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

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

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