Description Are you ready to learn Apache Spark the practical way — by analyzing 120+ years of real Olympic Games data? In this hands-on project-based course, you’ll use Apache Spark, Spark SQL, and Apache Zeppelin to explore the world’s most exciting sports dataset — the Olympic Games Dataset, containing information on athletes, countries, events, medals, ages, genders, heights, and weights spanning from 1896 to recent editions. Instead of learning Spark through boring theory, you’ll build a complete analytics project step by step, uncovering insights like: What You Will Learn By the end of this course, you’ll confidently be able to: Tools You’ll Use Tools                                             Purpose Apache Spark                              Big Data Processing Spark SQL                                    Querying and Analysis Apache Zeppelin                         Interactive Notebooks & Visualization Docker / Java                              Environment Setup Don’t worry if you’ve never installed Spark before — we guide you through Java installation, Docker setup, Zeppelin configuration, and Spark Interpreter connection — all step by step. Who is This Course For? This course is beginner-friendly and perfect for: No prior Spark experience is required — just basic familiarity with SQL or Python is enough to get started. Final Output — A Real Big Data Analytics Project By the end, you’ll build and present a complete Olympic Analytics Project — something you can proudly showcase on LinkedIn, GitHub, or your Resume. If you want to master Apache Spark with a fun, engaging, and real-world dataset — this course is for you. Enroll now and let’s analyze Olympic history with Big Data power! Who this course is for:

Description Become a front-end development expert with this comprehensive course that covers JavaScript, jQuery, and TypeScript. Whether you’re a beginner or an experienced developer looking to refine your skills, this course provides the knowledge and practical experience you need to build dynamic, interactive, and responsive web applications. Starting with JavaScript, you’ll learn the core language that powers the web, mastering everything from basic syntax to advanced concepts like closures and asynchronous programming. You’ll then explore jQuery, the popular JavaScript library, to simplify DOM manipulation, event handling, and animation. Finally, you’ll dive into TypeScript, a powerful typed superset of JavaScript that enhances code quality and development efficiency. Through hands-on projects, real-world examples, and expert guidance, this course will equip you with the skills to create robust front-end applications that deliver a seamless user experience. By the end of the course, you’ll have a deep understanding of JavaScript, jQuery, and TypeScript, making you a versatile and highly sought-after front-end developer. What You’ll Learn: Who This Course is For: Enrolling in this course will equip you with the essential skills to excel in front-end development. By mastering JavaScript, jQuery, and TypeScript, you’ll be able to build dynamic and responsive web applications that stand out in today’s competitive digital landscape. Start your journey to front-end mastery today! Who this course is for:

Description Master the art of database management and development with this comprehensive course that covers SQL, MySQL, PostgreSQL, and MongoDB. Whether you’re a beginner looking to understand the basics or an experienced developer aiming to expand your database expertise, this course provides everything you need to manage, optimize, and secure databases across multiple platforms. Starting with SQL, you’ll learn the fundamentals of database querying and data manipulation. You’ll then dive into MySQL and PostgreSQL, exploring advanced features, performance optimization, and complex data structures. Finally, you’ll explore MongoDB, the leading NoSQL database, to understand how to manage unstructured data and create flexible, scalable applications. This all-in-one course is packed with hands-on exercises, real-world projects, and expert guidance, ensuring you gain practical experience and the confidence to work with databases in any environment. By the end, you’ll have a strong command of both relational and non-relational databases, making you a versatile and in-demand professional in the field of data management. What You’ll Learn: Who This Course is For: By enrolling in this course, you’ll gain the skills to manage and optimize databases across different platforms, making you a valuable asset in any data-driven environment. Whether you’re looking to start a new career or upgrade your existing skills, this course offers everything you need to become a database expert. Start your journey to mastering databases today! Who this course is for:

Description Welcome to course: Python Development & Data Science: Variables and Data Types its the module of program “Professional Diploma in Python Development”You may join that course separately, or join the full program. Course provided by MTF Institute of Management, Technology and Finance MTF is the global educational and research institute with HQ at Lisbon, Portugal, focused on business & professional hybrid (on-campus and online) education at areas: Business & Administration, Science & Technology, Banking & Finance. MTF R&D center focused on research activities at areas: Artificial Intelligence, Machine Learning, Data Science, Big Data, WEB3, Blockchain, Cryptocurrency & Digital Assets, Metaverses, Digital Transformation, Fintech, Electronic Commerce, Internet of Things. MTF is the official partner of: IBM, Intel, Microsoft, member of the Portuguese Chamber of Commerce and Industry. MTF is present in 208 countries and has been chosen by more than 380,000 students. Course Author: Stanisław Marek Halkiewicz I am a seasoned educator specializing in the domains of econometrics, statistics, statistical programming, and mathematics, boasting an extensive track record exceeding three years. Notably, I have imparted my expertise to students hailing from prestigious global universities, solidifying my standing as a sought-after tutor. I have made significant contributions in research endeavors spanning statistics, econometrics, and statistical programming. Furthermore, my professional trajectory encompasses a diverse spectrum, ranging from project management to business intelligence and Data Science. Course Description: Course designed to introduce individuals to the world of Python programming. Python has gained immense popularity in recent years for its simplicity, versatility, and readability. This course is ideal for beginners who are new to programming. Importance For Companies: Versatility: Python is versatile and can be used for a wide range of applications, from web development and data analysis to artificial intelligence and automation. Companies can benefit from having Python developers who can adapt to various projects. Productivity: Python’s clean and readable syntax allows developers to write code more quickly and efficiently. This results in faster project development and reduced costs for companies. Large Ecosystem: Python has a vast ecosystem of libraries and frameworks, making it easier for companies to find solutions to their specific needs. This reduces the need for reinventing the wheel. Data Analysis and AI: Python is a go-to language for data analysis and machine learning. Companies can leverage Python to extract valuable insights from data and build AI-driven applications. Community and Support: Python has a strong and active community of developers. Companies can tap into this community for support, resources, and talent recruitment. Importance For Developers: Career Opportunities: Learning Python opens up a wide range of career opportunities in various industries. Python developers are in high demand, and their skills are transferable to different domains. Diversity of Roles: Python developers can work in roles such as web developer, data analyst, data scientist, machine learning engineer, and more. This diversity allows developers to explore different areas of interest. Ease of Learning: Python’s straightforward syntax makes it an ideal choice for beginners in programming. It’s also accessible to experienced developers looking to add another language to their skill set. Continuous Growth: Python is constantly evolving, with new libraries and frameworks being developed. This means that developers who invest in Python education can continue to grow their skill set over time. Remote Work Opportunities: Python development often allows for remote work, providing developers with flexibility in their careers. The course provides a foundation for individuals looking to embark on a career in programming or expand their skill set. Python’s significance in the tech industry, its versatility, and its demand in the job market make it a valuable asset for both companies and developers aiming to excel in today’s technology-driven world. Who this course is for:

Description In this course, you will become familiar with a variety of up-to-date financial analysis content, as well as algorithms techniques of machine learning in the Python environment, where you can perform highly specialized financial analysis. You will get acquainted with technical and fundamental analysis and you will use different tools for your analysis. You will learn the Python environment completely. You will also learn deep learning algorithms and artificial neural networks that can greatly enhance your financial analysis skills and expertise. This tutorial begins by exploring various ways of downloading financial data and preparing it for modeling. We check the basic statistical properties of asset prices and returns, and investigate the existence of so-called stylized facts. We then calculate popular indicators used in technical analysis (such as Bollinger Bands, Moving Average Convergence Divergence (MACD), and Relative Strength Index (RSI)) and backtest automatic trading strategies built on their basis. The next section introduces time series analysis and explores popular models such as exponential smoothing, AutoRegressive Integrated Moving Average (ARIMA), and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) (including multivariate specifications). We also introduce you to factor models, including the famous Capital Asset Pricing Model (CAPM) and the Fama-French three-factor model. We end this section by demonstrating different ways to optimize asset allocation, and we use Monte Carlo simulations for tasks such as calculating the price of American options or estimating the Value at Risk (VaR). In the last part of the course, we carry out an entire data science project in the financial domain. We approach credit card fraud/default problems using advanced classifiers such as random forest, XGBoost, LightGBM, stacked models, and many more. We also tune the hyperparameters of the models (including Bayesian optimization) and handle class imbalance. We conclude the book by demonstrating how deep learning (using PyTorch) can solve numerous financial problems. Who this course is for:

Description Due to the importance of data visualization, there are many pieces of training on this subject that teach basic and preliminary data visualization. But in this course, we will teach you how to visualize your data in an advanced way in Python. In this course, we even teach 3D visualization so that you can specifically visualize your data and draw graphs using Python codes. You can use this course to visualize your data for managers, scientific papers, work projects, university classes, personal websites, and even advertisements. In today’s world, a lot of data is being generated on a daily basis. And sometimes to analyze this data for certain trends, and patterns may become difficult if the data is in its raw format. To overcome this data visualization comes into play. Data visualization provides a good, organized pictorial representation of the data which makes it easier to understand, observe, and analyze. In this course, we will discuss how to advanced visualize data using Python. This is just one demonstration of the usefulness of data visualization that makes it so popular in data science. Let’s see some more reasons why data visualization is so important: 1. Data Visualization Discovers the Trends in Data 2. Data Visualization is Interactive 3. Data Visualization Provides a Perspective on the Data 4. Data Visualization Explains a Data Process 5. Data Visualization Strokes the Imagination 6. Data Visualization Tells a Data Story 7. Data Visualization Puts the Data into the Correct Context 8. Data Visualization is Educational for Users 9. Data Visualization Saves Time 10. Data Visualization Presents Data Beautifully All of these reasons demonstrate the importance of data visualization in data science. Basically, it is a much more user-friendly method to understand the data and also demonstrates the trends and patterns in the data to other people. And it doesn’t hurt that data visualization is beautiful to look at and so more appealing to people than rows of boring data! Who this course is for:

Description Machine learning and Deep learning have revolutionized various industries by enabling the development of intelligent systems capable of making informed decisions and predictions. These technologies have been applied to a wide range of real-world projects, transforming the way businesses operate and improving outcomes across different domains. In this training, an attempt has been made to teach the audience, after the basic familiarity with machine learning and deep learning, their application in some real problems and projects (which are mostly popular and widely used projects). Also, all the coding and implementation of the models are done in Python, which in addition to machine learning, students’ skills in Python language will also increase and they will become more proficient in it. In this course, students will be introduced to some machine learning and deep learning algorithms such as Logistic regression, multinomial Naive Bayes, Gaussian Naive Bayes, SGDClassifier, … and different models. Also, they will use artificial neural networks for modeling to do the projects. The use of effective data sets in different fields, data preparation and pre-processing, visualization of results, use of validation metrics, different prediction methods, image processing, data analysis and statistical analysis are other parts of this course. Machine learning and deep learning have brought about a transformative impact across a multitude of industries, ushering in the creation of intelligent systems with the ability to make well-informed decisions and accurate predictions. These innovative technologies have been harnessed across a diverse array of real-world projects, reshaping the operational landscape of businesses and driving enhanced outcomes across various domains. Within this training course, the primary aim is to impart knowledge to the audience, assuming a foundational understanding of machine learning and deep learning concepts. The focus then shifts to their practical applications in addressing real-world challenges and undertaking projects, many of which are widely recognized and utilized within the field. Moreover, the entirety of coding and models implementation is conducted using the Python programming language. This dual approach not only deepens the students’ grasp of machine learning but also contributes to their proficiency in the Python language itself. The curriculum of this course encompasses the introduction of several fundamental machine learning and deep learning algorithms, including Logistic Regression, Multinomial Naive Bayes, Gaussian Naive Bayes, SGDClassifier, and some other algorithms among others, alongside diverse model architectures. As a pivotal component of the course, students delve into the utilization of artificial neural networks for modeling, which serves as the cornerstone for executing the various projects. Comprehensive utilization of pertinent datasets spanning diverse domains, coupled with comprehensive data preparation and preprocessing techniques, takes precedence. The students are further equipped with the skills to visualize and interpret outcomes effectively, employ validation metrics judiciously, explore varied prediction methodologies, engage in image processing, and undertake data analysis and statistical analysis. These facets collectively constitute the multifaceted landscape covered by this course. And at the end, more than 40 complete and practical cheat sheets in the field of data science, machine learning, deep learning and Python have been given to you. Who this course is for: