In this 2-hours long project-based course, you will learn how to . Create IAM Identities using AWS Console and CLI . Enable ' Cross account access' . Create ' Identity Provider' in AWS By the end of this course, you will get a good understanding of what AWS IAM is and all we can do using IAM.

This course provides a framework for how analysts can create and evaluate quantitative measures. Consider the many tricky concepts that are often of interest to analysts, such as health, educational attainment and trust in government. This course will explore various approaches for quantifying these concepts. The course begins with an overview of the different levels of measurement and ways to transform variables. We’ll then discuss how to construct and build a measurement model. We’ll next examine surveys, as they are one of the most frequently used measurement tools. As part of this discussion, we’ll cover survey sampling, design and evaluation. Lastly, we’ll consider different ways to judge the quality of a measure, such as by its level of reliability or validity. By the end of this course, you should be able to develop and critically assess measures for concepts worth study. After all, a good analysis is built on good measures.

In this 1-hour long project-based course, you will learn how to create path models using Smartpls. We will take a project on changing behavior and check if attitudes or subjective norms impact behavior the most. We will learn how to launch this new software, create the model and run it. We will then show you how to interpret the same. 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.

Learn how to go from raw data to meaningful insights using AWS with this one-week course. Throughout the course, you’ll learn about the fundamentals of Data Analytics from AWS experts. Start off with an overview of different types of data analytics techniques - descriptive, diagnostic, predictive, and prescriptive before diving deeper into the descriptive data analytics. Then, apply your knowledge with a guided project that makes use of a simple, but powerful dataset available by default in every AWS account: the logs from AWS CloudTrail. The CloudTrail service enables governance, compliance, operational auditing, and risk auditing of your AWS account. Through the project you’ll also get an introduction to Amazon Athena and Amazon QuickSight. And, you’ll learn how to build a basic security dashboard as a simple but practical method of applying your newfound data analytics knowledge.

In this 1-hour long project-based course, we will predict titanic survivors’ using logistic regression and naïve bayes classifiers. The sinking of the Titanic is one of the key sad tragedies in history and it took place on April 15th, 1912. The numbers of survivors were low due to lack of lifeboats for all passengers. This practical guided project, we will analyze what sorts of people were likely to survive this tragedy with the power of machine learning. 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.

In this 1-hour long project-based course, you will learn the basic building blocks in the Python language and how to Develop a Python program that constructs reverse, complement, and reverse-complement nucleic acid sequences (DNA, RNA). Also, you will make your code read a file that has a long DNA sequence and deal with one of the complete genomes for the novel coronavirus.

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.

In this one-hour guided project, you will learn how to process geospatial data using Python. We will go through different geoprocessing tasks including how to create Geodataframes from CSV files and perform a spatial join.

This course focuses on how analysts can measure and describe the confidence they have in their findings. The course begins with an overview of the key probability rules and concepts that govern the calculation of uncertainty measures. We’ll then apply these ideas to variables (which are the building blocks of statistics) and their associated probability distributions. The second half of the course will delve into the computation and interpretation of uncertainty. We’ll discuss how to conduct a hypothesis test using both test statistics and confidence intervals. Finally, we’ll consider the role of hypothesis testing in a regression context, including what we can and cannot learn from the statistical significance of a coefficient. By the end of the course, you should be able to discuss statistical findings in probabilistic terms and interpret the uncertainty of a particular estimate.

The use of Excel is widespread in the industry. It is a very powerful data analysis tool and almost all big and small businesses use Excel in their day to day functioning. This is an introductory course in the use of Excel and is designed to give you a working knowledge of Excel with the aim of getting to use it for more advance topics in Business Statistics later. The course is designed keeping in mind two kinds of learners - those who have very little functional knowledge of Excel and those who use Excel regularly but at a peripheral level and wish to enhance their skills. The course takes you from basic operations such as reading data into excel using various data formats, organizing and manipulating data, to some of the more advanced functionality of Excel. All along, Excel functionality is introduced using easy to understand examples which are demonstrated in a way that learners can become comfortable in understanding and applying them. To successfully complete course assignments, students must have access to a Windows version of Microsoft Excel 2010 or later. ________________________________________ WEEK 1 Module 1: Introduction to Spreadsheets In this module, you will be introduced to the use of Excel spreadsheets and various basic data functions of Excel. Topics covered include: • Reading data into Excel using various formats • Basic functions in Excel, arithmetic as well as various logical functions • Formatting rows and columns • Using formulas in Excel and their copy and paste using absolute and relative referencing ________________________________________ WEEK 2 Module 2: Spreadsheet Functions to Organize Data This module introduces various Excel functions to organize and query data. Learners are introduced to the IF, nested IF, VLOOKUP and the HLOOKUP functions of Excel. Topics covered include: • IF and the nested IF functions • VLOOKUP and HLOOKUP • The RANDBETWEEN function ________________________________________ WEEK 3 Module 3: Introduction to Filtering, Pivot Tables, and Charts This module introduces various data filtering capabilities of Excel. You’ll learn how to set filters in data to selectively access data. A very powerful data summarizing tool, the Pivot Table, is also explained and we begin to introduce the charting feature of Excel. Topics covered include: • VLOOKUP across worksheets • Data filtering in Excel • Use of Pivot tables with categorical as well as numerical data • Introduction to the charting capability of Excel ________________________________________ WEEK 4 Module 4: Advanced Graphing and Charting This module explores various advanced graphing and charting techniques available in Excel. Starting with various line, bar and pie charts we introduce pivot charts, scatter plots and histograms. You will get to understand these various charts and get to build them on your own. Topics covered include • Line, Bar and Pie charts • Pivot charts • Scatter plots • Histograms