VA, Stafford - Quantico Corporate

800 Corporate Drive, Suite 301, Stafford, united states, 22554

The venue is located between interstate 95 and the Jefferson Davis Highway, in the vicinity of the Courtyard by Mario...

VA, Fredericksburg - Central Park Corporate Center

1320 Central Park Blvd., Suite 200, Fredericksburg, united states, 22401

The venue is located behind a complex of commercial buildings with the Bank of America just on the corner before the turn ...

VA, Richmond - Two Paragon Place

6802 Paragon Place Suite 410, Richmond, united states, 23230

The venue is located in bustling Richmond with Hampton Inn, Embassy Suites and Westin Hotel less than a mile away.

VA, Reston - Sunrise Valley

12020 Sunrise Valley Dr #100, Reston, united states, 20191
The venue is located just behind the NCRA and Reston Plaza Cafe building and just next door to the United Healthcare building...

VA, Reston - Reston Town Center I

11921 Freedom Dr #550, Reston, united states, 20190
The venue is located in the Reston Town Center, near Chico's and the Artinsights Gallery of Film and Contemporary Art.

VA, Richmond - Sun Trust Center Downtown

919 E Main St, Richmond , united states, 23219
The venue is located in the Sun Trust Center on the crossing of E Main Street and S to N 10th Street just opposite of 7 Eleve...

Subcategories (4)

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Anaconda
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RAPIDS
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Modin
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Kaggle

Explore Our Courses

Kaggle

14 Hours
This instructor-led, live training in Virginia (online or onsite) is aimed at data scientists and developers who wish to learn and build their careers in Data Science using Kaggle. By the end of this training, participants will be able to:
  • Learn about data science and machine learning.
  • Explore data analytics.
  • Learn about Kaggle and how it works.
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MATLAB Fundamentals, Data Science & Report Generation

35 Hours
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.
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Python in Data Science

35 Hours
The training course will help the participants prepare for Web Application Development using Python Programming with Data Analytics. Such data visualization is a great tool for Top Management in decision making.
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A Practical Introduction to Data Science

35 Hours
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.
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Python Programming for Finance

35 Hours
Python is a programming language that has gained huge popularity in the financial industry. Adopted by the largest investment banks and hedge funds, it is being used to build a wide range of financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to use Python 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 Python programming language
  • Download, install and maintain the best development tools for creating financial applications in Python
  • Select and utilize the most suitable Python packages and programming 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 a Python 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.
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Introduction to Data Science and AI using Python

35 Hours
This is a 5 day introduction to Data Science and Artificial Intelligence (AI). The course is delivered with examples and exercises using Python 
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F# for Data Science

21 Hours
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
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Introduction to Data Science

35 Hours
This instructor-led, live training (online or onsite) is aimed at professionals who wish to start a career in Data Science. By the end of this training, participants will be able to:
  • Install and configure Python and MySql.
  • Understand what Data Science is and how it can add value to virtually any business. 
  • Learn the fundamentals of coding in Python
  • Learn supervised and unsupervised Machine Learning techniques, and how to implement them and interpret the results.  
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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Big Data Business Intelligence for Telecom and Communication Service Providers

35 Hours
Overview Communications service providers (CSP) are facing pressure to reduce costs and maximize average revenue per user (ARPU), while ensuring an excellent customer experience, but data volumes keep growing. Global mobile data traffic will grow at a compound annual growth rate (CAGR) of 78 percent to 2016, reaching 10.8 exabytes per month. Meanwhile, CSPs are generating large volumes of data, including call detail records (CDR), network data and customer data. Companies that fully exploit this data gain a competitive edge. According to a recent survey by The Economist Intelligence Unit, companies that use data-directed decision-making enjoy a 5-6% boost in productivity. Yet 53% of companies leverage only half of their valuable data, and one-fourth of respondents noted that vast quantities of useful data go untapped. The data volumes are so high that manual analysis is impossible, and most legacy software systems can’t keep up, resulting in valuable data being discarded or ignored. With Big Data & Analytics’ high-speed, scalable big data software, CSPs can mine all their data for better decision making in less time. Different Big Data products and techniques provide an end-to-end software platform for collecting, preparing, analyzing and presenting insights from big data. Application areas include network performance monitoring, fraud detection, customer churn detection and credit risk analysis. Big Data & Analytics products scale to handle terabytes of data but implementation of such tools need new kind of cloud based database system like Hadoop or massive scale parallel computing processor ( KPU etc.) This course work on Big Data BI for Telco covers all the emerging new areas in which CSPs are investing for productivity gain and opening up new business revenue stream. The course will provide a complete 360 degree over view of Big Data BI in Telco so that decision makers and managers can have a very wide and comprehensive overview of possibilities of Big Data BI in Telco for productivity and revenue gain. Course objectives Main objective of the course is to introduce new Big Data business intelligence techniques in 4 sectors of Telecom Business (Marketing/Sales, Network Operation, Financial operation and Customer Relation Management). Students will be introduced to following:
  • Introduction to Big Data-what is 4Vs (volume, velocity, variety and veracity) in Big Data- Generation, extraction and management from Telco perspective
  • How Big Data analytic differs from legacy data analytic
  • In-house justification of Big Data -Telco perspective
  • Introduction to Hadoop Ecosystem- familiarity with all Hadoop tools like Hive, Pig, SPARC –when and how they are used to solve Big Data problem
  • How Big Data is extracted to analyze for analytics tool-how Business Analysis’s can reduce their pain points of collection and analysis of data through integrated Hadoop dashboard approach
  • Basic introduction of Insight analytics, visualization analytics and predictive analytics for Telco
  • Customer Churn analytic and Big Data-how Big Data analytic can reduce customer churn and customer dissatisfaction in Telco-case studies
  • Network failure and service failure analytics from Network meta-data and IPDR
  • Financial analysis-fraud, wastage and ROI estimation from sales and operational data
  • Customer acquisition problem-Target marketing, customer segmentation and cross-sale from sales data
  • Introduction and summary of all Big Data analytic products and where they fit into Telco analytic space
  • Conclusion-how to take step-by-step approach to introduce Big Data Business Intelligence in your organization
Target Audience
  • Network operation, Financial Managers, CRM managers and top IT managers in Telco CIO office.
  • Business Analysts in Telco
  • CFO office managers/analysts
  • Operational managers
  • QA managers
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Data Science for Big Data Analytics

35 Hours
Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy.
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Data Science essential for Marketing/Sales professionals

21 Hours
This course is meant for Marketing Sales Professionals who are intending to get deeper into application of data science in Marketing/ Sales. The course provides
detailed coverage of different data science techniques used for “upsale”, “cross-sale”, market segmentation, branding and CLV. Difference of Marketing and Sales - How is that sales and marketing are different? In very simplewords, sales can be termed as a process which focuses or targets on individuals or small groups. Marketing on the other hand targets a larger group or the general public. Marketing includes research (identifying needs of the customer), development of products (producing innovative products) and promoting the product (through advertisements) and create awareness about the product among the consumers. As such marketing means generating leads or prospects. Once the product is out in the market, it is the task of the sales person to persuade the customer to buy the product. Sales means converting the leads or prospects into purchases and orders, while marketing is aimed at longer terms, sales pertain to shorter goals.
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Jupyter for Data Science Teams

7 Hours
Jupyter is an open-source, web-based interactive IDE and computing environment. This instructor-led, live training introduces the idea of collaborative development in data science and demonstrates how to use Jupyter to track and participate as a team in the "life cycle of a computational idea".  It walks participants through the creation of a sample data science project based on top of the Jupyter ecosystem. By the end of this training, participants will be able to:
  • Install and configure Jupyter, including the creation and integration of a team repository on Git
  • Use Jupyter features such as extensions, interactive widgets, multiuser mode and more to enable project collaboraton
  • Create, share and organize Jupyter Notebooks with team members
  • Choose from Scala, Python, R, to write and execute code against big data systems such as Apache Spark, all through the Jupyter interface
Audience
  • Data science teams
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
  Note
  • The Jupypter Notebook supports over 40 languages including R, Python, Scala, Julia, etc. To customize this course to your language(s) of choice, please contact us to arrange.
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Data Science with KNIME Analytics Platform

21 Hours
KNIME Analytics Platform is a leading open source option for data-driven innovation, helping you discover the potential hidden in your data, mine for fresh insights, or predict new futures. With more than 1000 modules, hundreds of ready-to-run examples, a comprehensive range of integrated tools, and the widest choice of advanced algorithms available, KNIME Analytics Platform is the perfect toolbox for any data scientist and business analyst. This course for KNIME Analytics Platform is an ideal opportunity for beginners, advanced users and KNIME experts to be introduced to KNIME, to learn how to use it more effectively, and how to create clear, comprehensive reports based on KNIME workflows This instructor-led, live training (online or onsite) is aimed at data professionals who wish to use KNIME to solve complex business needs. It is targeted for the audience that doesn't know programming and intends to use cutting edge tools to implement analytics scenarios By the end of this training, participants will be able to:
  • Install and configure KNIME.
  • Build Data Science scenarios
  • Train, test and validate models
  • Implement end to end value chain of data science models
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 or to know more on this program, please contact us to arrange.
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Data Science Implementation Management using KNIME Server

14 Hours
KNIME Server is the enterprise software for team-based collaboration, automation, management, and deployment of data science workflows as analytical applications and services. By the end of this training, participants will be able to:
  • Schedule analytics workflows to run automatically and give yourself more time to focus on data science.
  • Control workflows to automate model management
  • Scale and pin workflow execution with well provisioned, high performance server architecture which is configured to your specifications.
  • Design, edit, and execute workflows on KNIME Server using the remote workflow editor and take advantage of well provisioned hardware in a secure environment.
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 or to know more on this program, please contact us to arrange.
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Presto for Data Science

14 Hours
This instructor-led, live training in Virginia (online or onsite) is aimed at data scientists who wish to query big data sources with Presto. By the end of this training, participants will be able to:
  • Employ Presto key concepts to optimize modern big data systems.
  • Use Presto to run exabyte scale warehouses.
  • Clone data to a proprietary data storage system.
  • Work with existing BI tools such as R and Tableau.
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Qlik Sense for Data Science

14 Hours
This instructor-led, live training in Virginia (online or onsite) is aimed at data analysts and web developers who wish to develop associative models in Qlik Sense. By the end of this training, participants will be able to:
  • Apply Qlik Sense in data science.
  • Use and navigate the Qlik Sense interface.
  • Build a data literate workforce with AI interaction.
  • Create a data-driven enterprise with Qlik Sense.
Read more...

Practical Quantum Computing

10 Hours
Practical Quantum Computing: Live Online Launch your high-tech career This is a 10 hour instructor-led, live online training course. After your immersive training, you will be ready to start work as an entry level quantum computing developer. By the end of this training, participants will be able to:
  • Run and test your quantum programs with the integrated IBM Q
  • Use Qiskit to create, compile, and execute quantum computing programs
  • Working with practical and advanced quantum algorithms such as QAOA 
  • Recast real-world problems into an appropriate quantum computing language
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.
Read more...

Research Methods and Professional Issues– Data science

7 Hours
This classroom based training session will contain presentations and Q&A sessions
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SMACK Stack for Data Science

14 Hours
This instructor-led, live training in Virginia (online or onsite) is aimed at data scientists who wish to use the SMACK stack to build data processing platforms for big data solutions. By the end of this training, participants will be able to:
  • Implement a data pipeline architecture for processing big data.
  • Develop a cluster infrastructure with Apache Mesos and Docker.
  • Analyze data with Spark and Scala.
  • Manage unstructured data with Apache Cassandra.
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Anaconda Ecosystem for Data Scientists

14 Hours
This instructor-led, live training in Virginia (online or onsite) is aimed at data scientists who wish to use the Anaconda ecosystem to capture, manage, and deploy packages and data analysis workflows in a single platform. By the end of this training, participants will be able to:
  • Install and configure Anaconda components and libraries.
  • Understand the core concepts, features, and benefits of Anaconda.
  • Manage packages, environments, and channels using Anaconda Navigator.
  • Use Conda, R, and Python packages for data science and machine learning.
  • Get to know some practical use cases and techniques for managing multiple data environments.
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GPU Data Science with NVIDIA RAPIDS

14 Hours
This instructor-led, live training in Virginia (online or onsite) is aimed at data scientists and developers who wish to use RAPIDS to build GPU-accelerated data pipelines, workflows, and visualizations, applying machine learning algorithms, such as XGBoost, cuML, etc. By the end of this training, participants will be able to:
  • Set up the necessary development environment to build data models with NVIDIA RAPIDS.
  • Understand the features, components, and advantages of RAPIDS.
  • Leverage GPUs to accelerate end-to-end data and analytics pipelines.
  • Implement GPU-accelerated data preparation and ETL with cuDF and Apache Arrow.
  • Learn how to perform machine learning tasks with XGBoost and cuML algorithms.
  • Build data visualizations and execute graph analysis with cuXfilter and cuGraph.
Read more...

Accelerating Python Pandas Workflows with Modin

14 Hours
This instructor-led, live training in Virginia (online or onsite) is aimed at data scientists and developers who wish to use Modin to build and implement parallel computations with Pandas for faster data analysis. By the end of this training, participants will be able to:
  • Set up the necessary environment to start developing Pandas workflows at scale with Modin.
  • Understand the features, architecture, and advantages of Modin.
  • Know the differences between Modin, Dask, and Ray.
  • Perform Pandas operations faster with Modin.
  • Implement the entire Pandas API and functions.
Read more...

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Testimonials (13)

examples and exercises

Kamil
Course - Introduction to Data Science and AI using Python

Machine Translated

The course is very interesting being the main focus nowdays

mohamed taher - FAB banak Egypt
Course - Introduction to Data Science and AI (using Python)

Ahmed was very interactive and didn’t mind answering any kind of questions Well presentation and smooth flow of the course

Mohamed Ghowaiba - FAB banak Egypt
Course - Introduction to Data Science and AI (using Python)

Helpful and good listener .. interactive

Ahmed El Kholy - FAB banak Egypt
Course - Introduction to Data Science and AI (using Python)

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

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

Intensity, Training materials and expertise, Clarity, Excellent communication with Alessandra

Marija Hornis Dmitrovic - Marija Hornis
Course - Data Science for Big Data Analytics

The example and training material were sufficient and made it easy to understand what you are doing.

Teboho Makenete
Course - Data Science for Big Data Analytics

It is great to have the course custom made to the key areas that I have highlighted in the pre-course questionnaire. This really helps to address the questions that I have with the subject matter and to align with my learning goals.

Winnie Chan - Statistics Canada
Course - Jupyter for Data Science Teams

Trainer was accommodative. And actually quite encouraging for me to take up the course.

Grace Goh - DBS Bank Ltd
Course - Python in Data Science

Do aprendizado, conseguir colocar em prática os exercícios propostos.

Samanta - Imdepa Rolamentos
Course - Qlik Sense for Data Science

Younes is a great trainer. Always willing to assist, and very patient. I will give him 5 stars. Also, the QLIK sense training was excellent, due to an excellent trainer.

Dietmar Glanninger - BMW
Course - Qlik Sense for Data Science

very interactive...

Richard Langford
Course - SMACK Stack for Data Science
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