The top 8 Data Collection courses you need to take

1. AWS: Data Collection Systems

AWS: Data Collection Systems Course is the first course of the AWS Certified Data Analytics Speciality Specialization. This Course is designed to describe data collection systems and their characteristics in detail. This course is basically divided into three modules and each module is further segmented by Lessons and Video Lectures. This course facilitates learners with approximately 3:30-4:00 Hours Video lectures that provide both Theory and Hands -On knowledge. Also, Graded and Ungraded Quiz are provided with every module in order to test the ability of learners. Module 1: Data Collection Systems and Data Streams in AWS Module 2: Data Integration Services in AWS Module 3: Data Compression and Transformation in AWS e.g. This is primarily aimed at first- and second-year undergraduates interested in engineering or science, along with high school students and professionals with an interest in programming...

2. Survey Data Collection and Analytics

This specialization covers the fundamentals of surveys as used in market research, evaluation research, social science and political research, official government statistics, and many other topic domains. In six courses, you will learn the basics of questionnaire design, data collection methods, sampling design, dealing with missing values, making estimates, combining data from different sources, and the analysis of survey data. In the final Capstone Project, you’ll apply the skills learned throughout the specialization by analyzing and comparing multiple data sources.\n\nFaculty for this specialisation comes from the Michigan Program in Survey Methodology and the Joint Program in Survey Methodology, a collaboration between the University of Maryland, the University of Michigan, and the data collection firm Westat, founded by the National Science Foundation and the Interagency Consortium of Statistical Policy in the U.S. to educate the next generation of survey researchers, survey statisticians, and survey methodologists. In addition to this specialization we offer short courses, a summer school, certificates, master degrees as well as PhD programs...

3. Data Collection and Processing with Python

This course teaches you to fetch and process data from services on the Internet. It covers Python list comprehensions and provides opportunities to practice extracting from and processing deeply nested data. You'll also learn how to use the Python requests module to interact with REST APIs and what to look for in documentation of those APIs. For the final project, you will construct a “tag recommender” for the flickr photo sharing site. The course is well-suited for you if you have already taken the "Python Basics" and "Python Functions, Files, and Dictionaries" courses (courses 1 and 2 of the Python 3 Programming Specialization). If you are already familiar with Python fundamentals but want practice at retrieving and processing complex nested data from Internet services, you can also benefit from this course without taking the previous two. This is the third of five courses in the Python 3 Programming Specialization...

4. Framework for Data Collection and Analysis

This course will provide you with an overview over existing data products and a good understanding of the data collection landscape. With the help of various examples you will learn how to identify which data sources likely matches your research question, how to turn your research question into measurable pieces, and how to think about an analysis plan. Furthermore this course will provide you with a general framework that allows you to not only understand each step required for a successful data collection and analysis, but also help you to identify errors associated with different data sources. You will learn some metrics to quantify each potential error, and thus you will have tools at hand to describe the quality of a data source. Finally we will introduce different large scale data collection efforts done by private industry and government agencies, and review the learned concepts through these examples. This course is suitable for beginners as well as those that know about one particular data source, but not others, and are looking for a general framework to evaluate data products...

5. Algorithms, Data Collection, and Starting to Code

This course starts you on your journey learning about computational thinking and beginning C programming. If you’d like to explore how we can interact with the world in a rigorous, computational way, and would also like to start learning to program, this is the course for you! You may have heard lots of talk about computational thinking recently, but if you ask 10 different people what it is you’ll probably get 10 different answers. Rather than trying to define computational thinking, we’ll just say it’s a problem-solving process that includes lots of different components. In this course, we’ll explore algorithms and data collection. Most people have a better understanding of what beginning C programming means! You’ll start learning how to develop C programs in this course by writing your first C program; learning about data types, variables, and constants; and honing your C programming skills by implementing a variety of STEM computations. This course doesn't assume you have any previous programming experience, so don't worry if you've never written code before. If that all sounds interesting to you, go ahead and jump into the course! Caution: Beginning (assuming no prior programming knowledge) is not the same as easy (not hard to do). Learning to program IS hard to do, especially since the courses in this specialization are built from a freshman-level college course. Meeting the course challenges while you master the material will be rewarding to you, but doing that will require hard work and maybe even a few expletives along the way. Module 1: Learn about algorithms and write your first C program Module 2: Discover how we store data in our programs Module 3: Explore how we use data collection to solve problems and answer questions Module 4: Practice writing C programs to implement STEM computations...

6. Teaching Impacts of Technology: Data Collection, Use, and Privacy

In this course you’ll focus on how constant data collection and big data analysis have impacted us, exploring the interplay between using your data and protecting it, as well as thinking about what it could do for you in the future. This will be done through a series of paired teaching sections, exploring a specific “Impact of Computing” in your typical day and the “Technologies and Computing Concepts” that enable that impact, all at a K12-appropriate level. This course is part of a larger Specialization through which you’ll learn impacts of computing concepts you need to know, organized into 5 distinct digital “worlds”, as well as learn pedagogical techniques and evaluate lesson plans and resources to utilize in your classroom. By the end, you’ll be prepared to teach pre-college learners to be both savvy and effective participants in their digital world. In this particular digital world (personal data), you’ll explore the following Impacts & Technology pairs -- Impacts (Show me what I want to see!): Internet Privacy, Custom Ads, Personalization of web pages Technologies and Computing Concepts: Cookies, Web vs Internet, https, Web Servers Impacts (Use my data…. But protect it!): Common Cybersecurity knowledge levels, ISP data collection, Internet design, finding out what is known about you online, software terms and services Technology and Computing Concepts: DNS, Cryptography (ciphers, hashing, encryption, SSL), Deep and Dark Web Impacts (What could my data do for me in the future?): What is Big Data, Machine Learning finds new music, Wearable technologies. Technology and Computing Concepts: AI vs ML, Supervised vs Unsupervised learning, Neural Networks, Recommender systems, Speech recognition In the pedagogy section for this course, in which best practices for teaching computing concepts are explored, you’ll learn how to apply Bloom’s taxonomy to create meaningful CS learning objectives, the importance of retrieval-based learning, to build learning activities with online simulators, and how to use “fun” books to teach computing. In terms of CSTA K-12 computer science standards, we’ll primarily cover learning objectives within the “impacts of computing” concept, while also including some within the “networks and the Internet” concepts and the “data and analysis” concept. Practices we cover include “fostering and inclusive computing culture”, “recognizing and defining computational problems”, and “communicating about computing”...

7. Develop Mobile Data Collection Solutions using Kobo Toolbox

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More organizations than ever before are embracing the switch from data collection using paper forms to using mobile devices. This is so due to the benefits of mobile data collection that include better data quality, speed and convenience, as well as being low cost. One of the best platforms for developing and deploying mobile data collection forms is Kobo Toolbox. The platform which includes a mobile app for data collection and web based tools for developing forms, aggregating data, managing data, and data visualizations, is by far the most feature rich and can be used throughout an organization's data management cycle. By the end of the course, participants will be able to:- Develop a data collection form in Kobo Toolbox- Implement skip and validation logic- Deploy the form into mobile devices- Collect and upload data- View and download data- Visualize data using reports and maps...