Data science TRAINING

Our data science courses start with the fundamentals of developing complex machine learning algorithms with Python. We will be implementing more advanced data science courses over time. As of now, these courses are catered to companies seeking to send their developers to a Bootcamp, or have an instructor come onsite for intensive hands-on-training.


R Language Essentials

This hands-on training course teaches technology professionals and data analysts the fundamentals of R programming. Lecture and lab sessions collaborate to cover importing and manipulating various formats of data, data mining techniques, performing predictive analysis and data visualization using R Commander and Deducer.

Upon completion of the course, attendees will be capable of employing data importing techniques, understanding decision trees, random forests, association rule mining, sentiment analysis, and machine learning techniques. Students will learn to manipulate data with functions like grepl(), sub(), and apply(); to apply data visualization for complex plots, implement linear and logistic regression and understanding Anova; to apply predictive analytics and implement R analytics to create business insights.

This is an instructor-led training (ILT) class and is available for onsite and online delivery.

Practical Machine Learning with Python

Machine learning is a type of artificial intelligence wherein computer programs learn new capabilities when exposed to data. This 3 day instructor led training course teaches the basics of machine learning with practical hands on labs using Python and various support libraries.

Day one introduces the foundational concepts of data science and machine learning. Hands on labs progressively build a basic collection of tools and experiments reinforcing the concepts covered in lecture. Attendees will learn how to create a basic Python development environment for machine learning while producing several basic but useful and instructive programs. Basic probability, statistics and basic data curation skills are developed throughout.

Days two and three build on the foundational skills imparted in day one, introducing formal classification of the most common machine learning algorithms and their purposes. Modules and labs give attendees experience using the most common algorithms and a chance to create real solutions, such as fraud detection and recommendation engines.

Upon completion attendees will have a broad but practical understanding of machine learning and a base from which to pursue real applications and further study.

This is an instructor-led training (ILT) class and is available for onsite and online delivery.

Advanced Python Programming

This Python training course continues from where RX-M's Python Essentials course leaves off, covering some topics in more detail, and adding many new ones, with a focus on enterprise development. This is a hands-on programming class; all concepts are reinforced by informal practice during the lecture, followed by lab exercises. Many labs build on earlier labs, which help students retain the earlier material.

All attendees will learn to use Python to leverage OS services, create modules, create and run unit tests, define classes, interact with network services, query databases and process XML data.

This is an instructor-led training (ILT) class and is available for onsite and online delivery.

Python Essentials

The Python language is popular because its use enhances program correctness and increases programmer efficiency. Because of its clear and elegant syntax, dynamic typing, automatic memory management, and straightforward module architecture, Python is simple to learn and fun to use. Its code is easy to read, write, extend and modify. This lab‐based Python training course offers proficiency in the core concepts of Python, and the skills and knowledge for building applications using any of the tens of thousands of task‐specific Python libraries.

This is an instructor-led training (ILT) class and is available for onsite and online delivery.


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