In just six months, you will become skilled in the fundamentals of data science, mastering critical concepts using Python. By receiving one-on-one support from a mentor who is an expert in the industry, you will be prepared to accelerate your career as a data scientist.
Pre-Module: 4-Week Python Coding Camp
All participants complete a month long data camp to learn or reinforce foundational Python coding skills. Live office hours and moderated discussion boards offer personalized support.
Module 1:
Practical Applications of Analytics
Learn to implement Python concepts; evaluate how companies leverage data to find opportunities; use a problem-solving framework; create functions and 'while' loops in Python; work with values, tuples, and lists; and explore Markdown in Jupyter Notebook.
Module 2:
Data Structures and Plotting
Identify structured and unstructured data; understand continuous and categorical data; explore data structures; determine what kind of data can be expressed in vectors vs. matrices; format and analyze data; and create simple plot points in Python.
Module 3:
Introduction to Statistics and Probability
To understand confidence intervals and p-values, gain a solid base of knowledge in statistics and probability, including means and distributions; and use Python to create quantile-quantile plots and perform hypothesis tests.
Module 4:
Linear Models - Ordinary Least Squares
Understand how to build and visualize OLS regression models, apply the problem-solving framework to create univariate and multivariate regression models; create user-friendly model outputs; and apply metrics to regression models.
Module 5:
Linear Regression: Interactions and Transformations
Learn transformation of variables into your model; fit variable interactions; perform the Shapiro-Wilks test to assess normality and execute transformations; create multivariate models with interaction variables; evaluate model fit and complexity; share results.
Module 6:
Logistic Regression and Applying GLMs
Explore modeling for binary outcomes; apply the problem-solving framework to create logistic regression models that determine the odds and probability of a given outcome; clearly communicate the output of models; and apply metrics to classification models.
Module 7:
Data Visualization Strategies
To effectively communicate to non-technical stakeholders, create interactive dashboards in Python; employ best practices for creating effective data visualizations; and plot different types of data using Seaborn in Python.
Module 8:
Experimental Design, Causal Research, and Targeting Analysis
Use statistical tests to determine statistical significance while using visualization techniques to fully illustrate the differences between test and control; apply experimental design and targeting analysis; and perform A/B testing in Python.
Module 9:
Machine Learning
Focus on modeling techniques to solve business challenges; apply machine learning techniques, such as K-means clustering, classification, and regression trees, and resampling through k-fold cross-validation; learning within the industry.
Module 10:
Final Project to Your Job-Ready Portfolio
Synthesize all of the concepts, skills, and applications you have learned, develop a project that demonstrates and explains various tools and techniques explored in the course; creating a portfolio presentation to share with prospective employers.
Module 1:
Practical Applications of Analytics
Learn to implement Python concepts; evaluate how companies leverage data to find opportunities; use a problem-solving framework; create functions and 'while' loops in Python; work with values, tuples, and lists; and explore Markdown in Jupyter Notebook.
Module 6:
Logistic Regression and Applying GLMs
Explore modeling for binary outcomes; apply the problem-solving framework to create logistic regression models that determine the odds and probability of a given outcome; clearly communicate the output of models; and apply metrics to classification models.
Module 2:
Data Structures and Plotting
Identify structured and unstructured data; understand continuous and categorical data; explore data structures; determine what kind of data can be expressed in vectors vs. matrices; format and analyze data; and create simple plot points in Python.
Module 7:
Data Visualization Strategies
To effectively communicate to non-technical stakeholders, create interactive dashboards in Python; employ best practices for creating effective data visualizations; and plot different types of data using Seaborn in Python.
Module 3:
Introduction to Statistics and Probability
To understand confidence intervals and p-values, gain a solid base of knowledge in statistics and probability, including means and distributions; and use Python to create quantile-quantile plots and perform hypothesis tests.
Module 8:
Experimental Design, Causal Research, and Targeting Analysis
Use statistical tests to determine statistical significance while using visualization techniques to fully illustrate the differences between test and control; apply experimental design and targeting analysis; and perform A/B testing in Python.
Module 4:
Linear Models - Ordinary Least Squares
Understand how to build and visualize OLS regression models, apply the problem-solving framework to create univariate and multivariate regression models; create user-friendly model outputs; and apply metrics to regression models.
Module 9:
Machine Learning
Focus on modeling techniques to solve business challenges; apply machine learning techniques, such as K-means clustering, classification, and regression trees, and resampling through k-fold cross-validation; learning within the industry.
Module 5:
Linear Regression: Interactions and Transformations
Learn transformation of variables into your model; fit variable interactions; perform the Shapiro-Wilks test to assess normality and execute transformations; create multivariate models with interaction variables; evaluate model fit and complexity; share results.
Module 10:
Final Project to Your Job-Ready Portfolio
Synthesize all of the concepts, skills, and applications you have learned, develop a project that demonstrates and explains various tools and techniques explored in the course; creating a portfolio presentation to share with prospective employers.
For full details of the curriculum, please download the brochure.
