Online or onsite, instructor-led live Data Analysis (Analysis of Data or Data Analytics) training courses demonstrate through discussion and hands-on practice the programming languages and methodologies used to perform Data Analysis.
Data Analysis training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Data Analysis trainings in South Carolina can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
SC, Charleston - Faber Center
4000 S. Faber Place Drive Suite 300, Charleston, united states, 29405
The venue is located in the Farber Center where you can also find the North American Merchant Services, Lima One Capital and ...
The venue is located in the Farber Center where you can also find the North American Merchant Services, Lima One Capital and Charleston Country Economic Development.
SC, Charleston - Meeting Street
170 Meeting Street, Charleston, united states, 29401
The venue is located in the hear of vibrant cultural area of downtown Charleston. Only 20 minutes away from Charleston Intern...
The venue is located in the hear of vibrant cultural area of downtown Charleston. Only 20 minutes away from Charleston International Airport with short walk to Museums and parks.
This instructor-led, live training in South Carolina (online or onsite) is aimed at beginner-level to intermediate-level data analysts and business professionals who wish to perform complex data analysis tasks more intuitively across various industries using Google Gemini AI.By the end of this training, participants will be able to:
This instructor-led, live training in South Carolina (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.
This instructor-led, live training in South Carolina (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:
This instructor-led, live training in South Carolina (online or onsite) is aimed at analysts who wish to use TM1 to analyze and customize reports and templates for data in a database.
By the end of this training, participants will be able to:
Learn how to analyze data using IBM Planning Analytics.
Create custom views of the data in a database.
Build reports and forms that communicate with TM1.
This instructor-led, live training in South Carolina (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.
This instructor-led, live training in South Carolina (online or onsite) is aimed at data analysts and data scientists who wish to use the MongoDB Aggregation Framework to perform data analysis while simplifying complex operations and aggregation pipelines.
By the end of this training, participants will be able to:
Install and configure MongoDB for data analysis.
Understand the concepts and stages of the MongoDB Aggregation Framework.
Learn about the basic structure, syntax, and operations for aggregation.
Learn how to handle advanced operations in aggregation.
Apply some optimization tools and techniques to improve aggregation performance.
Participants who complete this instructor-led, live training in South Carolina 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.
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
This instructor-led, live training in South Carolina 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.
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.
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.
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.
This instructor-led, live training in South Carolina (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.
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.
This instructor-led, live training in South Carolina (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.
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.
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
This instructor-led, live training in South Carolina (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.
This instructor-led, live training in South Carolina (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.
This instructor-led, live training in South Carolina (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.
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Testimonials (25)
Examples/exercices perfectly adapted to our domain
Luc - CS Group
Course - Scaling Data Analysis with Python and Dask
The topic of BPM is close to me due to the role I play. Filip has a nice, relaxed approach and explains the issues discussed very well. Dispenses knowledge at a good pace and quantity. The material is very nice and clear, and completing the training gives you a pleasant feeling of improving your skills. It's nice that we found time to also look at our problems with BPMN and suggest solutions. In my opinion, the IT side is also a strong point of the training. Everything works, nothing breaks, there are no delays and you don't have to worry about technology, you can just focus on the material. Congratulations on a great training platform.
Bogumił - GE Aerospace Poland Sp. z o.o.
Course - BPMN 2.0 for Business Analysts
Machine Translated
it was very well organized
Ana - DB Global Technology SRL
Course - SQL Advanced level for Analysts
the personalized part during which I was able to work on my project and directly apply the elements of the training
Caroline - Schreder
Course - Power BI Dashboards
Machine Translated
not intimidating
Jaztin - Flo Energy
Course - Business Process Modelling in BPMN 2.0
How big data work, data programs, greater knowledge of how our current world works using data
Ozayr Hussain - Vodacom
Course - A Practical Introduction to Data Analysis and Big Data
the clarity with which he explained the entire course, as well as the willingness to return to the syllabus when necessary
Carlos Eloy - AMERICAN EXPRESS COMPANY MEXICO
Course - Data Analytics With R
Machine Translated
Discussion on the use of ElasticSearchin our company project and its optimization.
Michal - Madkom
Course - Elasticsearch for Developers
Machine Translated
very comprehensive in regards to doing data analysis using python
Mervin Lau - MINDEF
Course - Python for Data Analysis
the trainer was very good and made the training perfect for my needs
Florentin - EXPERIS
Course - Search and Analytics with Amazon OpenSearch
Knowledge, substantive content, contact with others, willingness to help even with trivial problems, ability to interest listeners.
Michał
Course - Python for Excel
Machine Translated
The exercises and the practical part was good and reflected the topic we had learnt during the lecture before the task.
Justyna Legierska - RedCompass
Course - BPMN 2.0 Fundamentals and Workshop
From learning, being able to put the proposed exercises into practice.
Samanta - Imdepa Rolamentos
Machine Translated
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
Course - Design Patterns
The DevOps tool
Jeremie Mandap - Mizuho Bank Ltd - Manila Branch
Course - Agile Business Analysis
All the examples used and the lecturing style was on point even for a begginer i was able to understand and the training was so patient and always willing to go extra mile when in need of assistance.
Mathipa Chepape - Vodacom
Course - Big Data Business Intelligence for Telecom and Communication Service Providers
I learned a lot - not only in theoretical knowledge but I also applied that knowledge during the training and therefore I really understood what process mining is and how it works. Thanks a lot!
Julia Dörre - Techniker Krankenkasse
Course - Process Mining
Using on screen capture to drop in and out of other ppls sessions to help super quick. nice to have.
Mykhailo Charalambij - Federal Geospatial Platform, Natural Resources Canada
Course - D3.js for Data Visualization
Good exercises
Hui Yi Neo - Titansoft Pte Ltd
Course - Object Oriented Design using Design Patterns
el conocimiento y los ejercicios.
JESUS RUBEN GARCIA SOLIS - Gobierno del Estado de Durango
Course - Gestión Eficiente de los Requisitos Mediante Métodos Àgiles y Modelado Àgil de UML
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
Course - Data Analysis with Hive/HiveQL
I was benefit from the trainers patience and level of communication.
- Allianz Cornhill Information Services Private Limited
Course - Understanding Business Process Modeling with BPMN 2.0
Focusing on applying the course on my real work.
shahla - ECRA
Course - Introduction to Business Analysis: Defining Successful Projects
I really was benefit from the real life practical examples.
Wioleta
Course - Data and Analytics - from the ground up
I enjoyed all of Day 1.
Peter
Course - Contemporary Development Principles and Practices
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