About James Ingram

Front End Web Developer

I am a self-taught web designer and developer based in the Southern Tier of New York. I am passionate about using technology-based solutions to help solve real world challenges.

I first began learning to code in the mid 1990's. Remember those annoying pages with flashing text and too many animated gifs? I know I do; I created quite a few of them. The good news is that website design has come a long way since then, and so too have my skills.

Recently, I decided to move beyond the hobbyist stage and begin to seriously pursue web design and development. I have educated myself using various online resources, including:

In addition to these resources, I am enrolled in the iSchool at Syracuse University where I will obtain a master of science degree in Library and Information Science and a graduate certificate in Data Science. The curriculum focuses not only on traditional library science, but also on information technology, reflecting the evolving role of libraries in the digital age.

Certifications

University of Michigan (via Coursera)

Web Design for Everybody Specialization:

Introduction to HTML5
Introduction to CSS3
Interactivity with JavaScript
Advanced Styling with Responsive Design
Web Design Capstone Project

Python for Everybody Specialization:

Programming for Everybody
Python Data Structures
Using Python to Access Web Data
Using Databases with Python
Capstone: Retrieving, Processing, & Visualizing Data

Free Code Camp

freeCodeCamp Full Stack Development Certification: (in progress)

Front End Development
Data Visualization
Back End Development

Udemy

Learn Bootstrap Development
Projects in JavaScript and jQuery (in progress)
PHP & MySQL (in progress)

Microsoft (via edX)

Professional Program Certificate in Data Science (in progress)

Data Science Orientation
Querying Data with Transact-SQL
Analyzing and Visualizing Data with Excel
Essential Statistics for Data Analysis using Excel
Introduction to Python for Data Science
Data Science Essentials
Principles of Machine Learning
Programming with Python for Data Science
Applied Machine Learning
Capstone Project: Cortana Intelligence Competition

Skills

HTML5 & CSS3

Hypertext Markup Language and Cascading Style Sheets are two of the core technologies for building Web pages. HTML provides the structure of the page, CSS the (visual and aural) layout, for a variety of devices. Along with graphics and scripting, HTML and CSS are the basis of building Web pages and Web Applications

JavaScript & jQuery

JavaScript is an object-oriented programming language used to make interactive web pages. It is often used to create forms, polls, and quizzes. jQuery is a cross-platform JavaScript library. It simplifies the use of JavaScript and allows for the creation of dynamic web pages with animations and effects.

Bootstrap3

Bootstrap is a mobile-first and responsive framework that contains HTML and CSS based design templates for typography, forms, buttons, navigation, and other user interface components. It also contains scripting with JavaScript extensions. Bootstrap gives web pages a consistent and familiar look and feel.

Responsive Design

Responsive design is not a platform, but an approach to web development that allows web pages to be accessible on multiple screen sizes, browsers, and devices. A site built with responsive design adapts to the viewing environment by using fluid, proportion-based grids, eliminating the need for creating multiple versions of a site.

Python Programming

Python is a user-friendly, but very powerful high-level programming language for general-purpose programming. Python can be used in server-side web development in order to create dynamic web sites and applications. Python is especially useful in data science. Its core libraries like NumPy, SciPy, and Pandas make it ideal for data analysis and visualization.

Data Analysis & Visualization

Data analysis is the process of applying statistical and/or logical techniques to evaluate and describe data. Data visualization allows for the understanding of the significance of data by placing it in a visual context. Data science has a wide range of applications from policy analysis to biostatistics & epidemiology to business predictive modeling.