Job Description
It is a 12-week data science bootcamp in New York City that also offers online classes. In these courses, students learn beginner and intermediate levels of Data Science with Python, R, Spark, GitHub, Hadoop, and SQL. Once the learning foundation has been set, students get to work on multiple projects. The programs are known to balance intensive lectures with real world project work. Throughout the course, students work alone as well as in teams in at least four projects that are further showcased to employers during hiring interviews. Students also get to become a part of hiring events, student blogs, meet ups, and filmed presentations.
| Rating Criteria | Ratings | |
| Student to Teacher Ratio | 20:1 | 3 |
| Placement Rate | 93% | 5 |
| Number of years in the business | 7 years | 5 |
| Total Average Rating | 4.33/5 |
How to Apply?
The interested applicants should submit their application as early as possible since the NYC Academy has a competitive admission process due to a high number of enrollmentsagainst limited seats.
Follow these three simple steps to enroll in the Academy:
- Submit an online application designed to help the Admissions team to get brief about your work & educational background. Thereon, complete self-assessment questions to validate your technical experience with various data science tools. It takes not more than 10 to 15 minutes to complete the online application.
- Onceyour application is reviewed, you will get a call by the Admissions team to schedule a video interview. The interview gives a chance to both the parties to get a better understandingof if the bootcamp program is the best fit for your background and career goals or not.
- In the last and find step, you will needto give a technical assessment test. It consists of technical challenges which determine your programming experience&thought process. You will get 48 hours to complete &submit the technical assessment. Then the Admissions Team will review and evaluate your responses & decide if you’re ready to join the bootcamp.
Courses
Data Science Bootcamp
(MySQL, Data Science, Git, R, Data Visualization, Hadoop, Spark, Linux, Data Analytics, SQL, Python, Machine Learning)
Cost – $17,600
This program helps students learn modern data analytic techniques and master the requisite skills like Python and R programming languages like Hadoop while addressing real world data science problems. Students get to work alone and in teams to create at least five projects. Students will also have assistance in job search through interview preparation, resume review, and get opportunities to interview with hiring partners.
Getting in – Ideal applicants should have Masters or Ph.D. degree in Science, Engineering, Technology, or Math or equivalent experience of quantitative programming.
Big Data with Amazon Cloud, Hadoop/Spark and Docker
(Data Science, Hadoop, Spark, Data Structures, Python, Cloud Computing)
Cost – $2,990
It is a 6 week program that provides hands on introduction to Hadoop and Spark ecosystem of Big Data Technologies. The course covers key components of Apache Hadoop: MapReduce with streaming, HDFS, Hive and Spark. The course begins with a review of Python concepts and the format of the course is interactive. Students get to work on AWS (Amazon Web Service) and the instructions on how to connect and obtain an account etc. are provided beforehand.
Data Science with Python: Machine Learning
(Machine Learning, Linear Regression, Cross-Validation, Decision Trees, Unsupervised Learning)
Cost-$1890
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.