Become a Data Scientist

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.

Join us online on Tuesday, June 2 for a live #AMA “Ask Me Anything” session with our instructors, students, and staff at NYC Data Science Academy. Get a chance to have your questions directly answered so you can make an informed decision on how we can prepare you for a successful career in data science. Throughout the session, you will gain a better understanding of the remote/live online delivery of our in-person bootcamp, how we’re supporting our students during these challenging times, and how our current students are making the most out of their bootcamp experience online. Here’s what to expect from the session - Introduction of the school, the bootcamps, and the admissions process- What is remote/live online instruction and how it works- Job placement support during and after the bootcamp- Day in the life of a bootcamp student- AMA with our team and students Sign up today and get all your questions answered during the session.https://www.eventbrite.com/e/ama-ask-me-anything-about-the-remotelive-online-bootcamp-experience-tickets-106932520046

We often tend to forget and underemphasize the role of communication in data science career development. Mastering this skill is essential for career growth in the discipline. Join us on June 3, Wednesday to hear from Alexander Statnikov, Head of Data Science, Machine Learning, and Automation at Square as he makes a case about the importance of excellent communication skills to be a successful data scientist. His presentation will provide examples showing how communication is manifested in day-to-day work of data scientists, and share recipes for excelling in communication and fostering career growth. He will also share his perspective on the evolution of data science industry practice and expanded role of communication in the future. Register for free here: https://www.eventbrite.com/e/growing-as-a-data-scientist-and-the-role-of-communication-tickets-105948737526

Join us online on June 5, Friday to learn new developments in financial risk management and the role that data science play in this field. Data Science and Analytics Consulting Director and Co-founder, Colin Chen, will cover some new developments in financial risk management and the role that data science play within this field. Colin will go over some common gaps between risk control and risk assessment/analytics across all risk types - credit risks, market risks, and operational risks. Then, he will introduce solutions to close these gaps and explain the roles that data science can play in these solutions. Finally, he will do a deep dive on operational risks and illustrate some potential progress data science can make in this field. Colin Chen co-founded Data Science and Analytics Consulting, which focuses on data science projects from financial and media industries. He has over 12 years of experience in financial risk management. He worked at JPMorgan Chase as an Executive Director leading the Operational Risk Modeling group and at Bank of America as a Director of Model Risk Management. He also worked at Wells Fargo and Fannie Mae on credit and market risk models with mortgages, credit cards, auto loans, mortgage servicing rights and hedging. Register for free here: https://www.eventbrite.com/e/data-science-in-the-next-generation-of-risk-management-tickets-105135739828

Join us on June 10, Wed to hear from Naveen Singla, VP, Data Science at Bayer as he highlights domain knowledge importance in data science. Domain knowledge is prominently mentioned as a component of data science. However, compared to its more glamorous counterparts, mathematical modeling and programming expertise, it is quite often under-appreciated. Through this presentation, Naveen Singla, VP of Data Science at Bayer will highlight the importance of domain knowledge and provide some insights on how to exploit it for successful data science. He will also share his thoughts on how data scientists can acquire some necessary skills to proactively incorporate domain knowledge into their work. Register for free here: https://www.eventbrite.com/e/importance-of-domain-knowledge-in-data-science-w-bayer-vp-of-data-science-tickets-106017848238

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 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 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.

This is a 6-week evening program providing a hands-on introduction to the Hadoop and Spark ecosystem of Big Data technologies. The course will cover these key components of Apache Hadoop: HDFS, MapReduce with streaming, Hive, and Spark. Programming will be done in Python. The course will begin with a review of Python concepts needed for our examples. The course format is interactive. Students will need to bring laptops to class. We will do our work on AWS (Amazon Web Services); instructions will be provided ahead of time on how to connect to AWS and obtain an account.