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 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 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 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.

**Unleashing the full potential of data and AI requires a paradigm shift in the algorithms and tools used to analyze data and build models towards more interactive systems with highly collaborative and visual interfaces. Ideally, data scientists and domain experts should be able to closely work together and make discoveries together by directly manipulating, analyzing and visualizing data on the spot as a team, instead of having week-long forth-and-back interactions between them.** **Current visualization and workflow tools are ill-suited for this purpose. They were not designed to be interactive nor to support teams to actually work together rather than just share final results. Similarly, most machine learning algorithms are not able to provide initial answers at "human speed" (i.e., seconds), nor are existing methods sufficient to convey the impact of the various risk factors, such as multi-hypothesis problems. Finally, most visual data exploration tools still fail when used over large datasets or require horrendous loading times before any real-work can begin.** **Join us on August 9th from 5-6 PM EST, to learn how Northstar, a novel system developed for Interactive Data Exploration, required us to completely rethink the entire analytics stack.** **Agenda:****5:00 - 5:15 PM: NYC Data Science Academy Introduction****5:15 - 5:50 PM: Northstar (A Novel System) Presentation****5:50 - 6:00 PM: Q&A** **Bio:****Tim Kraska is an Associate Professor of Electrical Engineering and Computer Science in MIT's Computer Science and Artificial Intelligence Laboratory, co-director of the Data System and AI Lab at MIT ([email protected]), and co-founder of Einblick Analytics. Currently, his research focuses on building systems for machine learning, and using machine learning for systems to build instance-optimized systems. Tim is most known for developing techniques to make Data Science more interactive and collaborative, and creating the first Learned Index structure and Learned Query Optimizer.** **About NYC Data Science Academy** **NYC Data Science Academy provides data science training programs and courses that prepare students to use data science tools and apply them to real-world situations.** **NYC Data Science Academy teaches data science, trains companies and their employees to better profit from data, excels at big data project consulting, and connects trained Data Scientists to our industry. We have graduated over 5000 students at this point!** **Join our Interactive Distance / In-Person Learning Bootcamp, and get ready for the next step in your data science career! ([https://nycdatascience.edu/data-science-bootcamp/](https://nycdatascience.edu/data-science-bootcamp/))**

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 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 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 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.