Transform raw business data into actionable insights using automated, no-code AI solutions. Forge predictive models, design custom dashboards, and communicate results to maximize professional influence. This course empowers learners to automate time-consuming data processes, visualize trends for decision-makers, and drive change in both emerging and mature markets, including region-specific approaches for India, the USA, and Spanish-speaking learners.
This hands-on course equips learners with the skills to design, construct, and deploy a web-based Email Marketing Tool using Java Servlets, HTML, CSS, JDBC, and the JavaMail API. Through a real-world project, students will begin by outlining the front-end layout and form structure, then proceed to build dynamic servlets that interact with a MySQL database to retrieve employee data. Learners will then develop and integrate both frontend and backend logic to send automated emails, concluding with a working, browser-accessible tool. Emphasis is placed on practical understanding, modular development, and full-stack integration in Java-based web applications.
By the end of this course, learners will be able to:
• Plan and organize form-driven front-end interfaces for a web-based tool.
• Develop servlets that process requests and connect to backend systems.
• Apply JDBC techniques to retrieve and manipulate data from a MySQL database.
• Integrate the JavaMail API to send customized emails programmatically.
• Evaluate output through real-time testing and project walkthroughs.
This course focuses on how to lead with purpose, make better decisions and influence outcomes in complex environments. You’ll learn how to define clear direction, set effective goals and apply structured approaches to decision-making and problem-solving.
You’ll also explore how emotional intelligence, mentoring and psychological insight support strong relationships and leadership impact. By the end of this course, you’ll be able to apply practical leadership strategies, which improve performance, strengthen trust and help you lead with confidence.
In this short course, you’ll learn how to train and evaluate machine learning models with confidence. You’ll explore how mini-batch training and learning-rate schedulers shape convergence, how to read loss curves and logs to diagnose issues, and how class-imbalance techniques affect F1 scores. Through hands-on PyTorch practice, you’ll train models, investigate instability, and compare weighting and SMOTE. By the end, you’ll understand how to guide models toward stable, reliable performance.
Automation, Platform Trends, and Social Sales Growth
Stay ahead of the digital curve by mastering the latest automation tools, sales platforms, and perpetual upskilling strategies. This course prepares you to futureproof your social selling practice through trend monitoring, multi-platform selling—including WhatsApp, Threads, and emerging platforms—and continuous improvement cycles. Embrace innovative workflows, ensure compliance with evolving laws, and deliver sales excellence as technology and buyer behavior evolve worldwide.
In the world of business and organizations, nothing is more important than having an effective strategy. Why? Because without knowing our destination, we cannot determine the right path to get there. This course introduces the two sides of a strategy: competitive strategy and corporate strategy. Competitive strategy has to do with the environment in which firms and organizations are operating and how they can develop and sustain their competitive advantage. On the other hand, corporate strategy has to do with the specific strategies firms undertake to sustain their competitive advantage, such as product diversification, internationalization, and non-market strategies.
This course is best suited for individuals seeking to advance their careers through skill-building, industry application, and network expansion. Whether aiming for a promotion, transitioning to a new career, or growing one’s professional skills, learners will gain valuable insights into how they can contribute to their organizations and articulate those ideas with peers, recruiters, and other stakeholders.
In this course you learn to build, refine, extrapolate, and, in some cases, interpret models designed for a single, sequential series. There are three modeling approaches presented. The traditional, Box-Jenkins approach for modeling time series is covered in the first part of the course. This presentation moves students from models for stationary data, or ARMA, to models for trend and seasonality, ARIMA, and concludes with information about specifying transfer function components in an ARIMAX, or time series regression, model. A Bayesian approach to modeling time series is considered next. The basic Bayesian framework is extended to accommodate autoregressive variation in the data as well as dynamic input variable effects. Machine learning algorithms for time series is the third approach. Gradient boosting and recurrent neural network algorithms are particularly well suited for accommodating nonlinear relationships in the data. Examples are provided to build intuition on the effective use of these algorithms.
The course concludes by considering how forecasting precision can be improved by combining the strengths of the different approaches. The final lesson includes demonstrations on creating combined (or ensemble) and hybrid model forecasts.
This course is appropriate for analysts interested in augmenting their machine learning skills with analysis tools that are appropriate for assaying, modifying, modeling, forecasting, and managing data that consist of variables that are collected over time.
This course uses a variety of different software tools. Familiarity with Base SAS, SAS/ETS, SAS/STAT, and SAS Visual Forecasting, as well as open-source tools for sequential data handling and modeling, is helpful but not required. The lessons on Bayesian analysis and machine learning models assume some prior knowledge of these topics. One way that students can acquire this background is by completing these SAS Education courses: Bayesian Analyses Using SAS and Machine Learning Using SAS Viya.
Trust and Security with Google Cloud - Português Brasileiro
Conforme as organizações movem os próprios dados e aplicativos para a nuvem, novos problemas de segurança podem aparecer. No curso ""Trust and Security with Google Cloud"", explicamos os conceitos básicos de segurança na nuvem, o valor da abordagem multicamadas do Google Cloud para a proteção da infraestrutura e como o Google conquista e mantém a confiança dos clientes na nuvem.
Como parte do programa de aprendizado do Cloud Digital Leader, o objetivo deste curso é ajudar você a crescer profissionalmente e desenvolver o futuro do seu próprio negócio.
In this course, you will gain insights into how analyzing income statements, balance sheets, and cash flow statements can provide a comprehensive understanding of a company’s financial performance and condition. You will apply these concepts by conducting detailed financial statement analyses and interpreting the results to make informed business decisions.
This course is ideal for non-financial managers or professionals who seek to enhance their financial acumen to make better business decisions. No prior experience in finance is required. However, a basic understanding of business operations and strategic thinking will be beneficial.
In this course, learn how Python was built in C and, by extension, languages like Java and JavaScript. Review object-oriented programming syntax from several languages and use your knowledge of C to build a library that integrates high-level data structures like strings, lists, and dictionaries. Learn how to make your program run as efficiently as possible by covering memory allocation, de-allocation, and object lifecycle.
This is a self-paced lab that takes place in the Google Cloud console. This lab shows you how to get started with Dataplex in the Google Cloud console by walking you through creating a lake, adding a zone, attaching and detaching assets, and deleting zones and lakes.
