9 courses with the subject eCornell CAC, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
eCornell CAC101Constructing Expressions in Python0
Expressions are a core attribute of any Python program. In this course, you will construct expressions and reuse them to manipulate and compute variables in a variety of applications. This reusability enables a "create once, use everywhere" development paradigm which will streamline development of your current and future Python programs. You will develop the knowledge and skills to assign and access variables, combine variables and data in expressions, and leverage Python as a powerful calculator. You'll also use the enhanced capabilities of the IPython environment to do interactive work with Python and to explore your data through new analyses. The knowledge and skills you gain will help you construct Python expressions to streamline the development of your current and future Python data science projects.
eCornell CAC102Writing Custom Python Functions, Classes, and Workflows0
This course introduces you to the different scenarios in which you will utilize built-in Python functions, classes, and data types as opposed to creating your own or using a combination of built-in and custom-built capabilities. You will gain experience working with both built-in and custom-built functions, classes, and data types. Through practice and application of these basic building blocks/tools, you will gain an in-depth understanding of how these aspects of Python interoperate to create useful programs. Constructing Expressions in Python
eCornell CAC103Developing Data Science Applications0
Python is much more than a programming language. In this course, you will leverage the comprehensive Python ecosystem of libraries, frameworks, and tools to develop complex data science applications. Throughout this course, you will practice using the different Python tools appropriate to your dataset. You will leverage library resources for data acquisition and analysis as well as machine learning. Dataframes will be introduced as a means of manipulating structured data tables for advanced analysis. Additionally, you will practice basic routines for data visualization utilizing Jupyter Notebooks. Constructing Expressions in Python, Writing Custom Python Functions, Classes, and Workflows
eCornell CAC104Creating Data Arrays and Tables in Python0
Decision-makers generally do not use raw data to make decisions; they prefer data be summarized in easily understood formats that facilitate efficient decision-making. This course introduces data manipulation and visualization, both critical components of any data science project. This course introduces two commonly used data manipulation tools in the Python ecosystem: NumPy and Pandas. In addition, the Python ecosystem also includes a variety of data plotting packages such as Matplotlib, Seaborn, and Bokeh each of which specialize in particular aspects of data visualization. This course will give you experience integrating NumPy, Pandas, and the plotting packages to create rich, interactive data visualizations that help drive efficient decision-making. Constructing Expressions in Python, Writing Custom Python Functions, Classes, and Workflows Developing Data Science Applications
Most data science projects that use Python will require you to access and integrate different types of data from a variety of external sources. This course will give you experience identifying and integrating data from spreadsheets, text files, websites, and databases. To prepare for downstream analyses, you first need to integrate any external data sources into your Python program. You will utilize existing packages and develop your own code to read data from a variety of sources. You will also practice using Python to prepare disorganized, unstructured, or unwieldy datasets for analysis by other stakeholders. Constructing Expressions in Python, Writing Custom Python Functions, Classes, and Workflows, Developing Data Science Applications, Creating Data Arrays and Tables in Python .
eCornell CAC106Analyzing and Visualizing Data with Python0
In order to be useful within a professional environment, data must be structured in a way that can be understood and applied to real-world scenarios. This course introduces using Python to perform statistical data analysis and create visualizations that uncover patterns in your data. Using the tools and workflows you developed in earlier courses, you will carry out analyses on real-world datasets to become familiar with recognizing and utilizing patterns. Finally, you will form and test hypotheses about your data which will become the foundation upon which data-driven decision-making is built. Constructing Expressions in Python, Writing Custom Python Functions, Classes, and Workflows, Developing Data Science Applications, Creating Data Arrays and Tables in Python, Organizing Data with Python
In this course, you will explore some of the machine learning tools you can use to magnify the analytical power of Python data science programs. You will use the scikit-learn package a Python package developed for machine learning applications to develop predictive machine learning models. You will then practice using these models to discover new relationships and patterns in your data. These capabilities allow you to unlock additional value in your data that will aid in making predictions and, in some cases, creating new data. Constructing Expressions in Python, Writing Custom Python Functions, Classes, and Workflows, Developing Data Science Applications, Creating Data Arrays and Tables in Python, Organizing Data with Python, Analyzing and Visualizing Data with Python
Data drives many real-world endeavors, which means that storing and accessing the data is foundational to success. Relational databases are an industry-standard data storage mechanism for maintaining data integrity while allowing flexible data retrieval. You will begin this course by examining the basic table structures that form a relational database. Using the relational database format, you will define connections between your data fields and determine how those can be expressed. You will then practice normalizing a relational database to ensure data integrity and reduce redundancy. As this course concludes, you will use a relational database system called OmniDB along with structured query language (SQL) to retrieve specific information from the database.
Relational databases are workhorses which form the backbone for much of the information we find at our fingertips on the internet. In this course, you will learn to create and modify databases using OmniDB and structured query language (SQL) to import data, create tables, and modify fields. You will also practice cleaning data to maintain your database and ensure that it provides accurate information. As the course progresses, you will identify questions you want answered and practice translating those questions into SQL. You will also examine different forms of outputting data from a database, including outputting to a program or text file and outputting CSV text. Querying Relational Databases
