ACP Data Science

Practical Math and Statistics are the foundation of the fields of Data Science and Predictive Analytics. Statistics are used in every part of business, science, and institutional data processing. This course covers fundamental statistical skills needed for Data Science and Predictive Analytics. This is an application-oriented course and the approach is practical. Students will take a look at several statistical techniques and discuss situations in which one would use each technique, the assumptions made by each method, how to set up the analysis, as well as how to interpret the results. This course starts with an introduction to data analysis. Next the course covers the fundamental concepts of descriptive statistics, probability, and inferential statistics, which include the central limit theorem, and hypothesis testing. From there the course will focus on various statistical tests, including the Chi-Square test of independence, t-tests, correlation, ANOVA, linear regression, time series, and applying previously learned techniques in new situations.

Introduction to Python is a beginner introduction to programming using Python. This course is designed for those who have no programming experience and do not have a technical background. It is for those who want a gentle introduction. After this course, students may want to take a more intermediate or advanced Python course. Or, they may feel confident enough to start learning on their own. If you do not have a background in Python, but you do have a good background in Java, C, or another language, this course could feel slow for you. Students will learn the following: how to use variable types, flow control, and functions, how to interact with the system via Python, how to write simple scripts to process text, and how to use Jupyter, a popular development tool for Python.

The goal of this course is to demystify data science and to familiarize students with key data scientist skills, techniques, and concepts. Starting with foundational concepts like analytics taxonomy, the Cross-Industry Standard Process for Data Mining, and data diagnostics, the course will then move on to compare data science with classical statistical techniques. An overview of the most common techniques used in data science, including data analysis, statistical modeling, data engineering, relational databases, SQL and NoSQL, manipulation of data at scale (big data), algorithms for data mining, data quality, remediation and consistency operations will be covered.

This course is designed to enhance student proficiency in data design, data management, data warehouse, data modeling, and query manipulation skills. Topics include techniques and methods for identification, extraction, and preparation of data for processing with database software. Gain an overview of the basic techniques of data engineering, including data normalization, data engineering, relational and non-relational databases, SQL and NoSQL, manipulation of data at scale (big data), algorithms for data operations. Students will work in teams on a final project to explore, analyze, summarize and present findings in a real-world big data set.

Visualization plays a fundamental role in understanding properties and relationships in data to extract insights and communicate results. Whether the analytics is descriptive, diagnostic, prescriptive, or proscriptive, visualization is essential throughout any analytics cycle. This course will focus on applying various methods and techniques to different stages of the analytics cycle such as during data preparation, modeling, and reporting. Students will learn techniques for visualizing univariate, multivariate, temporal, text-based, hierarchical, and network/graph-based data both in ad hoc analysis as well as in automated generation.

Enterprises are using technologies such as MapReduce, Hadoop, Yarn, and Apache Spark to extract value from Big Data. This course provides an in-depth overview of Hadoop and Spark, the cornerstones of big data processing. To crystalize the concepts behind Hadoop and Spark, students will work through a series of short, focused exercises. Concepts covered include Hadoop architecture, the Apache Spark Big Data Framework, data ingestion, distributed processing, and functional programming. Additionally, students will learn how to configure and install a Hadoop cluster, write basic MapReduce programs, utilize advanced MapReduce programming practices, and utilize interfaces such as Pig and Hive to interact with Hadoop.