Courses

Gain essential insights into data analytics through a comprehensive two-part tutorial designed for managers and beginners. Explore fundamental concepts, techniques, and tools used in data analysis, equipping yourself with the knowledge to make data-driven decisions. Learn how to interpret and leverage data effectively, enhancing your managerial skills and driving business success.

Learn how to manage your manager. In this course, adapted from the podcast How to Be Awesome at Your Job, Mary Abbajay explains how to build a good relationship with your boss.

Learn how to create and manage apps with SCCM and prepare for Microsoft certification exam 70-703. See how to deploy apps using PowerShell scripts, and deploy App-V virtual apps.

This Data Product Manager Nanodegree program highlights the true power of data to shape and optimize product strategies. From building data pipelines to analyzing key product metrics, you'll learn how to drive product innovation and success.

In the era of AI, getting good data to the right people at the right time is more important than ever. In this course, Microsoft Fabric Analytics Engineer: Plan, Implement, and Manage a Solution for Data Analytics, you’ll gain the skills to pass the DP-600 certification exam and transfer those skills to plan, implement, and manage a Microsoft Fabric solution. First, you’ll explore planning a data analytics environment in Microsoft Fabric. Next, you’ll discover how to implement and manage a Microsoft Fabric environment. Finally, you’ll learn how to manage the development lifecycle within Microsoft Fabric. When you’re finished with this course, you’ll have the skills and knowledge of planning, implementing, and managing a solution for data analytics needed to pass the DP-600 exam and carry these skills into practice.

Nowadays, most of the decisions are taken in various organizations/sectors by analyzing stakeholder’s data. This is true for the education sector also. Therefore, minimal knowledge of data analysis is mandatory at all levels in the education sector, to take proactive decisions in improving the system. Education and training are progressively taking place in digital environments. As a result, these environments are generating both structured and unstructured amount of interaction and behavioral data that can be used to design better learning and teaching models for teaching, learning and assessment. The main objective of this course is to use different kinds of methods from data analytics to identify unique patterns from educational data. In particular, the learners will learn about methods and models that are being used in data analytics, students' behavior modeling, and personalized learning material recommendations. The module will be covered both at the theoretical level as well as the practical level where software tools will be used to analyze the data.

Module 1: The use of OneDrive, Git repositories, and Power BI deployment pipelines allows us to follow application lifecycle management techniques. This reduces administrative overhead and provides continuity in the development process.Upon completion of this module, you should be able to: Outline the application lifecycle process. Choose a source control strategy. Design a deployment strategy. Module 2: The deployment pipelines tool enables Power BI creators to manage the development lifecycle of organizational content.By the end of this module, you’ll be able to: Articulate the benefits of deployment pipelines Create a deployment pipeline using Premium workspaces Assign and deploy content to pipeline stages Describe the purpose of deployment rules Deploy content from one pipeline stage to another Module 3: Creating shared data assets for your analytics environment provides structure and consistency. Maintaining those assets is as important, and XMLA endpoint provides additional administrative capabilities.Upon completion of this module, you should be able to: Create specialized datasets. Create live and DirectQuery connections. Use Power BI service lineage view. Use XMLA endpoint to connect datasets.

Great leadership doesn't happen by chance—it happens by design. This course helps you create and evolve a personalized leadership checklist that keeps you focused on what matters most. You'll learn the core components of an effective checklist, from daily practices to strategic responsibilities, and how to structure them for clarity and action. Through practical tools, case examples, and hands-on activities, you'll build a Version 1 of your leadership checklist, test it against real challenges, and refine it as your role evolves.

In the Data Driven Manager specialization, you will learn how to first understand the type of data that you have (or want to generate), then describe it with numbers and graphs to communicate with your audience. You will practice using probability and distributions to understand the underlying nature of your data to make decisions and solve problems in a way that increases the likelihood of a desired outcome. You will learn the steps to create a plan to answer business and engineering questions and reduce risk when making decisions. You’ll study how to determine best- and worst-case scenarios using data. Finally, you’ll acquire data analysis skills to answer business and engineering questions that will help you make appropriate decisions. The courses in this specialization can be taken for academic credit as part of CU Boulder’s Master of Engineering in Engineering Management (ME-EM) degree offered on the Coursera platform. The ME-EM is designed to help engineers, scientists, and technical professionals move into leadership and management roles in the engineering and technical sectors. With performance-based admissions and no application process, the ME-EM is ideal for individuals with a broad range of undergraduate education and/or professional experience. Learn more about the ME-EM program at https://www.coursera.org/degrees/me-engineering-management-boulder.

Looking for a consistent and reliable way to install Windows applications? Learn about the Chocolatey package manager.

Dive into the world of data analytics with this comprehensive 59-minute video tutorial. Learn about the evolution of data over the past two decades and explore the data analytics life cycle. Gain an introduction to machine learning and fundamental algorithms. Watch a practical demonstration using Python/R programming to perform exploratory data analysis on a real dataset. Participate in a Q&A session to clarify doubts and deepen your understanding. Perfect for beginners, this tutorial provides a solid foundation to kickstart your journey in data analytics, equipping you with essential knowledge to leverage data for solving business problems, curing diseases, and even predicting the future.

Module 1: Learn the fundamental rules, regulations, and policies of a BI governance approach.In this module, you will: Define the key components of an effective BI governance model Describe the key elements associated with data governance Configure, deploy, and manage elements of a BI governance strategy Set up BI help and support settings Module 2: Learn how to collaborate and share dashboards and reports with coworkers.In this module, you will: Understand the differences between My workspace, workspaces, and apps Describe new workspace capabilities and how they improve the user experience Anticipate migration impact to Power BI users Share, publish to the web, embed links and secure Power BI reports, dashboards, and content Module 3: Learn how to use activity logs and auditing with Power BI to monitor and inspect user activity in a Power BI environment.In this module, you will: Discover what usage metrics are available through the Power BI admin portal Optimize use of usage metrics for dashboards and reports Distinguish between audit logs and the activity logs Module 4: Learn about the differences between Power BI Pro and Power BI Premium, and how Power BI Premium manages capacity resources.By the end of this module, you'll be able to: Describe the difference between Power BI Pro and Power BI Premium Define dataset eviction Explain how Power BI manages memory resources List three external tools you can use with Power BI Premium. Module 5: Learn how to establish a data access infrastructure for accessing all your data within Power BI.By the end of this module, you'll be able to: Understand the difference between gateways, the various connectivity modes, and data refresh methods. Describe the gateway network requirements, where to place the gateway in your network, and how to use clustering to ensure high availability. Scale, monitor, and manage gateway performance and users. Module 6: Learn the various ways to share Power BI reports beyond your Power BI tenant.By the end of this module, you'll be able to: Describe the various embedding scenarios that allow you to broaden the reach of Power BI Understand the options for developers to customize Power BI solutions Learn to provision and optimize Power BI embedded capacity and create and deploy dataflows Build custom Power BI solutions template apps Module 7: Common manual tasks can be automated with Microsoft Power BI Cmdlets for Windows PowerShell and PowerShell core.By the end of this module, you'll be able to: Use REST APIs to automate common Power BI admin tasks Apply Power BI Cmdlets for Windows PowerShell and PowerShell core Use Power BI Cmdlets Automate common Power BI admin tasks with scripting Module 8: Build reports using Power BI within Azure Synapse AnalyticsIn this module, you'll: Describe the Power BI and Synapse workspace integration Understand Power BI data sources Describe optimization options Visualize data with serverless SQL pools

Module 1: Microsoft Fabric is a SaaS solution for end-to-end data analytics. As an administrator, you can configure features and manage access to suit your organization's needs.In this module, you'll learn how to: Describe Fabric admin tasks Navigate the admin center Manage user access Govern data in Fabric Module 2: Discover how Microsoft Fabric can meet your enterprise's analytics needs in one platform. Discover the capabilities Fabric has to offer, understand how it works, and identify how you can use Fabric for your analytics needs.In this module, you'll learn how to: Describe end-to-end analytics in Microsoft Fabric Module 3: Explore the potential of the medallion architecture design in Microsoft Fabric. Organize and transform your data across Bronze, Silver, and Gold layers of a lakehouse for optimized analytics.In this module, you'll learn how to: Describe the principles of using the medallion architecture in data management. Apply the medallion architecture framework within the Microsoft Fabric environment. Analyze data stored in the lakehouse using DirectLake in Power BI. Describe best practices for ensuring the security and governance of data stored in the medallion architecture. Module 4: Learn the key concepts and strategies for implementing continuous integration and continuous deployment (CI/CD) in Microsoft Fabric.In this module, you'll learn how to: Define CI/CD and describe how it's implemented in Fabric. Implement version control and Git integration. Use deployment pipelines to automate the deployment process. Automate CI/CD using Fabric APIs.

Prepare for a new career in the high-growth field of data analytics, no experience or degree required. Get professional training designed by Google and have the opportunity to connect with top employers. There are 483,000 open jobs in data analytics with a median entry-level salary of $92,000.¹ Data analytics is the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision making. Over 8 courses, gain in-demand skills that prepare you for an entry-level job. You’ll learn from Google employees whose foundations in data analytics served as launchpads for their own careers. At under 10 hours per week, you can complete the certificate in less than 6 months. Upon completion, you can directly apply for jobs with Google and over 150 U.S. employers, including Deloitte, Target, Verizon, and of course, Google. 75% of certificate graduates report a positive career outcome (e.g., new job, promotion, or raise) within six months of completion² ¹Lightcast™ US Job Postings (2022: Jan. 1, 2022 - Dec. 31, 2022) ²Based on program graduate survey, United States 2022

Dive into a comprehensive 19-hour course on Big Data Analytics offered by the California Institute of Technology. Explore best programming practices, data models, relational databases, SQL, and alternative databases. Learn about inference, uncertainty, and probability basics. Master the R programming language for data analysis. Delve into machine learning concepts, including supervised and unsupervised learning, classification, and clustering techniques. Study decision trees, random forests, and their applications in space exploration and cancer research. Examine pattern recognition, dimensionality reduction, and feature selection methods. Discover data visualization principles, tools, and techniques for effective communication. Investigate cloud computing, algorithmic approaches to big data, semantics, and genetic algorithms. Conclude with an in-depth look at big data architecture fundamentals and content detection and analysis for large-scale datasets.

Practical introduction to Internet of Things (IoT), Data Analytics, NodeMCU, ESP8266 and Machine Learning,Learn by doing What you'll learn: Quick start Building IoT ProjectsDesign IoT Devices using Thingspeak Platform, NodeMCU (ESP8266)Data Collection using IoT DevicesMATLABData Analysis and Data VisualizationMachine Learning Welcome to IoT Data Analytics Course. This is practical course to learn IoT and Data Analytics from the beginning. Learn how to program NOdeMCU (ESP8266), collecting data and data analysis.There are billions of devices in homes, industries, cities, hospitals, cars, and thousands of other places. With the rapid increase of devices, you increasingly need solutions to connect them, and collect, store, and analyze device data. Data in its raw form is not always useful. Data need to be processed to transform into information. In this course, you will learn how to collect and analyse sensor data. You will learn, data processing, data visualization and machine learning algorithms for predictive analytics. The following are the various topics covered in this training:Introduction to Internet of Things (IoT)Getting started with Arduino ProgrammingLearn to work with NodeMCU (ESP8266 based IoT Board)Collecting Data from sensors locallySending Sensor Data to IoT Cloud (Thingspeak)Introduction to MATLABData AnalysisData VisualizationMachine LearningYou'll get to practice the skills learned during the training, by doing more than five projects on Internet of Things (IoT) and Data analytics. Hands on ProjectsSending Light Sensor Values to IoT CloudSending Temperature and Humidity Values to IoT CloudSensor Data VisualizationEnergy savings with Anomaly Detection using Z-Score AnalysisCorrelation between Temperature and Humidity and RegressionTemperature Prediction using Polynomial RegressionWhat am I going to get from this course?Build IoT projects for sensor data collectionApply the fundamentals of machine learning and statistics to extract value from IoT dataUnderstand different business use-cases for IoT data

Explore a real-world business case study on big data analytics in this 22-minute conference talk from GOTO Stockholm 2017. Delve into the power of graph technology and fast big data ingestion for solving complex problems like anti-money laundering and fraud detection. Learn how legacy systems, data management challenges, and the evolution of big data impact modern business solutions. Discover the potential of deep learning and innovative approaches like NeoConnections in tackling fraud and AML issues. Gain insights from Paul Smith, Data Science Director at Trifork, as he shares his experience and expertise in leveraging advanced analytics for practical business applications.

This course introduces the use of the Python programming language to manipulate datasets as an alternative to spreadsheets. You will follow the OSEMN framework of data analysis to pull, clean, manipulate, and interpret data all while learning foundational programming principles and basic Python functions. You will be introduced to the Python library, Pandas, and how you can use it to obtain, scrub, explore, and visualize data. By the end of this course you will be able to: • Use Python to construct loops and basic data structures • Sort, query, and structure data in Pandas, the Python library • Create data visualizations with Python libraries • Model and interpret data using Python This course is designed for people who want to learn the basics of using Python to sort and structure data for data analysis. You don't need marketing or data analysis experience, but should have basic internet navigation skills and be eager to participate.

This specialization develops learners’ analytics mindset and knowledge of data analytics tools and techniques. Specifically, this specialization develops learners' analytics skills by first introducing an analytic mindset, data preparation, visualization, and analysis using Excel. Next, this specialization develops learners' skills of using Python for data preparation, data visualization, data analysis, and data interpretation and the ability to apply these skills to issues relevant to accounting. This specialization also develops learners’ skills in machine learning algorithms (using Python), including classification, regression, clustering, text analysis, time series analysis, and model optimization, as well as their ability to apply these machine learning skills to real-world problems.

Big data is a fast-growing field and skills in the area are some of the most in demand today. The Big Data Analytics program from Queensland University of Technology (QUT) comprises four online courses that each look at a different element of big data. You’ll begin by examining how big data is collected and stored, before going on to explore how statistical inference, machine learning, mathematical modelling and data visualisation are used in its analysis. You’ll become familiar with predictive analysis, dimension reduction, machine learning, clustering techniques and decision trees, before going on to look at the maths that underpins many of the tools you can use to manage and analyse big data. Accessible for free on desktop, tablet or mobile and delivered in bite-sized chunks, the courses provide a flexible way to develop your big data analytics skills. When you complete all four courses, upgrade and earn a Certificate of Achievement for each, you will receive a FutureLearn Award as proof of completing the program of study.