In this hands-on guided project, we will train regression models to find the probability of a student getting accepted into a particular university based on their profile. This project could be practically used to get the university acceptance rate for individual students using web application. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

This course offers step-by-step guidance and hands-on experience of designing and implementing a real-world data mining project, including problem formulation, literature survey, proposed work, evaluation, discussion and future work. Data Mining Project can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Course logo image courtesy of Mariana Proença, available here on Unsplash: https://unsplash.com/photos/_WgnXndHmQ4

By the end of this project, you will learn How you can use data visualization techniques to answer to some analytical questions. in this project we are going to use COVID19 dataset we have consisting of the data related cumulative number of confirmed, recovered, and deaths cases. we are going to prepare this dataset to answer these questions: How does the Global Spread of the virus look like?, How intensive the spread of the virus has been in the countries? Does covid19 national lockdowns and self-isolations in different countries have actually impact on COVID19 transmission? we are going to use Plotly module, which is a great visualization tool in python, in order to plot some insightful and intuitive graphs to answer the questions.

In this project-based course, you will design and execute a complete GIS-based analysis – from identifying a concept, question or issue you wish to develop, all the way to final data products and maps that you can add to your portfolio. Your completed project will demonstrate your mastery of the content in the GIS Specialization and is broken up into four phases: Milestone 1: Project Proposal - Conceptualize and design your project in the abstract, and write a short proposal that includes the project description, expected data needs, timeline, and how you expect to complete it. Milestone 2: Workflow Design - Develop the analysis workflow for your project, which will typically involve creating at least one core algorithm for processing your data. The model need not be complex or complicated, but it should allow you to analyze spatial data for a new output or to create a new analytical map of some type. Milestone 3: Data Analysis – Obtain and preprocess data, run it through your models or other workflows in order to get your rough data products, and begin creating your final map products and/or analysis. Milestone 4: Web and Print Map Creation – Complete your project by submitting usable and attractive maps and your data and algorithm for peer review and feedback.

Have you started using spreadsheets like Excel and want to learn how to write formulas and functions to perform simple data analysis? In this project, you will learn about the general format for writing formulas and functions in Excel to perform analysis on the sales data from a sample company. In this analysis, you will calculate total sums of profits, you will learn how to use functions to analyze the popularity of the items sold and you will also learn how to calculate averages and percentages of monthly profits. Throughout the project, you will work through some examples that will show you how to apply the formulas and functions you have learned.

A good algorithm usually comes together with a set of good data structures that allow the algorithm to manipulate the data efficiently. In this online course, we consider the common data structures that are used in various computational problems. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments. This will help you to understand what is going on inside a particular built-in implementation of a data structure and what to expect from it. You will also learn typical use cases for these data structures. A few examples of questions that we are going to cover in this class are the following: 1. What is a good strategy of resizing a dynamic array? 2. How priority queues are implemented in C++, Java, and Python? 3. How to implement a hash table so that the amortized running time of all operations is O(1) on average? 4. What are good strategies to keep a binary tree balanced? You will also learn how services like Dropbox manage to upload some large files instantly and to save a lot of storage space!

Spreadsheet software remains one of the most ubiquitous pieces of software used in workplaces around the world. Learning to confidently operate this software means adding a highly valuable asset to your employability portfolio. Across the globe, millions of job advertisements requiring Excel skills are posted every day. At a time when digital skills jobs are growing much faster than non-digital jobs, completing this course will position you ahead of others, so keep reading. In this last course of our Specialization Excel Skills for Business you will build on the strong foundations of the first three courses: Essentials, Intermediate I + II. In the Advanced course, we will prepare you to become a power user of Excel - this is your last step before specializing at a professional level. The topics we have prepared will challenge you as you learn how to use advanced formula techniques and sophisticated lookups. You will clean and prepare data for analysis, and learn how to work with dates and financial functions. An in-depth look at spreadsheet design and documentation will prepare you for our big finale, where you will learn how to build professional dashboards in Excel.

Are you interested in Data Science but lack the math background for it? Has math always been a tough subject that you tend to avoid? This course will teach you the most fundamental Linear Algebra that you will need for a career in Data Science without a ton of unnecessary proofs and concepts that you may never use. Consider this an expressway to Data Science with approachable methods and friendly concepts that will guide you to truly understanding the most important ideas in Linear Algebra. This course is designed to prepare learners to successfully complete Statistical Modeling for Data Science Application, which is part of CU Boulder's Master of Science in Data Science (MS-DS) program. Logo courtesy of Dan-Cristian Pădureț on Unsplash.com

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you learn how to connect Google Data Studio to Google BigQuery data tables, create charts, and explore the relationships between dimensions and measures.

In this 1-hour long project-based course, you will learn how to create Neural Networks in the Deep Learning Framework PyTorch. We will creating a Convolutional Neural Network for a 10 Class Image Classification problem which can be extended to more classes. We will start off by looking at how perform data preparation and Augmentation in Pytorch. We will be building a Neural Network in Pytorch. We will add the Convolutional Layers as well as Linear Layers. We will then look at how to add optimizer and train the model. Finally, we will test and evaluate our model on test data. The project will get you introduced with Pytorch. You will in the end understand how the framework works and get you started with building Neural Networks in Pytorch. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.