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NYC Data Science Academy courses & bootcamps in Data Engineering, Data Science, R, Python, and Machine Learning

Upcoming training from NYC Data Science Academy: 6-Week Intensive Data Engineering Bootcamp, 12-Week Data Science Bootcamp, courses in R, Python, Data Science and Machine Learning, and more.

NYC Data Science Academy Exclusive Offering from NYC Data Science Academy!

6-Week Intensive Data Engineering Bootcamp (first and only Data Engineering program in East Coast)

Nycdsa Data Engineering Bootcamp

Website: nycdatascience.com/data-engineering-bootcamp/
Fall: Aug 24 - Oct 2, 2015 (Application deadline: Aug 21, 2015)
Winter: Nov 2 - Dec 18, 2015 (Application deadline: Oct 25, 2015)

Schedule: Monday through Friday, from 9:30am to 6:30pm

Students will become adept at using the major data engineering tools, including Hadoop and Spark. Starting with the basic tools of Python and SQL, we will explore the structure and components of the Hadoop ecosystem, with particular emphasis on analyzing large volumes of data. Student work will include in-class exercises, technical presentations, an individualized class project, and interview training. This course is hands-on; students should anticipate doing extensive computer work, including significant programming in Python.

The lead instructor used to work at UIUC as tenured professor and worked in Google as Engineer before coming to teaching at NYC Data Science Academy.

12-Week, Full-Time Data Science Bootcamp

NYC DSA Data Science Bootcamp

Website: nycdatascience.com/data-science-bootcamp/
Fall: Sep 21 - Dec 11, 2015 (Application deadline: Aug 21, 2015)
Spring: Feb 1 - Apr 22, 2016 (Application deadline: Jan 1, 2016)

In this program, all the students will learn beginner and intermediate levels of Data Science with R, Python & Hadoop as well as the most popular and useful R packages like Shiny, Knitr, rCharts and more. Through the program, students expected to finish several projects (such as Python web scrapping, R Shiny Apps, Machine Learning for real world problem or Kaggle competition, Hadoop & Spark, etc) and build their portfolio. Check out our students' work from blog including Python web scrapping, R Shiny applications and more.

Students will have interview and coding prep and get access to our online recruiting platform and our wide network within the data science community.

Students and Employers Networking Events

NYC DSA Hiring Partners

RSVP: https://www.eventbrite.com/e/students-and-employers-networking-event-tickets-17879807987

Date: August 11th, 6-8 pm

Here at NYC Data Science Academy, we train highly qualified, individuals (mostly PhDs and Master's) to become data scientists in a full-time, project-intensive, 12-week bootcamp. Rigorous training of stellar candidates leads to a highly talented pool of candidates for your data science needs.

Our summer cohort is in the final month of classes and the students are excited to meet people working (and hiring) in the industry. We have partnered with diverse companies from small startups to large corporations to help place our students in positions just right for them. At the August 11th event you'll have the chance to meet our graduates in person and you could find the candidate that best meets your team's needs. It is also a great opportunity to meet your peers in the data science community. Please RSVP at Eventbrite.

Part time Data Science Courses

Nycdsa Part Time Courses

Big Data with Hadoop and Spark (Weekday Night Classes)

RSVP: nycdatascience.com/courses/big-data-with-hadoop-and-spark/
Dates: August 11, 13, 18, 21, 25, 27, September 1, 3, 8, 10, 15, 17 (Tuesday and Thursday Nights), 2015
Schedule: 7:00 pm - 9:00 pm

An intensive, hands-on introduction to the Hadoop ecosystem of Big Data technologies. The emphasis in this course is on learning several of the major components of Apache Hadoop - HDFS, MapReduce, Hive, Pig, Streaming - by doing exercises of increasing complexity. Programming will be done in Python. Students are expected to be familiar with using an operating system from the command line; knowledge of Python is helpful; the material in Learn Python the Hard Way is sufficient background knowledge. The course format is mixed lecture/lab. Students will need to bring their own laptops to connect to our servers; instructions will be provided ahead of time as to how to install any required software.

It is suitable for people who are in Data Scientist position and want to level up their skills or people who are Software Engineer or Database Engineers who want to get into Big Data Domain. Classes are recored if you can't make all of them.

Intensive Data Science Toolkit Course (Linux, Git, SQL) (Weekday Night Classes)
RSVP: www.eventbrite.com/e/intensive-data-science-toolkitlinux-git-sql-course-tickets-17990203182
Date: Aug 10, 12, 17, 19, 24, 26 (Mon and Wed)
Hours: 6:30-9:30pm

This class is about the basic tools to work in a Linux setting. It includes:
  • Common shell command to achieve daily tasks, include file system, job management, standard IO and pipe.
  • Typical usage of git, a version control system, and online version github
  • In-depth usage of vi and markdown
  • SQL database access.

Data Science with Python: Data Analysis and Visualization (5 Sunday Classes)
RSVP: nycdatascience.com/courses/data-science-with-python-data-analysis/
Date: Sept 20, 27, Oct 4, 11, 18
Time: 1:00pm - 5:00pm

This five week course is an introduction to data analysis with the Python programming language, and is aimed at beginners. We introduce how to work with different data structure in Python. We covered the most popular modules, including Numpy, Scipy, Pandas, matplotlib, Seaborn, and ggplot, to do data analytics and visualization. We use ipython notebook to demonstrate the results of codes and change codes interactively during the class. Our past students include people have no programming experience and people have little exposure by taking Python class. Students told us our classes are very engaging, interactive, hands-on and have tons of content.

Data Science with Python: Machine Learning (5 Sunday Classes)
RSVP: nycdatascience.com/courses/data-science-with-python-machine-learning/
Date: Sep 20, 27, Oct 4, 11, 18
Time: 1:00pm - 5:00pm

This class will introduce you a wide range of machine learning tools in Python. The main focus is on how to use those tools to solve real world problems. Machine learning is a combination of statistical models with programming. After successful completion of this course, students will be able to carry out your experiments with the public available algorithms or develop your own algorithm. Specifically, students will be fluent with popular machine learning techniques with the scikit-learn module, be aware of other available machine learning module, explain and adopt the machine learning algorithm.

Data Science with R: Data Analysis and Visualization (Five Sat Classes)
RSVP: nycdatascience.com/courses/data-science-with-r-data-analysis/
Date: Sep 19, 26, Oct 3, 10, 17
Time: 10:00am - 5:00pm

Each class is 35 hours of classroom guidance with an optional three week-long showcase project of students' own choices and optional presentation of their projects. This intensive class will introduce you to the wonderful world of R and provide you with an excellent understanding of the language that leaves you with a firm foundation to build upon.From the rudimentary building blocks of programming basics, to data manipulation and use of advanced drawing packages.

After successful completion of this course, students will be able to:
  • Write R script for data analysis
  • Use dplyr for advanced data processing
  • Perform descriptive data analysis
  • Apply R ggplot2 package to make advanced visualization

Data Science with R: Machine Learning (Five Sat Classes)
RSVP: nycdatascience.com/courses/data-science-with-r-machine-learning/
Date: Sep 19, 26, Oct 3, 10, 17
Time: 10:00am - 5:00pm

This class is about the predictive modeling using the R programming language. The goal is to understand the general predictive modeling process and how it can be implemented in R.

A rich set of important models (e.g. tree-based models, support vector machines) will be described in an intuitive manner to illustrate the process of training and evaluating models. We cover a variety of topics, such as: Linear models, Logistic regression, Subset selection, Shrinkage methods, Dimension reduction methods, KNN and Naive Bayes models, Random Forest models, GBM models, Support Vector Machines,Maximal margin classifiers, Support vector classifiers, Association Rule, Market Basket Analysis, K-means clustering, Hierarchical clustering and etc.