Data Science

at General Assembly - Penn Quarter

(2265)
Course Details
Price:
$3,950 8 seats
Start Date:

Tue, Dec 03, 6:30pm - Feb 20, 9:30pm (21 sessions)

Next start dates (1)

Location:
Penn Quarter
509 7th St NW 3rd Fl
At E St NW
Washington, District of Columbia 20004
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Important:
A computer will not be provided
No class on: 12/24, 12/26, & 12/31/2019
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Description
Class Level: All levels
Age Requirements: 18 and older
Average Class Size: 20

What you'll learn in this data science course:

This is a part time course.  

In this course, students learn to build robust predictive models, test their validity, and clearly communicate resulting insights.

Course Outline

Unit 1: Programming BasicsWhat is Data Science
  • Describe course syllabus and establish the classroom environment
  • Answer the questions: "What is Data Science? What roles exist in Data Science?"
  • Define the workflow, tools and approaches data scientists use to analyze data
Your Development Environment
  • Navigate through directories using the command line
  • Use git and GitHub to share repositories
Python Foundations
  • Conduct arithmetic and string operations in Python
  • Assign variables
  • Implement loops and conditional statements
  • Use Python to clean and edit datasets
Unit 2: Research Design and Exploratory Data Analysis

Exploratory Data Analysis
  • Use DataFrames and Series to read data
  • Rename, remove, combine, select, and join data
  • Identify and handle null and missing values
Experiments and Hypothesis Testing
  • Determine causality and sampling bias
  • Test a hypothesis using a sample case study
  • Validate your findings using statistical analysis (p-values, confidence intervals)
Data Visualization in Python
  • Define key principles of data visualization
  • Create line plots, bar plots, histograms and box plots using Seaborn and Matplotlib
Statistics in Python
  • Use NumPy and Pandas libraries to analyze datasets using basic summary statistics
  • Create data visualization – scatter plots, scatter matrix, line graph, box plots, and histograms – to discern characteristics and trends in a dataset
  • Identify a normal distribution within a dataset using summary statistics and visualization
Unit 3: Foundations of Data Modeling

Linear Regression
  • Define data modeling and linear regression
  • Differentiate between categorical and continuous variables
  • Build a linear regression model using a dataset that meets the linearity assumption using the scikit-learn library
Evaluating Model Fit
  • Define regularization, bias, and errors metrics
  • Evaluate model fit by using loss functions including mean absolute error, mean squared error, root mean squared error
  • Select regression methods based on fit and complexity
KNN and Classification
  • Define a classification model
  • Build a K–Nearest Neighbors using the scikit–learn library
  • Evaluate and tune model by using metrics such as classification accuracy⁄error
Logistic Regression
  • Build a Logistic regression classification model using the scikit learn library
  • Describe the sigmoid function, odds, and odds ratios and how they relate to logistic regression
  • Evaluate a model using metrics such as classification accuracy ⁄ error, confusion matrix, ROC ⁄ AOC curves, and loss functions
Unit 4: Machine Learning

Decision Trees and Random Forest
  • Describe the difference between classification and regression trees and how to interpret these models
  • Explain and communicate the tradeoffs of decision trees vs regression models
  • Build decision trees and random forests using the scikit-learn library
Working with API Data
  • Access public APIs and get information back
  • Read and write data in JSON
  • Use the requests library
Natural Language Processing
  • Demonstrate how to tokenize natural language text using NLTK
  • Categorize and tag unstructured text data
  • Explain how to build a text classification model using NLTK
Working with Time Series Data
  • Explain why time series data is different than other data and how to account for it
  • Create rolling means and plot time series data using the Pandas library
  • Perform autocorrelation on time series data
Final Presentations
  • Present final presentation to peers, instructor, and guest panelists who will identify strengths and areas for improvement
School Notes:
For students enrolling in 12 week part time and immersive classes, it is not recommended that you book more than one class simultaneously.

Still have questions? Ask the community.

Refund Policy
If you can't make it to a class/workshop, please email us at [email protected] at least 7 days before the scheduled event date. No refunds will be given after this timeframe.

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Start Dates (2)
Start Date Time Teacher # Sessions Price
6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan 21 $3,950
This course consists of multiple sessions, view schedule for sessions.
Thu, Dec 05 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Tue, Dec 10 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Thu, Dec 12 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Tue, Dec 17 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Thu, Dec 19 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Thu, Jan 02 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Tue, Jan 07 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Thu, Jan 09 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Tue, Jan 14 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Thu, Jan 16 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Tue, Jan 21 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Thu, Jan 23 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Tue, Jan 28 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Thu, Jan 30 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Tue, Feb 04 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Thu, Feb 06 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Tue, Feb 11 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Thu, Feb 13 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Tue, Feb 18 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Thu, Feb 20 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan 20 $3,950
This course consists of multiple sessions, view schedule for sessions.
Thu, Jan 30 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Tue, Feb 04 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Thu, Feb 06 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Tue, Feb 11 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Thu, Feb 13 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Tue, Feb 18 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Thu, Feb 20 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Tue, Feb 25 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Thu, Feb 27 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Tue, Mar 03 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Thu, Mar 05 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Tue, Mar 10 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Thu, Mar 12 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Tue, Mar 17 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Thu, Mar 19 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Tue, Mar 24 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Thu, Mar 26 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Tue, Mar 31 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan
Thu, Apr 02 6:30pm - 9:30pm A. Szwec, G. Gandenberger, A. Worsley, W. Kiang Yeo, K. Coyle & Sri Kanajan

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