1.011 Project Evaluation covers methodologies for evaluating civil engineering projects, which typically are large-scale and long-lived and involve many economic, financial, social and environmental factors. The course places an emphasis on dealing with uncertainty. Students learn basic techniques of engineering economics, including net present value analysis, life-cycle costing, benefit-cost analysis, and other approaches to project evaluation. Examples are drawn from both contemporary and historical projects in various fields, including transportation systems, urban development, energy and environmental projects, water resource management, telecommunications systems, and other elements of the public and private projects and programs.

What you learn in this course will give you a strong foundation in all the areas that support analytics and will help you to better position yourself for success within your organization. You’ll develop skills and a perspective that will make you more productive faster and allow you to become a valuable asset to your organization.

This course also provides a basis for going deeper into advanced investigative and computational methods, which you have an opportunity to explore in future courses of the Data Analytics for Business specialization.

Syllabus

Data and Analysis in the Real World
-Welcome to week 1! In this module we’ll learn how to think about analytical problems and examine the process by which data enables analysis & decision making. We’ll introduce a framework called the Information-Action Value chain which describes the path from events in the world to business action, and we’ll look at some of the source systems that are used to capture data. At the end of this course you will be able to: Explain the information lifecycle from events in the real world to business actions, and how to think about analytical problems in that context , Recognize the types of events and characteristics that are often used in business analytics, and explain how the data is captured by source systems and stored using both traditional and emergent technologies, Gain a high-level familiarity with relational databases and learn how to use a simple but powerful language called SQL to extract analytical data sets of interest, Appreciate the spectrum of roles involved in the data lifecycle, and gain exposure to the various ways that organizations structure analytical functions, Summarize some of the key ideas around data quality, data governance, and data privacyAnalytical Tools
-In this module we’ll learn about the technologies that enable analytical work. We’ll examine data storage and databases, including the relational database. We’ll talk about Big Data and Cloud technologies and ideas like federation, virtualization, and in-memory computing. We’ll also walk through a landscape of some of the more common tool classes and learn how these tools support common analytical tasks.

Data Extraction Using SQL
-In this module we’ll learn how to extract data from a relational database using Structured Query Language, or SQL. We’ll cover all the basic SQL commands and learn how to combine and stack data from different tables. We’ll also learn how to expand the power of our queries using operators and handle additional complexity using subqueries.

Real World Analytical Organizations
-In this module we focus on the people and organizations that work with data and actually execute analytics. We’ll discuss who does what and see how organizational structures can influence efficiency and effectiveness. We’ll also look at the supporting rules & processes that help an analytical organization run smoothly, like Data Governance, Data Privacy, and Data Quality.

Course Curriculum

Section 1: Introduction
Section 2: Advanced Computing

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