Data Science Professional Development Courses

Refine your data science skillsets through our industry informed curriculum and real–world projects. Pick and choose the best class that fits your schedule. Seats are limited!
Filter By :
Clear All
This is a class for computer-literate people with no programming background who wish to learn basic Python programming. The course is aimed at those who want to learn “data wrangling” – manipulating downloaded files to make them amenable to analysis. We concentrate on language basics such as list and string manipulation, control structures, simple data analysis packages, and introduce modules for downloading data from the web.
*This is an in-person/live online class that will be conducted on Weekdays
This class is a comprehensive introduction to data science with Python programming language. This class targets people who have some basic knowledge of programming and want to take it to the next level. It introduces how to work with different data structures in Python and covers the most popular data analytics and visualization modules, including numpy, scipy, pandas, matplotlib, and seaborn. We use Ipython notebook to demonstrate the results of codes and change codes interactively throughout the class.
*This is an in-person/live online class that will be conducted on Sundays
This 20-hour Machine Learning with Python course covers all the basic machine learning methods and Python modules (especially Scikit-Learn) for implementing them. The five sessions cover: simple and multiple Linear regressions; classification methods including logistic regression, discriminant analysis and naive bayes, support vector machines (SVMs) and tree based methods; cross-validation and feature selection; regularization; principal component analysis (PCA) and clustering algorithms. After successfully completing of this course, you will be able to explain the principles of machine learning algorithms and implement these methods to analyze complex datasets and make predictions in Python.
*This is an in-person/live online class that will be conducted on Sundays
This course is a 35-hour program designed to provide a comprehensive introduction to R. You’ll learn how to load, save, and transform data as well as how to write functions, generate graphs, and fit basic statistical models with data. In addition to a theoretical framework in which you will learn the process of data analysis, this course focuses on the practical tools needed in data analysis and visualization. By the end of the course, you will have mastered the essential skills of processing, manipulating and analyzing data of various types, creating advanced visualizations, generating reports, and documenting your codes.
*This is an in-person/live online class that will be conducted on Saturdays
This course is an introduction to ML systems in production that will demonstrate and give students exposure to how real production ML systems operate. Using Python, Docker, Kubernetes, Google Cloud and various open-source tools, students will bring the different components of an ML system to life and setup real, automated infrastructure.
*This is an in-person/live online class that will be conducted on Weekdays
This course demonstrates a practical and intuitive approach to NLP applications through variety of different use-cases. Essentials and practical fundamentals of NLP methods are presented via generic Python packages including but not limited to Regex, NLTK, SpaCy and Huggingface. The high-level foundations followed by hands-on code examples on a notebook environment will be studied touching on different aspects of NLP from conventional statistical text analytics approaches to the state-of-the-art deep/transfer learning models paired with result interpretations, industry challenges, visualizations and a prototype web application.
*This is an in-person/live online class that will be conducted on Weekdays
This 35-hour Machine Learning with R course introduces both the theoretical foundation of machine learning algorithms as well as their practical applications in R. It will introduce you to data mining, performance measures and dimension reduction, regression models, both linear and generalized, KNN and Naïve Bayes models, tree models, and SVMs as well as the Association Rule for analysis. After successfully completing of this course, you will be able to break down the mathematics behind major machine learning algorithms, explain the principles of machine learning algorithms, and implement these methods to solve real-world problems.
$2,990.00
*We do not offer this course at this moment. Please join our waiting list to be notified when it becomes available again.