**Get a chance to meet directly with our admissions officers, instructors, and other staff members who can walk you through our Bootcamp curriculum and the admissions process.** **Hear more about the admission process, interview tips, and our Data Science Bootcamps. As well as what topics we will cover. Did you know we offer lifetime career support to our Alumni? Our Career Service team will introduce some of our alumni and share more about their journey, which includes how to prepare for the Bootcamp, what to expect, and what happens after.** **Agenda****2:00 -2:15: Admission Presentation** **2:15 - 2:35: Career Service** **2:35 - 2:55: Alumni Representative** **2:55 - 3:00: QA Session** **About NYC Data Science Academy** **NYC Data Science Academy provides data science training programs and courses that prepare people for employment opportunities for data science professionals across all industries.**  **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 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 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 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.

**Interested in learning Data Science but apprehensive about the statistics and coding requirements? This workshop will be an example of how you can confidently learn fundamental statistics and coding skills needed to understand and practice Data Science. In particular, you will learn the following important concepts:** * **Statistics: A/B testing, hypothesis testing, p-value, permutations, and permutation test.*** **Coding: variables, loops, and functions.** **We start by discussing “What is an A/B test?” and “Why do we use A/B testing?”, followed by several examples of A/B tests in Data Science. Then, we answer the main question of “How is an A/B test done?” with a detailed explanation of the steps involved in the A/B testing process (using didactic animations to illustrate those steps).** **Afterward, we introduce permutations and the Permutation Test as a good method to do the hypothesis testing part of an A/B test. We explain the Permutation Test process in detail, using didactic animations to illustrate the steps involved.** **Throughout this process, we provide intuitive explanations for hypothesis testing and the associated statistical concepts. In particular, we provide a clear graphical/visual interpretation of what is the p-value.** **Finally, we show how to code the learned concepts (from scratch) using both Python and R.** **This is an introductory-level webinar perfect for anyone interested in learning Data Science. You don’t need prior experience or background in coding nor statistics.** **Save your spot today for this free data science webinar.** **About NYC Data Science Academy** **NYC Data Science Academy provides data science training programs and courses that prepare people for employment opportunities for data science professionals across all industries.** **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 Learning Bootcamp, and get ready for the next step in your data science career! ([https://nycdatascience.com/data-science-bootcamp/](https://nycdatascience.com/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.

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.