Praktyczne szkolenia na żywo z Data Analysis pokazuje jak poprzez praktyczne ćwiczenia opanować podstawy i zaawansowane koncepcje. Na szkoleniu Data Analysis poznasz proces przetwarzania danych, które będziesz mógł następnie wykorzystać do różnych celów m.in jak tworzyć różne raporty na podstawie użytecznych informacji i wniosków. W zależności od rodzaju danych i stawianych problemów, może to oznaczać użycie metod statystycznych, eksploracyjnych i innych.
Szkolenia Data Analysis są dostępne jako "szkolenie na miejscu" lub "szkolenie na żywo". Szkolenie stacjonarne może odbywać się lokalnie w siedzibie klienta w Ostrowiec Świętokrzyski lub w ośrodkach szkoleniowych NobleProg w Ostrowiec Świętokrzyski. Zdalne szkolenie online odbywa się za pomocą interaktywnego, zdalnego pulpitu DaDesktop .
NobleProg -- Twój lokalny dostawca szkoleń.
Ostrowiec Świętokrzyski
Centrum Biurowo – Konferencyjne, Sandomierska 26A, Ostrowiec Świętokrzyski, Polska, 27-400
Sale szkoleniowe są przestronne, dobrze oświetlone oraz doskonale wyposażone, co zapewnia komfortowe wa...
Sale szkoleniowe są przestronne, dobrze oświetlone oraz doskonale wyposażone, co zapewnia komfortowe warunki dla uczestników. Dodatkowo, w budynku znajduje się podjazd oraz winda, co ułatwia dostęp dla osób z ograniczoną mobilnością oraz ułatwia transport sprzętu szkoleniowego. Obiekt usytuowany jest zaledwie 3.5 kilometra od centrum miasta, co umożliwia szybki i łatwy dostęp do innych atrakcji miejskich. Ponadto, znajduje się tylko 600 metrów od najbliższego przystanku autobusowego, co ułatwia dojazd uczestnikom korzystającym z komunikacji miejskiej.
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.
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
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.
Kurs jes przeznaczony dla analityków, badaczy naukowych, statystyków, osób na co dzien wykorzystujących MS Excel w pracy oraz wszystkich tych którzy chcieliby poznać jakie możliwości daje analiza statystyczna w Excel.
Kurs poprawia znajomość programu Excel, statystyki, analizy statystycznej oraz zwiększa efektywności i skuteczność pracy czy też prowadzonych badań. Szkolenie opisuje jak używać Analysis ToolPack w programie Microsoft Excel, funkcji statystycznych oraz jak wykonywać procedury statystyczne. Szkolenie pokazuje ograniczenia Excela i wyjaśnia jak je przezwyciężyć.
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.
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.
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.
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
This instructor-led, live training (online or onsite) is aimed at HR professionals and recruitment specialists who wish to use analytical methods improve organisational performance. This course covers qualitative as well as quantitative, empirical and statistical approaches.
Format of the Course
Interactive lecture and discussion.
Lots of exercises and practice.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
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.
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.
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:
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
This instructor-led, live training (online or onsite) is aimed at HR professionals who wish to use analytical methods improve organisational performance. This course covers qualitative as well as quantitative, empirical and statistical approaches.
Format of the Course
Interactive lecture and discussion.
Lots of exercises and practice.
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.
IBM Cloud Pak for Data is a multi-cloud software platform for collecting, organizing and analyzing data for use in AI.
This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use IBM Cloud Pak to prepare data for use in AI solutions.
By the end of this training, participants will be able to:
Install and configure Cloud Pak for Data.
Unify the collection, organization and analysis of data.
Integrate Cloud Pak for Data with a variety of services to solve common business problems.
Implement workflows for collaborating with team members on the development of an AI solution.
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.
Python is a high-level programming language famous for its clear syntax and code readability. Excel is a spreadsheet application developed by Microsoft which is widely used in many industries. Adding Python to Excel makes it a powerful tool for data analytics.
In this instructor-led, live training (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.
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
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.
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
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.
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.
OpenSearch (successor of Elasticsearch) is an open source software for search and analytics. It provides an easy-to-use visualization tool, OpenSearch Dashboards, for real-time data monitoring and reporting.
This instructor-led, live training (online or onsite) is aimed at developers and administrators who wish to use OpenSearch to perform distributed search and data analytics in a unified and secure system.
By the end of this training, participants will be able to:
Set up the necessary development environment to start building search and analytics solutions with OpenSearch and OpenSearch Dashboards.
Understand the three approaches (snapshot, restart, and rolling) in upgrading from Elasticsearch OSS to OpenSearch.
Learn how to index data, create data streams, run queries, and streamline cross-cluster operations in OpenSearch.
Explore and use plugins, APIs, clients, and ingestion tools (Beats, Logstash, Grafana, etc.) to optimize search experience while keeping clusters secure.
Use OpenSearch Dashboards to centralize data management, visualization, logging, monitoring, and maintenance.
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.
Your company wishes to gain insights from the data it has collected over the years. Yet you face endless options of tools and approaches for doing so. There are user friendly UI tools such PowerBi, backend command line tools like SQL, and versatile "data wrangling" programming languages like Python and R.
This course elects the most powerful and flexible tool for the job: Python.
Python is a readable programming language. It offers something that most other solutions cannot: flexibility and adaptability. After the initial investment in learning Python, you can use it to manipulate and visualize your data in ways that would be difficult and time-consuming in other tools.
This instructor-led, live training (online or onsite) is aimed at persons who wish to learn just enough Python to begin crunching numbers from sales data, traffic analytics, customer interactions, etc.
The training is designed to enhance learning, retention, and hands-on practice. By combining different learning approaches such as online learning, live classroom interaction, peer learning and pair programming, this training aims to maximize the effect of each lesson. It reinforces learned concepts through a mix of pre-learning, interactive learning, and post-learning, all while including a social element that makes the course fun and engaging. The training is divided into three parts:
Pre-course
In-course
Post-course
This training is different from pure online learning in that it emphasizes live interaction with the trainer. It is also different from a traditional classroom in that it includes offline learning and self-paced practice.
A unique aspect of NobleProg trainings is its "pair programming" approach to learning. Pair programming allows two or more person's to collaboratively solve challenging and thought-provoking problems on the same machine. This approach has proven to be a powerful and efficient way to teach and learn. NobleProg makes this possible through its learning platform, DaDesktop. DaDesktop provides a collaborative space for participants and instructors to share and interact with each others' machines in real-time. Video conferencing and the ability to record lessons are just some of the features included in DaDesktop as part of this training.
By the end of this training, participants will be able to:
Install and configure the necessary software, libraries and development environment to begin writing just enough Python code for data analysis.
Analyze data from sources such as Excel, CSV, JSON files and databases.
Clean data to improve its usefulness before analyzing it.
Perform simple statistical analysis.
Generate reports that present the desired data in just the right format, from straight numbers to charts, to graphs and tables.
Gain valuable insight from data, including trends in performance, and problem areas to make better business decisions.
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 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
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
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.
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.
Exploratory data analysis refers to the analysis of a data set to better understand its characteristics. This analysis is mostly done using graphs, plots, and other visual techniques.
This instructor-led, live training (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.
Format of the Course
Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.
Course Customization Options
This course can be offered in Python or R language.
To request a customized training for this course, please contact us to arrange.
LibreOffice Calc is the spreadsheet component of the LibreOffice software suite. It is a cross-platform and free alternative to Microsoft Excel, providing the full features for advanced analysis.
This instructor-led, live training (online or onsite) is aimed at data analysts who wish to use LibreOffice Calc to conduct many forms of data analysis (from basic to advanced), leveraging similar functionalities found in Excel and other spreadsheet software.
By the end of this training, participants will be able to:
Install the LibreOffice suite on any operating system.
Understand the full features and functionalities available in LibreOffice Calc.
Learn how to clean, validate, manipulate, and visualize data in Calc.
Apply the various functions and tools in Calc to perform complex data analysis.
Gain some tips and insights on conducting advanced data analysis.
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.
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Opinie uczestników (25)
Examples/exercices perfectly adapted to our domain
Luc - CS Group
Szkolenie - Scaling Data Analysis with Python and Dask
Tematyka BPM jest mi bliska z uwagi na pełnioną rolę. Filip ma fajne, luźne podejście i świetnie tłumaczy omawiane zagadnienia. Dawkuje wiedzę w dobrym tempie i ilości.
Materiał jest bardzo fajny i przejrzysty a przejście szkolenia daje przyjemne poczucie podniesienia umiejętności. Fajnie, że znaleźliśmy czas na przyjrzenie się również naszym problemom z BPMN i podpowiedzenie rozwiązań.
Strona informatyczna też jest moim zdaniem mocną stroną szkolenia. Wszystko działa, nic się nie psuje, nie ma opóźnień i nie trzeba zajmować się techniką tylko można skupić się na materiale.
Gratuluję świetnej platformy szkoleniowej.
Bogumił - GE Aerospace Poland Sp. z o.o.
Szkolenie - BPMN 2.0 for Business Analysts
it was very well organized
Ana - DB Global Technology SRL
Szkolenie - SQL Advanced level for Analysts
How big data work, data programs, greater knowledge of how our current world works using data
Ozayr Hussain - Vodacom
Szkolenie - A Practical Introduction to Data Analysis and Big Data
jasność, z jaką wyjaśnił cały kurs, a także gotowość do powrotu do agendy, gdy było to konieczne
Carlos Eloy - AMERICAN EXPRESS COMPANY MEXICO
Szkolenie - Data Analytics With R
Przetłumaczone przez sztuczną inteligencję
Dyskusja na temat zastosowania ElasticSearcha w naszym projekcie firmowym i jego optymalizacji.
Michal - Madkom
Szkolenie - Elasticsearch for Developers
bardzo wszechstronny w odniesieniu do analizy danych przy użyciu Pythona
Mervin Lau - MINDEF
Szkolenie - Python for Data Analysis
Przetłumaczone przez sztuczną inteligencję
Trener był bardzo dobry i sprawił, że szkolenie było idealne dla moich potrzeb.
Florentin - EXPERIS
Szkolenie - Search and Analytics with Amazon OpenSearch
Przetłumaczone przez sztuczną inteligencję
Wiedza, merytoryka, kontakt z innymi, chęć pomocy nawet w błachych problemach, umiejętność zaciekawienia słuchaczy.
Michał
Szkolenie - Python for Excel
The exercises and the practical part was good and reflected the topic we had learnt during the lecture before the task.
Justyna Legierska - RedCompass
Szkolenie - BPMN 2.0 Fundamentals and Workshop
Po każdym przerobionym rozdziale były ćwiczenia.
Grażyna - Atmoterm SA
Szkolenie - Business Analysis Essentials
otwartość i kontakt z prowadzącym
Dariusz - Devqube
Szkolenie - Analiza biznesowa i systemowa z użyciem notacji UML - warsztat praktyczny dla PO w metodyce Scrum
The trainer highlighted the really important concepts in software architecture with quizzes.
The high-level perspective on effective implementation of software development cycle starting from requirements elicitation to unit testing is very refreshing to data scientist without much experience in software development.
Kok Cheong Cheng - DBS Bank
Szkolenie - Design Patterns
Zadania do zrealizowania samodzielnie oraz późniejsze wspólne rozwiązywanie
Katarzyna Kopysc-Falenta - CapGemini
Szkolenie - Data Analysis with Python, Pandas, and Numpy
ćwiczenia i dobre praktyki wskazane na spotkaniu. narzędzia używane podczas szkolenia
Ania - DGS Business Services Sp. z o.o.
Szkolenie - BPMN for Analysts
Sposób prowadzenia szkolenia
Andrzej Zaczek - Fundacja DKMS
Szkolenie - Excel For Statistical Data Analysis
Szerszy kontekst całości, której fragmentem się zajmuje
Anna Gocała - Powszechny Zakład Ubezpieczeń Spółka Akcyjna
Szkolenie - Business Analysis, BABOK V3.0 and IIBA Certification Preparation
Good exercises
Hui Yi Neo - Titansoft Pte Ltd
Szkolenie - Object Oriented Design using Design Patterns
Forma przekazania wiedzy poprzez dokładne wyjaśnienie zagadnienia.
Katarzyna Stańczak - Mowi Poland SA
Szkolenie - Data Mining and Analysis
I thought he did a great job of tailoring the experience to the audience. This class is mostly designed to cover data analysis with HIVE, but me and my co-worker are doing HIVE administration with no real data analytics responsibilities.
ian reif - Franchise Tax Board
Szkolenie - Data Analysis with Hive/HiveQL
Focusing on applying the course on my real work.
shahla - ECRA
Szkolenie - Introduction to Business Analysis: Defining Successful Projects
I really was benefit from the real life practical examples.
Wioleta
Szkolenie - Data and Analytics - from the ground up
Podstawowe informacje pozwalajae na samodzielne rozpoczęcie pracy i uwagi dot. optymalizacji podejścia
Robert Kaźmierczak
Szkolenie - Microsoft Office Excel - moduł Business Intelligence
"gawędziarski" styl prowadzenia
Tomasz Niziołek
Szkolenie - Business Analysis
I enjoyed all of Day 1.
Peter
Szkolenie - Contemporary Development Principles and Practices
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