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Data Science

at General Assembly - Northern Denver

Course Details
Start Date:

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Northern Denver
3858 Walnut St
Btwn N Marion & 40th Streets
Denver, Colorado 80205
Book at Office/Home
A computer will not be provided
No class on Sep 2
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 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.

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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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