Data Science Program_

What does a Data Scientist do? Learn the types of questions that data scientists answer every day, and how they answer them. Discover how to use the data science pipeline and machine learning methodologies to provide value to businesses and their decision makers. These include statistical and mathematical concepts, Python, Jupyter Notebooks, Linux / MacOS Terminal, SQL, Microsoft Excel & Google Sheets, Tableau, Git, and Hadoop technologies. Prerequisite: Admission to program

Learn how to use applied statistics to solve practical problems. Decide what data to collect and how to collect it. Then, analyze and interpret your data and present your results. Explore the fundamentals of applied statistics in Data Science using Excel, including measures of central tendency, tests of significance, common distributions, and variable independence. Prerequisite: Fundamentals of Data Science

How your data is stored matters. Use MySQL to gather, parse, clean, aggregate, and store data. Learn how to read data from SQL databases and perform basic joins, aggregates, and group-bys. Write tables, export data, and explore database structure and schema. Prerequisite: Applied Statistics

Learn to use Python to make raw data more valuable for analytics through, data wrangling, data analysis, data visualization, and machine learning. Explore how to use packages such as Pandas, NumPy, SciPy, Scikit Learn, and Matplotlib. Prerequisite: SQL

Learn to prepare data with regression algorithms to predict numerical events. You will build on your previous skills from past modules to gather and prepare, parse, package, and analyze data from a relational database. You’ll learn new skills such as indexing, selecting, plotting, and linear regression, as well as how to evaluate the performance of regression models. By the end of this module, you’ll deliver a final report and model predictions from a practical use case.

Learn to predict data classes with classification algorithms. Gather data from multiple source types such as csv and SQL. Develop a classification model to predict categorical events. Explore topics such as grouping, aggregations, computational tools, text data, missing data, DataFrame objects, plotting, data visualization, and how to evaluate classification models. By the end of this module, you’ll deliver a final report and model predictions from a practical use case. Prerequisite: Regression

Use unstructured machine learning and clustering algorithms to find unknown patterns in data. Use clustering algorithms to identify similar groups using Python and Scikit-Learn and explore how they can be used for further analysis and modeling. Prerequisite: Classification

Use structured machine learning to employ forecasting methods and predict events over time.. You will practice with a practical application and raw data to learn how to develop, evaluate, and improve performance of the model. Explore concepts such as time dependency, accounting for seasonality, and how to effectively split data into training and test sets. By the end of the module, you will develop a time series model and deliver a final report, model, and predictions. Prerequisite: Clustering 

Learn how to detect rare or anomalous events.Students will learn methods for detecting rare or anomalous events. You’ll practice building an anomaly detection model using Python. Explore streaming and unstructured data (such as logs), regular expressions, and how to apply data science to cyber security. Prerequisite: Time Series Analysis

Use Natural Language Processing techniques to perform common tasks such as sentiment analysis and topic modeling. Use Python’s NLTK package (or an equivalent) to analyze the sentiment of tweets related to a particular subject. You will also learn how to access data from a public API, such as Twitter. Prerequisite: Anomaly Detection

Learn how to access distributed data from cloud platforms. Work through the data science pipeline — from gathering data through the deployment of a machine learning model. You will apply previously learned machine learning methodologies using technologies such as Spark and Hive. Understand the Hadoop framework and how to access data to develop data products, such as machine learning models. Prerequisite: Natural Language Processing

With a solid understanding of the fundamentals, experience advanced data science topics such as model pipelines, A/B testing in machine learning, graph analysis, recommendation engines, R, deep learning, deployment of production models to the cloud, and NoSQL databases. Learn use cases, key concepts, and resources for diving deeper into these topics. Prerequisite: Distributed Machine Learning

Presenting your findings to colleagues is a crucial part of a Data Scientist’s work. You will learn best practices for storytelling, visualizations, presentations, calls to actions, and more. Explore important factors to adapt and appeal to various types of audiences, such as visual design. Use tools such as Matplotlib and Seaborn packages, Javascripts D3 library, R’s ggplot2 library, and Tableau and deliver a presentation advocating a recommendation based on findings. Prerequisite: Advanced Topics

Data Science is often defined as the intersection of programming, mathematics & statistics, and business or domain expertise. However, most data scientists will switch industries during their career, and the ability to adapt quickly into a new domain is critical to maintaining the data science trifecta. In this module, you will learn frameworks for learning what domain knowledge is most relevant to their work and for quickly acquiring the skills needed to start adding value. Prerequisite: Storytelling with Data

Throughout the program, you’ll learn a variety of skills related to career preparation and professional development. By the time you graduate, you will possess soft skills such as team-building and communication, as well as career development skills such as resume writing, online branding, and interviewing. We also teach best practices in project / time management, ethics, big data architecture, and portfolio development in Kaggle, Data.world, and Github. Prerequisite: Fundamentals of Data Science

Work in a small team to complete a real-world capstone project that uses each skill you’ve learned throughout the course. Prerequisite: Domain Expertise Development