Data
Science

11-Week Technology Course

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We Teach Core Skills

Math and programming skills

Applying your math and programming skills to make meaning out of large data sets

Data manipulation tools

Learning how to analyze and manipulate data with Python

Learn to make predictions with modeling

Learning how to make predictions about data using fundamental modeling techniques that will help you make better informed business decisions

My team at Amazon couldn't have built its recommendation system without the foundational data mining and machine learning skills taught in this course. When contributing to the curriculum, I was careful to balance the theory with the real-world challenges of applying it to big data.

Frank Kane
Former Senior Manager, Amazon.com

Frank Kane, Amazon.com

We Embrace The Details

Unit 1: The Basics

Introduction to Data Exploration

  • Describe the data mining workflow and the key traits of a successful data scientist.
  • Extract, format, and preprocess data using UNIX command-line tools.
  • Explore & visualize data.

Introduction to Machine Learning

  • Explain the concepts and applications of supervised & unsupervised learning techniques.
  • Describe categorical and continuous feature spaces, including examples and techniques for each.
  • Discuss the purpose of machine learning and the interpretation of predictive modeling results.

Unit 2: Fundamental Modeling Techniques

K-Nearest Neighbors Classification

  • Describe the setting and goal of a classification task.
  • Minimize prediction error using training & test sets, optimize predictive performance using cross-validation.
  • Understand the kNN classification algorithm, its intuition and implementation.
  • Implement the "hello world" of machine learning (kNN classification of iris dataset).

Naive Bayes Classification

  • Outline the basic principles of probability, including conditional probability and Bayes’ theorem.
  • Describe inference in the Bayesian setting, including the prior and posterior distributions and the likelihood function.
  • Understand the naive Bayes classifier and its assumptions.
  • Implement a spam filter using the naive Bayes technique.

Regression & Regularization

  • Explain the concepts of regression models, including their assumptions and applications.
  • Discuss the motivation for regularization techniques and their use.
  • Implement a regularized fit.

Logistic Regression

  • Describe the applications of logistic regression to classification problems and probability estimation.
  • Introduce the concepts underlying logistic regression, including its relation to other regression models.
  • Predict the probability of a user action on a website using logistic regression.

K-Means Clustering with R

  • Explain the purpose of exploratory data analysis, its applications in continuous and categorical feature spaces, and the interpretation and use of clustering results.
  • Discuss the importance of the distance function in cluster formation, as well as the importance of scale normalization.
  • Implement a k-means clustering algorithm.

Unit 3: Further Modeling Techniques

Ensemble Techniques

  • Describe general ensemble techniques such as bagging and boosting.
  • Build an enhanced classification algorithm using AdaBoost.

Decision Trees & Random Forests

  • Describe the use and construction of decision trees for classification tasks.
  • Create a random forest model for ensemble classification.

Dimensionality Reduction

  • Explain the practical and conceptual difficulties in working with very high-dimensional data.
  • Understand the application and use of dimensionality reduction techniques.
  • Draw inferences from high-dimensional datasets using principal components analysis.

Recommendation Systems

  • Explain the use of recommendation systems, and discuss several familiar examples.
  • Understand the underlying concepts, including collaborative & content-based filtering.
  • Implement a recommendation system.

Unit 4: Other Tools

Database Technologies

  • Introduce concepts and use of relational databases, alternative database technologies such as NoSQL, and popular examples of each.

Network Analysis

  • Describe the use of graphs and graph theory to analyze problems in network analysis.
  • Explore network visualization.

Map-Reduce

  • Describe the concepts of parallel computing and applications to problems in big data.
  • Introduce the map-reduce framework.
  • Implement and explore examples of map-reduce tasks.
I learned more from the hands-on methods at General Assembly than I did during my entire three years in law school. GA's courses allow students to focus on the ideas that are most interesting to them and that will help propel their careers forward.

Whitney Meers, Digital Content Specialist

Students working at laptops

Upcoming in

28 April –
09 July

Mondays & Wednesdays
7-10pm
Tuition: $28,000 HKD
(Payment plans available)

Instructor

Mart van de Ven
Data Architect, Technologist

For over a decade Mart has used web technologies to build services with a purpose. Following his Masters in Linguistics and Information Design, he developed ontologies for SAP, built social media apps with Onoko, and most recently was a Data Architect at Demand Analytics. He teaches technologies as he does linguistics: with an orientation towards uncovering the implicit structures and contexts, surpassing the technicalities, and ultimately communicating a greater purpose, above mere technical skills. He is an advocate for open source, social coding and web standards, especially given how they enrich and accelerate your web-dev learning process.

08 May –
05 August

Tuesdays & Thursdays
6:30 - 9:30pm
Tuition: $4,000 USD
(Payment plans available)

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02 June –
13 August

Mondays & Wednesdays
7-10 pm
Tuition: $4,000 USD
(Payment plans available)

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10 June –
14 August

Tuesdays & Thursdays
7-10pm
Tuition: $4,000 USD
(Payment plans available)

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21 July –
01 October

Mondays & Wednesdays
6:30-9:30pm
Tuition: $4,000 USD
(Payment plans available)

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Be the first to know when other details are announced for this session.

25 August –
12 November

Mondays & Wednesdays
7-10pm
Tuition: $28,000 HKD
(Payment plans available)

Instructor

Mart van de Ven
Data Architect, Technologist

For over a decade Mart has used web technologies to build services with a purpose. Following his Masters in Linguistics and Information Design, he developed ontologies for SAP, built social media apps with Onoko, and most recently was a Data Architect at Demand Analytics. He teaches technologies as he does linguistics: with an orientation towards uncovering the implicit structures and contexts, surpassing the technicalities, and ultimately communicating a greater purpose, above mere technical skills. He is an advocate for open source, social coding and web standards, especially given how they enrich and accelerate your web-dev learning process.

17 November –
11 February

Mondays & Wednesdays
7-10pm
Tuition: $28,000 HKD
(Payment plans available)

Instructor

Mart van de Ven
Data Architect, Technologist

For over a decade Mart has used web technologies to build services with a purpose. Following his Masters in Linguistics and Information Design, he developed ontologies for SAP, built social media apps with Onoko, and most recently was a Data Architect at Demand Analytics. He teaches technologies as he does linguistics: with an orientation towards uncovering the implicit structures and contexts, surpassing the technicalities, and ultimately communicating a greater purpose, above mere technical skills. He is an advocate for open source, social coding and web standards, especially given how they enrich and accelerate your web-dev learning process.

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We don’t currently have any sessions scheduled in San Francisco. Request more info to be the first to know when we put something on the calendar!

We’re holding an info session on Tuesday, April 22 at 7:00pm

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GA New York City (West)
10 East 21st Street, 4th Floor
New York, NY 10010

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GA Los Angeles
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Santa Monica, CA 90401

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GA Boston (WeWork)
51 Melcher Street
Boston, MA 02210

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GA San Francisco - Lofts
580 Howard St.
San Francisco, CA 94105

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GA Washington D.C. (1776 Penthouse)
1133 15th Street NW, The Penthouse
Washington, DC 20005

We’re holding an info session on Monday, May 19 at 6:30pm

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GA Boston (WeWork)
51 Melcher Street
Boston, MA 02210

We’re holding an info session on Tuesday, July 1 at 7:00pm

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GA Washington D.C. (1776 Penthouse)
1133 15th Street NW, The Penthouse
Washington, DC 20005

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Put Your Education to Work

At General Assembly, we not only help you build new skills, we aim to provide chances to put them to use. Registration in a course includes access to GA Studio, which assists interested students in creating additional portfolio pieces, preparing for the job search process, and finding new career opportunities.

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