Data Science

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