Data Science

at General Assembly - Old Fourth Ward

(2258)
Course Details
Price:
$3,950 8 seats
Start Date:

Tue, Dec 03, 6:30pm - Feb 18, 9:30pm (20 sessions)

Location:
Old Fourth Ward
675 Ponce De Leon Ave NE 2nd Fl
At Ponce City Market NE
Atlanta, Georgia 30308
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Important:
A computer will not be provided
No classes on Dec 24, Dec 26, Dec 31
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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 data course.  

Skills & Tools: Use Python to mine datasets and predict patterns.

Production Standard: Build statistical models—regression and clarification—that generate usable information from raw data.

The Big Picture: Master the basics of machine learning and harness the power of data to forecast what's next.

In this 11 week course, students learn to build robust predictive models, test their validity, and clearly communicate resulting insights.

Unit 1: Research Design and Exploratory Data Analysis

What 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
Research Design and Pandas 
  • Define a problem and identify appropriate data sets using the data science workflow 
  • Walkthrough the data science workflow using a case study in the Pandas library 
  • Import, format and clean data using the Pandas Library
Statistics Fundamental I 
  • Use NumPy and Pandas libraries to analyze datasets using basic summary statistics: mean, median, mode, max, min, quartile, inter-quartile, range, variance, standard deviation and correlation 
  • Create data visualization – scatter plots, scatter matrix, line graph, box blots, and histograms – to discern characteristics and trends in a dataset 
  • Identify a normal distribution within a dataset using summary statistics and visualization
Statistics Fundamental II 
  • Explain the difference between causation vs. correlation 
  • Test a hypothesis within a sample case study 
  • Validate your findings using statistical analysis (p-values, confidence intervals)
Instructor Choice 
  • Focus on a topic selected by the instructor/class in order to provide deeper insight into exploratory data analysis
Unit 2: Foundations of Data Modeling

Introduction to 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
Introduction to 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
Introduction to 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
Communicate Results from Logistic Regression 
  • Explain the tradeoff between the precision and recall of a model and articulate the cost of false positives vs. false negatives. 
  • Identify the components of a concise, convincing report and how they relate to specific audiences ⁄ stakeholders 
  • Describe the difference between visualization for presentations vs. exploratory data analysis
Flexible Class Session 
  • Focus on a topic selected by the instructor ⁄ class in order to provide deeper insight into data modeling
Unit 3: Data Science in the Real World

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
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
Dimensionality Reduction 
  • Explain how to perform a dimensional reduction using topic models 
  • Demonstrate how to refine data using latent dirichlet allocation (LDA) 
  • Extract information from a sample text dataset
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
Creating Models with Time Series Data 
  • Decompose time series data into trend and residual components 
  • Validate and cross-validate data from different data sets 
  • Use the ARIMA model to forecast and detect trends in time series data
The Value of Databases 
  • Describe the use cases for different types of databases 
  • Explain differences between relational databases and document-based databases 
  • Write simple select queries to pull data from a database and use within Pandas
Moving Forward with your Data Science Career 
  • Specify common models used within different industries 
  • Identify the use cases for common models 
  • Discuss next steps and additional resources for data science learning
Flexible Class Session 
  • Focus on a topic selected by the instructor⁄class in order to provide deeper insight into data science in the real world
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 (1)
Start Date Time Teacher # Sessions Price
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, 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

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General Assembly

General Assembly is a pioneer in education and career transformation, specializing in today’s most in-demand skills. The leading source for training, staffing, and career transitions, we foster a flourishing community of professionals pursuing careers they love.

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