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computer-organization-and-programming

Computer structure and machine language, addressing techniques, macros, file I/O, program segmentation, and linkage.

data-mining

Introduction to basic data mining techniques (such as association rules mining, cluster analysis, and classification methods) and their applications (such as Web data mining, biomedical data mining and security).

data-visualization

Data visualization is about displaying data in visual forms such as charts, diagrams, or 3D models. This course is targeted towards students who are interested in using data visualization in their work as well as those who are interested in developing visualization systems. Topics include the theoretical basis (e.g. Gestalt theory, visual attention, visual complexity theories), visualization techniques (e.g. interactive maps, time series charts, scatterplot maps, trees, networks, graphs, etc.), and visualization tools (e.g. Google Chart Tools, Processing). Students will learn basic visualization principles, how to choose the right kind of display for specific purposes, and how to provide interactive features for the user interface.

databases-and-sql-for-data-science

Much of the world's data resides in databases. SQL (or Structured Query Language) is a powerful language which is used for communicating with and extracting data from databases. A working knowledge of databases and SQL is a must if you want to become a data scientist. The purpose of this course is to introduce relational database concepts and help me learn and apply the foundational knowledge of the SQL language. It is also intended to get me started with performing SQL access in a data science environment. The emphasis in this course is on hands-on and practical learning . As such, I worked with real databases, real data science tools, and real-world datasets. I created a database instance in the cloud. Through a series of hands-on labs I practice building and running SQL queries. I also learned how to access databases from Jupyter notebooks using SQL and Python.

lispy

LISP interpreter in Python

machinelearning

Design and Implement your Machine Learning Algorithm on the following Dataset

programming-language-concepts

sweproject

system-level-programming

An introduction to programming at the level of the operating system. Topics include editors, system calls, programming tools, files, processes, interprocess communication, and shells.

web-programming

writing-javascript-actions