Tableau, Power BI, Excel analytics, and business intelligence
37 courses available
Showing 37 courses
Udemy
Intermediate FileMaker Pro 15 Scripting, Calculations, Relationships and Reporting. Beginner-friendly Data Science & Analytics course on Udemy with 8 hours of content. Rated 4.9/5 by 101 learners. Price: $59.99.
Udemy
Intermediate FileMaker Pro 15 Scripting, Calculations, Relationships and Reporting. Beginner-friendly Data Science & Analytics course on Udemy with 9 hours of content. Rated 4.9/5 by 73 learners. Price: $59.99.
Udemy
Learn SPSS from the simplest explanations, so much practice and APA reporting templates. Beginner-friendly Data Science & Analytics course on Udemy with 6 hours of content. Rated 4.7/5 by 15 learners. Price: $199.99.
Udemy
Intermediate FileMaker Pro 15 Scripting, Calculations, Relationships and Reporting. Beginner-friendly Data Science & Analytics course on Udemy with 9 hours of content. Rated 4.7/5 by 385 learners. Price: $19.99.
Udemy
This course is for anyone who wants to develop feature-rich applications using Caspio's online database platform. Beginner-friendly Data Science & Analytics course on Udemy with 2 hours of content. Rated 4.5/5 by 4 learners. Available for free.
Udemy
A course on how to deliver good internal organisational reporting for your company / client. Beginner-friendly Data Science & Analytics course on Udemy with 1 hour of content. Rated 4.2/5 by 11 learners. Price: $29.99.
Udemy
This course prepares participants to review, analyze, and make decisions based on results from business intelligence. Beginner-friendly Data Science & Analytics course on Udemy with 1 hour of content. Rated 3.9/5 by 20 learners. Price: $19.99.
Udemy
Master business intelligence concepts with ease!. Beginner-friendly Data Science & Analytics course on Udemy with 4 hours of content. Rated 3.8/5 by 110 learners. Price: $199.99.
Udemy
Qlik Sense is a self driven data visualization tool. Drive insight discovery with the data visualization app. Beginner-friendly Data Science & Analytics course on Udemy with 20 hours of content. Rated 2.9/5 by 6 learners. Price: $3.
Universitat Autònoma de Barcelona (via Coursera)
Los continuos cambios tecnológicos, sobre todo en aquellos aspectos vinculados a las tecnologías de la información y la comunicación (TIC) hacen que las personas tengan la necesidad de actualizarse de forma continua para que sus conocimientos no queden obsoletos. En este contexto, para las empresas se convierte en algo imprescindible disponer de profesionales que tengan las competencias necesarias para ejercer con éxito las actividades que requieren en su lugar de trabajo. El curso, basado en las experiencias previas y el syllabus de ECDL (European Computer Driving Licence), nace con la voluntad de facilitar: el desarrollo continuado de las personas en aquellas competencias vinculadas a las tecnologías de la información y la comunicación, la inserción laboral y la renovación de las competencias tecnológicas de los estudiantes y de los profesionales. Los cursos que ofrecemos bajo el título general “Competencias digitales” están destinados a personas sin conocimientos de ofimática o a personas con unas competencias digitales básicas y que deseen mejorar sus conocimientos de ofimática para ser más eficientes en sus estudios o en su trabajo, o bien quieran aumentar sus perspectivas laborales. En este curso, trabajaremos la aplicación Microsoft Word (procesador de textos). Si piensas inscribirte también en los cursos de Excel y PowerPoint, puedes hacer la inscripción en el curso "Competencias Digitales 2: Herramientas de Ofimática (Microsoft Word, Excel y PowerPoint)" donde tendrás los contenidos de los tres cursos en uno solo y te ahorrarás tener que pagar el certificado de cada uno).
Real Madrid Graduate School Universidad Europea (via Coursera)
Este curso introduce los conceptos matemáticos, estadísticos y de manejo de datos esenciales para trabajar eficazmente en analítica del fútbol. Los alumnos construirán una base sólida explorando medidas de tendencia central, variabilidad, distribuciones de probabilidad, desviaciones estándar e intervalos de confianza: los fundamentos que sustentan todo el razonamiento analítico en el deporte. A través de ejemplos específicos del fútbol, el curso explica cuándo utilizar distintos estimadores, cómo interpretar la incertidumbre y por qué es crucial elegir la distribución correcta al modelar rendimiento y eventos del partido. Más allá de la estadística, los alumnos descubrirán el ecosistema de datos del fútbol: datos de conteo, GPS, datos de eventos y datos esqueléticos, y comprenderán cómo se recopila, estructura y utiliza cada tipo en el análisis profesional. El curso también introduce herramientas clave como APIs, web scraping, Python, estructuras de datos y principios de visualización con Tableau, Power BI y Matplotlib. Al finalizar, los alumnos dominarán la intuición matemática, los fundamentos técnicos y las competencias de alfabetización de datos necesarias para analizar el fútbol con rigor y avanzar hacia técnicas más avanzadas de modelado.
Google データアナリティクス プロフェッショナル認定プログラムの最初のコースです。各コースでは、初歩的なデータ アナリスト業務に必要なスキルを習得します。あらゆる組織で、プロセスの改善、商機とトレンドの見極め、新製品のリリース、慎重な意思決定などに、データ アナリストが必要とされています。このコースでは、Google が開発した実践的なカリキュラムを通じてデータ アナリティクスの世界を紹介します。教材では、データ アナリティクスに関する多数の主要トピックに触れながら、Google データアナリティクス プロフェッショナル認定プログラムの概要がわかるよう工夫されています。現職の Google データ アナリストが、最適なツールやリソースを使って、一般的なアナリスト業務を遂行する実践的な方法を指導します。 この認定プログラムを修了すると、エントリーレベルのデータ アナリスト職に応募できるようになります。過去の業務経験は不要です。 このコース修了後の目標は以下の通りです。 ジュニア データ アナリストやアソシエート データ アナリストが日常的に関わる業務やプロセスを理解できるようになる。 専門的なツールボックスに追加できる、主要な分析スキル（データ クリーニング、データ分析、データの可視化）とツール（スプレッドシート、SQL、R プログラミング、Tableau）を習得する。 データのライフサイクルやデータ分析プロセスなど、ジュニア データ アナリストの業務に関わる数多くの用語や概念を理解できるようになる。 データ エコシステムにおけるアナリティクスの役割を評価できるようになる。 分析的思考について自己診断ができるようになる。 コース修了後、求人情報を検索でき、求職活動のベストプラクティスを知る。
Coursera
Unlock the power of cohort analysis to transform raw user data into actionable retention insights that drive business growth. This course empowers data professionals to systematically segment users by acquisition channels, calculate meaningful retention metrics, and diagnose the true drivers behind user churn patterns. This Short Course was created to help data analysts accomplish strategic user retention optimization through advanced cohort analysis techniques. By completing this course, you'll be able to confidently build Looker explores that reveal sticky user segments, overlay retention curves with business events to identify seasonal patterns, and distinguish between temporary user fatigue and long-term engagement decline. These skills enable you to provide data-driven recommendations that directly impact product-market fit and marketing spend optimization. By the end of this course, you will be able to: Apply cohort analysis to calculate user retention segmented by acquisition channel Analyze retention curves to distinguish between user fatigue and seasonal effects This course is unique because it combines hands-on technical implementation in Looker with strategic business analysis, enabling you to bridge the gap between data extraction and actionable business insights. To be successful in this course, you should have a background in data analysis fundamentals and basic familiarity with business intelligence tools.
هذه هي الدورة التدريبية الأولى في شهادة تحليلات البيانات من Google. ستزودك هذه الدورات بالمهارات التي تحتاجها للتقدم لوظائف محلل البيانات على المستوى التمهيدي. تحتاج المؤسسات من جميع الأنواع إلى محللي بيانات لمساعدتها على تحسين عملياتها، وتحديد الفرص والاتجاهات، وإطلاق منتجات جديدة، واتخاذ قرارات مدروسة. في هذه الدورة التدريبية، ستتعرف على عالم تحليلات البيانات من خلال المناهج العملية التي طورتها Google. تغطي المواد التي تمت مشاركتها الكثير من موضوعات تحليلات البيانات الرئيسية، وهي مصممة لتوفر لك نظرة عامة على ما سيأتي في شهادة تحليلات البيانات من Google. سيقوم محللو بيانات Google الحاليون بإرشادك وتزويدك بالطرق العملية لإنجاز مهام محلل البيانات الشائعة باستخدام أفضل الأدوات والموارد. سيتم تجهيز المتعلمين الذين يكملون برنامج الشهادة هذا للتقدم لوظائف المستوى التمهيدي كمحللين بيانات. لا تلزم خبرة سابقة. بنهاية هذه الدورة، سوف: تكتسب فهم للممارسات والعمليات التي يستخدمها محلل بيانات مبتدئ أو مشارك في وظيفتهم اليومية. تتعرّف على المهارات التحليلية الأساسية (تنظيف البيانات، وتحليل البيانات، ومؤثرات عرض البيانات) والأدوات (جداول البيانات، SQL، برمجة R، Tableau) التي يمكنك إضافتها إلى صندوق أدواتك الاحترافي. تكتشف مجموعة متنوعة من المصطلحات والمفاهيم ذات الصلة بدور محلل البيانات المبتدئ، مثل دورة حياة البيانات وعملية تحليل البيانات. تقيمّ دور التحليلات في النظام الشامل للبيانات. تقوم بإجراء تقييم ذاتي للتفكير التحليلي. تكتشف فرص العمل المتاحة لك عند الانتهاء من البرنامج، وتتعرّف على أفضل الممارسات في البحث عن وظيفة.
University of Colorado Boulder (via Coursera)
Fundamental Neural Pathways For Movement is the second course of the specialization "Science of Movement". This course will provide you with a deeper understanding of the intricate processes that govern our ability to move and perform complex motor tasks. In this course you will learn how generation of the activation signals required for movement can involve different parts of the nervous system. The discussion begins with the simplest neural pathways, those involving reflexes. Despite the simplicity of these pathways, the responses they produce depend on the context in which they are activated. The second type of movement to be considered will be the automatic behaviors produced by rhythmic activation signals. You will learn that these signals are constrained by the biomechanical requirements for the movement, they are modulated by the information received by the central nervous system from sensory receptors, and they are controlled by different parts of the brain. By the end of this course, you will have gained a solid understanding of the neural pathways that underlie reflexes, locomotion, and intentional actions. You will appreciate the remarkable complexity and organization of our nervous system, and how it enables us to interact with the world around us. Whether you are interested in sports science, rehabilitation, or neurology, this course will equip you with the knowledge and skills to excel.
Johns Hopkins University (via Coursera)
This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This course will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.
Meta
Develop a working knowledge and familiarity with advanced database concepts such as usage, modeling, automation, storage, optimization and administration. To take this course, you must have completed the previous Database courses. You must also be eager to continue your journey with coding. The Professional Certificates create opportunities so that anyone regardless of education, background or experience can learn high-quality skills to land a high-growth career—no degree or experience required to get started. By the end of this course, you’ll be able to: Deploy basic data modeling skills and navigate modern storage options for a data warehouse Apply planning and execution of ETL style database engineering by building upon existing MySQL skills Develop a working knowledge of the different aspects of managing a database including administration of database operations and concerns, alongside processes and solutions for monitoring, reporting and debugging Demonstrate data modeling skills within a real-world project environment You’ll gain experience with the following tools and software: Workbench data modeling software Syntaxes used to interact with a data warehouse Extract, transform and loading (ETL) techniques and methods MySQL data warehouse administration Data warehouse monitoring and reporting tools Database debugging and testing tools Data modeling tools
Coursera
Imagine you’re tasked with solving a complex challenge that demands both strategic thinking and hands-on expertise. How do you approach it confidently? In this course, you will be guided through essential concepts and practical applications, empowering you to tackle real-world problems effectively. This course equips you with in-depth knowledge, interactive exercises, and actionable skills designed for immediate impact in your field. By the end of this course, you will have developed a robust understanding of key principles, gained experience with proven strategies, and be prepared to implement solutions in dynamic environments. Learners should be familiar with basic Python, SQL, basic PySpark, data engineering fundamentals, streaming concepts, and data quality awareness. This course is designed for intermediate data engineers, analytics engineers, and BI professionals who want to build reliable real-time data pipelines with automated quality checks and executive-ready dashboards using Microsoft Fabric, PySpark, and Power BI. By the end of this course, you'll be ready to apply what you’ve learned to drive results and adapt to evolving challenges with confidence.
Google 데이터 애널리틱스 수료증 과정의 첫 번째 강좌입니다. 이 강좌에서는 데이터 애널리스트 직무에 필요한 입문 수준의 스킬을 배우게 됩니다. 모든 기업에는 절차를 개선하고, 기회와 추세를 식별하고, 신제품을 출시하고, 신중한 결정을 내리는 데 도움을 줄 데이터 애널리스트가 필요합니다. 이 강좌에서는 Google에서 개발한 실습형 커리큘럼을 통해 데이터 애널리틱스 세계의 기본사항을 배워봅니다. 핵심 데이터 애널리틱스 주제를 다룬 학습 자료에서 Google 데이터 애널리틱스 수료증 과정의 내용을 간략하게 살펴볼 수 있습니다. 현직 Google 데이터 애널리스트가 실습을 통해 최고의 도구와 리소스를 사용하여 일반적인 데이터 애널리스트 작업을 완료하는 방법을 제시하고 지도합니다. 이 수료증 과정을 완료한 수강생은 데이터 애널리스트로서 입문 수준의 직무에 지원할 역량을 갖추게 됩니다. 관련 경험은 필요하지 않습니다. 이 강좌의 목표는 다음과 같습니다. 주니어 또는 어소시에이트 데이터 애널리스트의 일상 업무 관행과 절차 이해 전문성을 강화하는 데 도움이 되는 핵심 분석 스킬(데이터 정리, 데이터 분석, 데이터 시각화)과 도구(스프레드시트, SQL, R 프로그래밍, Tableau) 학습 데이터 라이프 사이클 및 데이터 분석 과정 등 주니어 데이터 애널리스트의 역할과 관련된 다양한 용어와 개념 학습 데이터 생태계에서 애널리틱스의 역할 평가 분석적 사고의 자체 평가 실시 본 수료증 과정 완료 시 이용 가능한 취업 기회 확인 및 구직 권장사항 학습
Genentech
This course is aimed to demonstate how principles and methods from data science can be applied in clinical reporting. By the end of the course, learners will understand what requirements there are in reporting clinical trials, and how they impact on how data science is used. The learner will see how they can work efficiently and effectively while still ensuring that they meet the needed standards.
Este é o primeiro curso do Certificado de análise de dados do Google. Estes cursos darão a você as habilidades necessárias para se candidatar em empregos de analista de dados de nível introdutório. Organizações de todos os tipos precisam de analistas de dados para ajudá-las a melhorar os processos, identificar oportunidades e tendências, lançar novos produtos e tomar decisões conscientes. Neste curso, você conhecerá o mundo da análise de dados com um currículo prático desenvolvido pelo Google. O material compartilhado abrange muitos tópicos importantes de análise de dados e foi projetado para oferecer uma visão geral do que está por vir no Certificado de análise de dados do Google. Os analistas de dados do Google vão instruir e oferecer maneiras práticas de realizar tarefas comuns de analistas de dados com as melhores ferramentas e recursos. Os alunos que concluírem este programa de certificação poderão se candidatar a empregos de nível introdutório para analista de dados. Nenhuma experiência anterior é necessária. Ao final deste curso, você poderá: Ter uma compreensão das práticas e dos processos utilizados por um analista de dados júnior ou associado no trabalho cotidiano. Aprender sobre as principais habilidades (limpeza, análise e visualização de dados) e ferramentas (planilhas, SQL, programação R, Tableau) analíticas que você pode incluir às ferramentas profissionais que já tem. Descobrir uma ampla variedade de termos e conceitos relevantes para a função de analista de dados júnior, como o ciclo de vida e o processo de análise de dados. Avaliar o papel da análise no ecossistema de dados. Realizar uma autoavaliação de pensamento analítico. Explorar as oportunidades de emprego disponíveis após a conclusão do programa e aprender sobre as práticas recomendadas na procura de uma vaga.
Coursera
In this course, you’ll gain the skills to turn complex data into actionable insights with ThoughtSpot’s intuitive analytics platform. Imagine navigating through a business meeting where the future strategies hinge on last quarter's data analysis. This course will empower you to bypass the limitations of traditional dashboards and get the critical insights you need instantly. Using interactive dashboards, AI-powered analytics, and real-world scenarios, you’ll gain the confidence to make data-driven decisions and elevate business performance. This course is ideal for Data Analysts, BI Professionals, Data Science Students, Decision-Makers, IT Engineers, Marketing Analysts, Financial Analysts, and Operations Managers. Learners should have basic understanding of business intelligence, data analysis, database management, cloud computing, AI, machine learning concepts, and data visualization basics. By the end, you won’t just know how to navigate ThoughtSpot; you’ll be able to transform raw data into strategic intelligence that drives success in your role. Whether you're a beginner or someone looking to advance your BI skills, this course offers the tools, knowledge, and confidence you need to succeed in today’s data-driven world. Get ready to take control of your data and unlock insights that will shape the future of your business.
IBM
Ready to start a career in Data Analysis but don’t know where to begin? This course presents you with a gentle introduction to Data Analysis, the role of a Data Analyst, and the tools used in this job. You will learn about the skills and responsibilities of a data analyst and hear from several data experts sharing their tips & advice to start a career. This course will help you to differentiate between the roles of Data Analysts, Data Scientists, and Data Engineers. You will familiarize yourself with the data ecosystem, alongside Databases, Data Warehouses, Data Marts, Data Lakes and Data Pipelines. Continue this exciting journey and discover Big Data platforms such as Hadoop, Hive, and Spark. By the end of this course you’ll be able to understand the fundamentals of the data analysis process including gathering, cleaning, analyzing and sharing data and communicating your insights with the use of visualizations and dashboard tools. This all comes together in the final project where it will test your knowledge of the course material, and provide a real-world scenario of data analysis tasks. This course does not require any prior data analysis, spreadsheet, or computer science experience.
Universidad Nacional Autónoma de México (via Coursera)
Welcome to the specialization course Business Intelligence and Data Warehousing. This course will be completed on six weeks, it will be supported with videos and various documents that will allow you to learn in a very simple way how to identify, design and develop analytical information systems, such as Business Intelligence with a descriptive analysis on data warehouses. You will be able to understand the problem of integration and predictive analysis of high volume of unstructured data (big data) with data mining and the Hadoop framework. After completing this course, a learner will be able to ● Create a Star o Snowflake data model Diagram through the Multidimensional Design from analytical business requirements and OLTP system ● Create a physical database system ● Extract, Transform and load data to a data-warehouse. ● Program analytical queries with SQL using MySQL ● Predictive analysis with RapidMiner ● Load relational or unstructured data to Hortonworks HDFS ● Execute Map-Reduce jobs to query data on HDFS for analytical purposes Programming languages: For course 2 you will use the MYSQL language. Software to download: Rapidminer MYSQL Excel Hortonworks Hadoop framework In case you have a Mac / IOS operating system you will need to use a virtual Machine (VirtualBox, Vmware).
Genentech
The aim of this course is to introduce learners to open-source R packages that can be used to perform clinical data reporting tasks. The main emphasis of the course will be the clinical data flow from raw data (both CRF and non-CRF) to SDTM to ADaM to final outputs. While several open-source tools to complete these tasks will be introduced, the objective of this course is not to become an expert in any of these tools but rather to introduce participants to the broader concepts behind these tasks. That way the tools simply serve as an example of how the underlying concepts could be put into action in code.
Coursera
This course is an advanced level course designed for learners who want to use Qlik Sense to perform sophisticated data analytics, build dashboards, and communicate full reports and stories from their data. These advanced concepts include more than just visualization features such as dynamic filtering and conditional formatting, but more so data functionality such as advanced expressions, drill-downs, leads, lags, and more. This is important as these skills are directly required when creating sophisticated business analyses and dashboards. This course demonstrates the advanced functionalities of Qlik Sense. The course starts with advanced data analysis techniques, including both strict analysis like calculated fields, set analysis, etc. It then goes through advanced visualization and dashboard functionality, like dynamic filters, drill-downs, lags, leads, and more. Finally, the course demonstrates how to collaborate with other people, through sharing, bookmarking, and embedding dashboards and reports. "Advanced Data Analysis and Collaboration with Qlik Sense" is the third and final course in our series, building upon the foundations laid in "Data Ingestion, Exploration, and Visualization with Qlik Sense" and "Introduction to Data Visualization with Qlik Sense." Together, these courses provide a comprehensive skill set for leveraging Qlik Sense as a powerful tool for data analytics, empowering you to analyze data effectively, create impactful visualizations, and generate valuable insights for a wide range of business purposes, from performance tracking to decision-making and beyond. This program is designed for anyone interested in mastering advanced data analytics and visualization concepts with Qlik Sense. These include data and business analysts who regularly analyze and communicate with data. To excel in "Advanced Data Analysis and Collaboration with Qlik Sense," learners should have a solid grasp of Qlik Sense, including basic data analysis concepts and...
University of Minnesota (via Coursera)
In this data-driven world, companies are often interested in knowing what is the "best" course of action, given the data. For example, manufacturers need to decide how many units of a product to produce given the estimated demand and raw material availability? Should they make all the products in-house or buy some from a third-party to meet the demand? Prescriptive Analytics is the branch of analytics that can provide answers to these questions. It is used for prescribing data-based decisions. The most important method in the prescriptive analytics toolbox is optimization. This course will introduce students to the basic principles of linear optimization for decision-making. Using practical examples, this course teaches how to convert a problem scenario into a mathematical model that can be solved to get the best business outcome. We will learn to identify decision variables, objective function, and constraints of a problem, and use them to formulate and solve an optimization problem using Excel solver and spreadsheet.
Coursera
This course is an intermediate-level course designed for learners who want to continue their data visualization journey with Qlik Sense, a powerful sophisticated Business Intelligence tool. Data preparation and ingestion is a key prerequisite for data visualization, and this course not only dives deep into that concept, but other important intermediate topics such as filters, expressions, and personalization. The second of a series of three courses on Qlik Sense emphasizes the significance of moving beyond basic chart creation. In today's data-driven landscape, businesses and professionals require more than simple visualizations to gain a competitive edge. They need to develop a system of highly customized and tailored dashboards that precisely align with their unique data needs. These dashboards serve as powerful tools for effective decision-making and data-driven insights. For example, marketing teams can benefit from dashboards that provide real-time metrics on campaign performance, audience engagement, and conversion rates. This course provides a comprehensive understanding of data visualization with Qlik Sense. It begins with data ingestion and visualization, including how to load data from various sources, how to clean data, and how to build data models. The course then goes through how to create sophisticated charts and dashboards, with intermediate concepts like filters and expression. Finally, the course demonstrates how to apply personalization to tailor the functionality and look of the dashboard. This course is tailored for those looking to advance their data visualization skills with Qlik Sense. It's ideal for data analysts seeking to craft compelling narratives or arguments through data, as well as individuals aiming to effortlessly transform datasets into actionable graphs using Qlik Sense. To excel in this course, intermediate-level learners should have the following prerequisites: a basic grasp of Qlik Sense, familiarity with simple Excel f...
Coursera
Did you know that 78% of healthcare facilities still rely on manual inventory tracking, leading to costly stockouts and expired supplies? This course transforms healthcare administration professionals into confident clinic operations specialists who can navigate organizational structures, decode performance metrics, and maintain critical supply levels. This Short Course was created to help health administration professionals accomplish seamless clinic operations through systematic organizational knowledge and inventory control. By completing this course, you'll be able to confidently identify the right personnel for any operational issue, discuss KPI trends with supervisors using precise terminology, and prevent supply disruptions through proactive inventory management that you can apply immediately in your daily work. By the end of this course, you will be able to: Navigate reporting structures to escalate issues appropriately. Interpret and communicate essential operational KPIs. Execute inventory procedures to maintain optimal stock levels. This course is unique because it combines organizational navigation, performance measurement, and supply management into one comprehensive foundation that mirrors real clinic workflows. To be successful in this project, you should have a background in healthcare environments and basic administrative experience.
Coursera
Did you know that poorly designed data models are responsible for 80% of business intelligence project failures? This Short Course was created to help data management and engineering professionals accomplish effective self-service BI reporting through dimensional modeling. By completing this course, you'll be able to design intuitive star schema data models that eliminate complex joins, accelerate query performance, and empower business users to drag-and-drop fields directly in visualization tools like Tableau. You'll master the foundational skills of identifying business processes, structuring fact and dimension tables, and implementing surrogate key relationships. By the end of this course, you will be able to: Create a star schema data model to enable self-service business intelligence reporting This course is unique because it bridges the gap between technical data engineering and business user needs, focusing on practical implementation that directly supports analyst workflows and dashboard creation. To be successful in this project, you should have a background in basic database concepts and familiarity with SQL queries.
Whizlabs
Welcome to Microsoft Fabric: Monitor and Optimize Analytics Solutions, an advanced and hands-on course designed for data professionals who want to master monitoring, performance tuning, and troubleshooting within Microsoft Fabric’s unified analytics platform. This course teaches you how to ensure that Fabric workloads remain reliable, performant, and optimized for enterprise-scale analytics. This advanced course is designed for data engineers and analytics professionals who want to master performance optimization, monitoring, and troubleshooting within Microsoft Fabric. Throughout the course, you’ll explore how to design scalable semantic models, optimize enterprise-scale workloads, diagnose ingestion and transformation issues, and accelerate performance for lakehouses, Spark environments, event streams, and data warehouses. With 3+ hours of focused video content, the course blends conceptual understanding with real-world demonstrations inside Fabric. You will learn how to tune DAX, improve query performance, optimize pipelines, resolve Eventstream/Eventhouse errors, and manage large-scale data storage. Each module includes interactive quizzes and in-video checkpoints to reinforce learning. Enroll in Microsoft Fabric: Optimize, Monitor, and Troubleshoot Data Solutions to gain the skills needed to improve system reliability, maximize performance efficiency, and support enterprise-grade data workloads in Microsoft Fabric. Course Modules Module 1: Data Modeling and Optimization in Microsoft Fabric: Module 2: Monitoring, Optimization, and Troubleshooting in Microsoft Fabric Module 3: Data Engineering and Performance Optimization in Microsoft Fabric Recommended Background A basic understanding of Microsoft Fabric components such as Lakehouses, Warehouses, Pipelines, and Eventstreams. Familiarity with core data engineering concepts - data ingestion, transformation, modeling, and analytics workflows. Working knowledge of SQL or experience with Power BI; exposure to DA...
Coursera
Working with spatial data means more than making maps—it means producing results that planners and decision-makers can trust. In this short, hands-on course, Crunch Vectors with GeoPandas, learners practice transforming raw vector data into map-ready, planning-quality insights using GeoPandas. Through realistic examples and guided activities, learners perform spatial joins between cities and counties, choose spatial relationships intentionally, reproject data to EPSG:3857 for web mapping, and summarize attributes into clear service-territory totals. Designed for beginner data analysts, this course builds confidence in spatial reasoning, validation, and aggregation—helping learners deliver datasets that support accurate mapping, reporting, and real-world planning decisions.
Ini adalah materi pertama dalam program Sertifikasi Data Analitik Google. Materi ini akan membekali Anda dengan keterampilan yang Anda butuhkan untuk melamar kerja sebagai analis data tingkat pemula. Semua jenis organisasi membutuhkan analis data untuk memperbaiki proses operasional mereka, mengidentifikasi peluang dan tren, meluncurkan produk baru, dan membuat keputusan yang tepat. Di materi ini, Anda akan diperkenalkan dengan dunia analitik data berdasarkan kurikulum yang langsung dikembangkan oleh Google. Materi yang dibahas mencakup berbagai topik penting dalam analisis data, dan dirancang untuk memberi gambaran umum tentang apa yang akan Anda pelajari dalam program Sertifikasi Analitik Data Google. Analis data Google akan mengajarkan dan memberi tahu Anda cara untuk menyelesaikan tugas analis data yang umum dengan menggunakan alat dan sumber daya terbaik. Peserta didik yang menyelesaikan program sertifikasi ini akan memiliki bekal yang cukup untuk melamar kerja sebagai analis data tingkat pemula. Tidak membutuhkan pengalaman apa pun. Di akhir materi ini, Anda akan Mendapatkan pemahaman tentang praktik dan proses yang digunakan oleh analis data junior sehari-hari. Mempelajari tentang keterampilan analitis utama (pembersihan data, analisis data, visualisasi data) dan alat (spreadsheet, SQL, pemrograman R, Tableau) yang dapat ditambahkan di set perangkat profesional Anda. Menemukan berbagai macam istilah dan konsep yang berhubungan dengan pekerjaan analis data junior, seperti siklus hidup data dan proses analisis data. Mengevaluasi peran analitik dalam ekosistem data. Melakukan penilaian mandiri dengan pola pikir analitis. Menelusuri peluang kerja yang tersedia setelah Anda menyelesaikan program ini, dan mempelajari tentang praktik baik dalam pencarian kerja.
Tableau Learning Partner (via Coursera)
The Business Analysis Process course will give you a foundational understanding of the process of business analysis and will introduce you to a framework that can be used within a variety of industries and organizations. You’ll see how an analyst assesses a business problem, prepares business requirements, and implements a solution. You will learn how to identify stakeholders, to analyze them, and to define their role in a business analysis project. You’ll also learn how to gather requirements from these stakeholders, analyze them, and create a business requirements document that conforms to industry best practices. Lastly, you’ll explore the functions and practical uses of crafting visual models, gaining hands-on experience in creating common models used by business intelligence analysts. This course is for anyone who is curious about entry-level roles that demand fundamental Tableau skills, such as business intelligence analyst or data reporting analyst roles. It is recommended (but not required) that you have some experience with Tableau Public, but even if you're new to Tableau Public, you can still be successful in this program. By the end of the course, you will be able to: -Demonstrate an in-depth knowledge of the business analysis process. -Describe the methods used to identify stakeholders, and define their role in a business analysis project. -Describe the methods used for gathering requirements from stakeholders. -Create a business requirements document that conforms to industry best practices. -Create a visual model of a business process.
Coursera
Within this 1-hour long guided project you will learn how to create decision-support interactive dashboards merging economic and spatial data with Qlik Sense. These types of dashboards graphically represent the best locations for data-driven decisions. You will learn how to: Connect multiple distinct data sources to a common data pool Elaborate interactive data visualizations and perform data discoveries Display spatial data using data density overlaid on a map You will build an interactive dashboard to support the decision on where are the best spots to setup a new petrol station. To facilitate this decision, you will display all current petrol stations on a interactive map with their historical prices. This dashboard will assist in identifying the most suitable locations for each customer segment to set up a new petrol station. The course is aimed to provide a guided introduction on how to create interactive dashboards with Qlik Sense and no previous knowledge is required. After completing the course you will be able to create decision-support interactive dashboards to support data-driven decisions with minimal cognitive load by decision makers.
Universidad Austral (via Coursera)
La toma de decisiones está en la esencia de los negocios. Gerenciar es tomar decisiones, muchas veces bajo presión, con información desordenada y en un contexto de incertidumbre. Un aspecto básico es entender y analizar la información, organizar los datos de forma de facilitar su posterior uso y la toma de decisiones. Si bien hay muchas otras dimensiones que entran en juego, el primer paso es formular bien el problema, estructurarlo y procesar la información. En este sentido, el principal objetivo de este curso es ayudarlo a ser un mejor tomador de decisiones a través de herramientas técnicas. A lo largo de este curso el alumno desarrollará habilidades cuantitativas para la toma de decisiones, a través del aprendizaje de métodos estadísticos con aplicaciones a los negocios en Excel. El foco está en la comprensión y en el uso de herramientas básicas de análisis e inferencia estadística tratando de que el alumno sea un usuario de estos métodos, comprenda en qué consisten, cuál es la intuición, su uso y aplicaciones.
Johns Hopkins University (via Coursera)
In the course "Advanced Techniques in Data Visualization", you will explore advanced data visualization techniques that will elevate your ability to communicate complex data. Building upon foundational skills, you’ll learn to harness the power of color, interactivity, and specialized visualization methods, such as hierarchical structures, networks, and geospatial data. The course covers the essential role of color theory in visualization, teaching you how to enhance data clarity and accessibility. You will also dive into the world of interactive visualizations, gaining practical experience in creating user-driven data experiences. As you explore hierarchical and network visualizations, you'll discover how to represent complex relationships in a way that is easy to understand. The course will guide you through the principles of mapping data, allowing you to transform spatial data into compelling visual narratives. Finally, you will learn to visualize textual data, uncovering patterns and insights that might otherwise remain hidden. With hands-on experience using popular tools such as Tableau and Power BI, this course prepares you to create sophisticated, effective, and impactful visualizations for any audience.