version 3.x with practical exercises making use of the full power. This course can be delivered on any operating system (all flavours of UNIX, including Linux and Mac OS X, as well as Microsoft Windows).

The practical exercises constitute about 70% of the course time, and around 30% are demonstrations and presentations. Discussions and questions can be asked throughout the course.

Note: the training can be tailored to specific needs upon prior request ahead of the proposed course date.

Data Analytics With R

21 hours
Overview
[R](http://www.r-project.org/) 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

Data Analysis with Hive/HiveQL

7 hours
Overview
This course covers how to use Hive SQL language (AKA: Hive HQL, SQL on Hive, HiveQL) for people who extract data from Hive

Data Analysis in Python using Pandas and Numpy

14 hours
Overview
Pandas is a Python package that provides data structures for working with structured (tabular, multidimensional, potentially heterogeneous) and time series data.

Knowledge Discovery in Databases (KDD)

21 hours
Overview
Knowledge discovery in databases (KDD) is the process of discovering useful knowledge from a collection of data. Real-life applications for this data mining technique include marketing, fraud detection, telecommunication and manufacturing.

In this instructor-led, live course, we introduce the processes involved in KDD and carry out a series of exercises to practice the implementation of those processes.

Audience

- Data analysts or anyone interested in learning how to interpret data to solve problems

Format of the Course

- After a theoretical discussion of KDD, the instructor will present real-life cases which call for the application of KDD to solve a problem. Participants will prepare, select and cleanse sample data sets and use their prior knowledge about the data to propose solutions based on the results of their observations.

NLP: Natural Language Processing with R

21 hours
Overview
It is estimated that unstructured data accounts for more than 90 percent of all data, much of it in the form of text. Blog posts, tweets, social media, and other digital publications continuously add to this growing body of data.

This instructor-led, live course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements.

By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance.

Format of the Course

- Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding

A Practical Introduction to Data Analysis and Big Data

35 hours
Overview
Participants who complete this instructor-led, live training in Ελλάδα 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.

Elasticsearch for Developers

14 hours
Overview
This instructor-led, live training in Ελλάδα 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.

MATLAB Fundamentals, Data Science & Report Generation

35 hours
Overview
In the first part of this training, we cover the fundamentals of MATLAB and its function as both a language and a platform. Included in this discussion is an introduction to MATLAB syntax, arrays and matrices, data visualization, script development, and object-oriented principles.

In the second part, we demonstrate how to use MATLAB for data mining, machine learning and predictive analytics. To provide participants with a clear and practical perspective of MATLAB's approach and power, we draw comparisons between using MATLAB and using other tools such as spreadsheets, C, C++, and Visual Basic.

In the third part of the training, participants learn how to streamline their work by automating their data processing and report generation.

Throughout the course, participants will put into practice the ideas learned through hands-on exercises in a lab environment. By the end of the training, participants will have a thorough grasp of MATLAB's capabilities and will be able to employ it for solving real-world data science problems as well as for streamlining their work through automation.

Assessments will be conducted throughout the course to gauge progress.

Format of the Course

- Course includes theoretical and practical exercises, including case discussions, sample code inspection, and hands-on implementation.

Note

- Practice sessions will be based on pre-arranged sample data report templates. If you have specific requirements, please contact us to arrange.

Advanced Python

28 hours
Overview
In this instructor-led, live training in Ελλάδα, participants will learn advanced Python programming techniques, including how to apply this versatile language to solve problems in areas such as distributed applications, data analysis and visualization, UI programming and maintenance scripting.

Data and Analytics - from the ground up

42 hours
Overview
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](https://www.nobleprog.co.uk/instructor-led-online-training-courses) over a period of weeks if preferred. We can work with you to deliver the course to best suit your needs.

SQL Advanced level for Analysts

21 hours
Overview
The aim of this course is to provide a clear understanding of the use of SQL for different
databases (Oracle, SQL Server, MS Access...). Understanding of analytic functions and the
way how to join different tables in a database will help delegates to move data analysis
operations to the database side, instead of doing this in MS Excel application. This can also
help in creating any IT system, which uses any relational database.

Apache Kylin: From Classic OLAP to Real-Time Data Warehouse

14 hours
Overview
Apache Kylin is an extreme, distributed analytics engine for big data.

In this instructor-led live training, participants will learn how to use Apache Kylin to set up a real-time data warehouse.

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

- Consume real-time streaming data using Kylin
- Utilize Apache Kylin's powerful features, rich SQL interface, spark cubing and subsecond query latency

Note

- We use the latest version of Kylin (as of this writing, Apache Kylin v2.0)

Audience

- Big data engineers
- Big Data analysts

Format of the course

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

Datameer for Data Analysts

14 hours
Overview
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

Embedding Projector: Visualizing Your Training Data

14 hours
Overview
Embedding Projector is an open-source web application for visualizing the data used to train machine learning systems. Created by Google, it is part of TensorFlow.

This instructor-led, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project.

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

- Explore how data is being interpreted by machine learning models
- Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it
- Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals.
- Explore the properties of a specific embedding to understand the behavior of a model
- Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers

Audience

- Developers
- Data scientists

Format of the course

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

kdb+ and q: Analyze Time Series Data

21 hours
Overview
kdb+ is an in-memory, column-oriented database and q is its built-in, interpreted vector-based language. In kdb+, tables are columns of vectors and q is used to perform operations on the table data as if it was a list. kdb+ and q are commonly used in high frequency trading and are popular with the major financial institutions, including Goldman Sachs, Morgan Stanley, Merrill Lynch, JP Morgan, etc.

In this instructor-led, live training, participants will learn how to create a time series data application using kdb+ and q.

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

- Understand the difference between a row-oriented database and a column-oriented database
- Select data, write scripts and create functions to carry out advanced analytics
- Analyze time series data such as stock and commodity exchange data
- Use kdb+'s in-memory capabilities to store, analyze, process and retrieve large data sets at high speed
- Think of functions and data at a higher level than the standard function(arguments) approach common in non-vector languages
- Explore other time-sensitive applications for kdb+, including energy trading, telecommunications, sensor data, log data, and machine and network usage monitoring

Audience

- Developers
- Database engineers
- Data scientists
- Data analysts

Format of the course

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

F# for Data Science

21 hours
Overview
Data science is the application of statistical analysis, machine learning, data visualization and programming for the purpose of understanding and interpreting real-world data. F# is a well suited programming language for data science as it combines efficient execution, REPL-scripting, powerful libraries and scalable data integration.

In this instructor-led, live training, participants will learn how to use F# to solve a series of real-world data science problems.

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

- Use F#'s integrated data science packages
- Use F# to interoperate with other languages and platforms, including Excel, R, Matlab, and Python
- Use the Deedle package to solve time series problems
- Carry out advanced analysis with minimal lines of production-quality code
- Understand how functional programming is a natural fit for scientific and big data computations
- Access and visualize data with F#
- Apply F# for machine learning

Explore solutions for problems in domains such as business intelligence and social gaming

Audience

- Developers
- Data scientists

Format of the course

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

R Programming for Finance

28 hours
Overview
R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.

In this instructor-led, live training, participants will learn how to use R to develop practical applications for solving a number of specific finance related problems.

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

- Understand the fundamentals of the R programming language
- Select and utilize R packages and techniques to organize, visualize, and analyze financial data from various sources (CSV, Excel, databases, web, etc.)
- Build applications that solve problems related to asset allocation, risk analysis, investment performance and more
- Troubleshoot, integrate deploy and optimize an R application

Audience

- Developers
- Analysts
- Quants

Format of the course

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

Note

- This training aims to provide solutions for some of the principle problems faced by finance professionals. However, if you have a particular topic, tool or technique that you wish to append or elaborate further on, please please contact us to arrange.

Apache Drill

21 hours
Overview
Apache Drill is a schema-free, distributed, in-memory columnar SQL query engine for Hadoop, NoSQL and other Cloud and file storage systems. The power of Apache Drill lies in its ability to join data from multiple data stores using a single query. Apache Drill supports numerous NoSQL databases and file systems, including HBase, MongoDB, MapR-DB, HDFS, MapR-FS, Amazon S3, Azure Blob Storage, Google Cloud Storage, Swift, NAS and local files. Apache Drill is the open source version of Google's Dremel system which is available as an infrastructure service called Google BigQuery.

In this instructor-led, live training, participants will learn the fundamentals of Apache Drill, then leverage the power and convenience of SQL to interactively query big data across multiple data sources, without writing code. Participants will also learn how to optimize their Drill queries for distributed SQL execution.

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

- Perform "self-service" exploration on structured and semi-structured data on Hadoop
- Query known as well as unknown data using SQL queries
- Understand how Apache Drills receives and executes queries
- Write SQL queries to analyze different types of data, including structured data in Hive, semi-structured data in HBase or MapR-DB tables, and data saved in files such as Parquet and JSON.
- Use Apache Drill to perform on-the-fly schema discovery, bypassing the need for complex ETL and schema operations
- Integrate Apache Drill with BI (Business Intelligence) tools such as Tableau, Qlikview, MicroStrategy and Excel

Audience

- Data analysts
- Data scientists
- SQL programmers

Format of the course

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

AI Awareness for Telecom

14 hours
Overview
AI is a collection of technologies for building intelligent systems capable of understanding data and the activities surrounding the data to make "intelligent decisions". For Telecom providers, building applications and services that make use of AI could open the door for improved operations and servicing in areas such as maintenance and network optimization.

In this course we examine the various technologies that make up AI and the skill sets required to put them to use. Throughout the course, we examine AI's specific applications within the Telecom industry.

Audience

- Network engineers
- Network operations personnel
- Telecom technical managers

Format of the course

- Part lecture, part discussion, hands-on exercises

Dremio for Self-Service Data Analysis

21 hours
Overview
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.

Python for Excel

14 hours
Overview
In this instructor-led, live training in Ελλάδα (onsite or remote), participants will learn how to combine the capabilities of Python and Excel.

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

- Install and configure packages for integrating Python and Excel.
- Read, write, and manipulate Excel files using Python.
- Call Python functions from Excel.

Data Analysis for Marketers

14 hours
Overview
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.

A Practical Introduction to Data Science

35 hours
Overview
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.

Apache Arrow for Data Analysis across Disparate Data Sources

14 hours
Overview
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.

Creating Dashboards Using Microsoft Power BI

14 hours
Overview
About the Course:

Microsoft Power BI transforms your company data into rich visuals that facilitate new ways of thinking about and organizing your data, so that you can focus on what is important to achieving your goals. This course covers both Power BI on Line and Power BI desktop.

Audience:

This course is intended for Business Managers, Report Developers, Analysts, Project Managers and Team Leads.

At Course Completion

Upon completing this course, students will have a basic understanding of the topics below, as well as an ability to utilize and implement the concepts learned.

- Power BI
- Power BI Desktop
- Working with CSV, TXT, and Excel Worksheets
- Connecting to Databases
- Merging, Grouping, Summarizing, and Calculating Data
- Reporting
- Power BI Online

Exploratory Data Analysis

7 hours
Overview
This instructor-led, live training in Ελλάδα (online or onsite) is aimed at persons who wish to use graphical and numerical techniques to better understand the structure of a dataset before commencing more formal analysis.

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

- Identify variables that that indicate connections within the data.
- Identify unusual patterns in data.
- Generate visually appealing and easy to read graphics that provide insight into the data.
- Use exploratory analysis to pave the way for more complex statistical modeling.

Python Programming Fundamentals

14 hours
Overview
This course is designed for those wishing to learn the Python programming language. The emphasis is on the Python language, the core libraries, as well as on the selection of the best and most useful libraries developed by the Python community. Python drives businesses and is used by scientists all over the world – it is one of the most popular programming languages.

Upcoming Data Analysis Courses

IRIS: Document Classification and Data Capture

11-10-2021 09:30
7 hours
Remote 750 EUR Classroom N/A
Read more

QlikView Server Administration

11-10-2021 09:30
14 hours
Remote 1500 EUR Classroom N/A
Read more

Qlik Sense for Data Science

21-10-2021 09:30
14 hours
Remote 1500 EUR Classroom N/A
Read more

Qlik: Advanced Topics in Design and Development

27-10-2021 09:30
14 hours
Remote 1500 EUR Classroom N/A
Read more

Power BI Dashboards

09-11-2021 09:30
14 hours
Remote 1500 EUR Classroom N/A
Read more

Other countries

Consulting

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Course Discounts

Security Analyst

18-10-2021 09:30
35 hours
Online4250 EUR 3825 EUR Classroom5450 EUR N/A
Read more

Apache Spark MLlib

18-10-2021 09:30
35 hours
Online4250 EUR 3825 EUR Classroom5450 EUR N/A
Read more

Systems and Service Architecture - Microservices Architecture

02-12-2021 09:30
14 hours
Online1700 EUR 1530 EUR Classroom2300 EUR N/A
Read more

Process Mining

03-01-2022 09:30
21 hours
Online2550 EUR 2295 EUR Classroom3350 EUR N/A
Read more

Jenkins: Continuous Integration for Agile Development

24-01-2022 09:30
14 hours
Online1700 EUR 1530 EUR Classroom2300 EUR N/A
Read more
See All

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At the moment we are focusing on the following areas:

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