PMI-ACP® Exam Prep 2025

Top 10 data analytics course using matplotlib 2022, Top 10 data visualization course using matplotlib 2022, Matplotlib 2022The data analytics is the process of finding insights of the data. It involves following important steps,1. Collection of relevant data2. Preprocessing and transforming data3. Plotting data using different types of graphs4. Understanding insight of the dataWe can plot data in different types of plots using matplotlib library. Matplotlib is a cross-platform, data visualization and graphical plotting library for Python and its numerical extension NumPy. It along with python numpy package provides open source alternative to MATLAB. Developers can use matplotlib library for plotting graphs. Also they can use matplotlib’s APIs (Application Programming Interfaces) to embed plots in GUI based applications. In this course you are going to learn details of matplotlib library. The content of this course is as follows,Chapter 1: Introduction to MatPlotLibA. What is Matplotlib?B. Pyploy APIC. PyLab ModuleD. Simple PlotChapter 2: Object Oriented MatplotlibA. Object oriented interfaceB. Figure classC. Axes classD. TransformsChapter 3: Multiple PlotsA. MultiplotsB. Subplots functionC. Subplot2grid functionChapter 4: Formatting PlotsA. GridsB. Formatting axesC. Setting limitsD. Setting ticks and tick labelsE. Twin axesChapter 5: Types of PlotsA. Bar plotB. Stacked bar chartC. HistogramD. Pie chartE. Scatter plotF. Contour plotG. Quiver plot

Time-to-application is number one goal of this course -- After the course you can directly start optimizing using a ready-to-use Python based Genetic Algorithm Tool!No time consuming "tool development from scratch" -- we work with ready-to-use, flexible Genetic Algorithm Optimization Tool written in the most basic Python and you get the tool at the end of the courseFor all the "Beginners" who want to be real-world users in the most effective way possibleNo advanced Python or programming skills needed -- Most basic Python is used for the whole algorithm -- Python lists and Numpy arrays thats it!Designed for Beginners who don't want to program the algorithm or spent a lot of time to transfer someones hard-coded program to their individual optimisation problemsComplete Beginners Guide to Genetic Algorithm OptimizationDo you want to learn about a very powerful optimization method that is used for many optimization problems such as neural networks, engineering, logistics, finance and many more? Do you want to avoid a ton of theory without practical application or very specific code snippets that are not really transferable to your individual optimization problems? If so, this course will help you enhance your optimization skills with:- Genetic Algorithm based Optimization- A ready-to-use python based simple but flexible Genetic Algorithm Based OptimizerGenetic algorithm based optimization is a metaheuristic optimization method for a large specturm of optimization problems with multiple design parameters (multi-parameter). Compared to other optimization methods it is more stable against local extrema and offers great flexibility. This course is desig

This course covers the concepts of Penetrant (also called Liquid or Dye Penetrant) Testing, an NDT method used to detect cracks and cavities open on the surface of nonporous metallic materials. Liquid Penetrant Testing is one of the simplest and most popular NDT methods for inspection.In this course, we will cover the required concepts around penetrant testing training necessary for completion of the PT certification program. This is essential for ensuring compliance as per NAS-410, SNT-TC-1A and NavSea Technical Publication T9074-AS-GIB-010/271.The training is divided into eight (8) chapters.Penetrant history, capillary action and wetting, cohesion and adhesion, and the methods of liquid penetrant testingSurface preparation; pre-cleaning; and the various liquid penetrant systemsSafety; LOX materials; penetrant application techniques; penetrant dwell times; temperature limits; excess penetrant removal techniques and over cleaning precautions; advantages and disadvantages of each removal technique; developer types and applicationInterpretation and evaluation; false, non-relevant and relevant indicationsQuality Control test; Tam panel; Comparator BlockTerminology; product discontinuities by product form such as casting, forgings etc.; discussion of methods to make the different product forms and weldingSystem classifications by Type, Method, Developer forms; penetrant terms such as wetting ability defined; procedure requirementsTypical qualification and certification documents; SNT-TC-1A overviewThis course content conforms to the training subjects listed in ANSI/ASNT CP-105, Standard Topical Outlines for Qualification of Nondestructive Testing Personnel. This training will meet most certification document for liquid penetrant testing such as SNT-TC-1A. It encompasses the subject matter listed in the ANSI/ASNT CP 105 outline for liquid penetrant ce

The course "Principles of Data Science and Machine Learning for Natural Sciences" is designed to connect traditional scientific disciplines with the rapidly growing fields of Data Science (DS) and Machine Learning (ML). As research increasingly depends on large datasets and advanced computational methods, it’s becoming essential for scientists to know how to leverage DS and ML techniques to improve their work.This course offers a solid introduction to the key concepts of Data Science and Machine Learning, specifically aimed at scientists and researchers in areas like biology, chemistry, physics, and environmental science. Participants will learn the basics of data analysis, including data collection, cleaning, and visualization, before moving on to machine learning algorithms that can help identify patterns and make predictions from data.The course doesn’t require any programming skills and focuses on fundamental theoretical concepts. It's structured into six main sections:1. Introduction We'll start by introducing the course, covering its main features, content, and how to follow along.2. Core DS/ML Concepts We’ll go over basic concepts like variables, data scaling, training, datasets, and data visualization.3. Classification In this section, we’ll discuss key classification algorithms such as decision trees, random forests, Naive Bayes, and KNN, with examples of how they can be applied in scientific research.4. Regression We’ll briefly cover linear and multiple linear regression, discussing the main ideas and providing examples relevant to science.5. Clustering This section will focus on standard and hierarchical clustering methods, along with practical examples for scientific applications.6. Neural Networks Finally, we’ll introduce neural

Exploring and assessing the strength of associations between variables/features plays a fundamental role in statistical analysis and machine learning.All the applications in the course are implemented in Python. There are overlaps between this course and my other course "Correlations, Associations and Hypothesis Testing (with R)".I decided to create this course after leading many data science projects and coming across many data scientists struggling with the fundamentals of association between variables/features and hypothesis testing.This course will be beneficial to junior analysts as well as to more experienced data scientists. In particular,If you are an aspiring/junior data analyst/scientist, this course will contribute towards building the right foundation at an early stage of your career.If you are an experienced data scientist, this course will help you to re-visit and eventually improve your understanding of the assessment of associations between variables/features.The course is divided into three main sections.The first section looks at the assessment and quantification of associations between numerical variables.The second section focusses on the assessment of associations between categorical variables.The third section covers the assessment of associations between numerical and categorical variables.Each section discusses a number of statistical metrics in relation to associations between variables and then build statistical hypothesis tests to measure the strengths of these associations.There are practical sessions throughout the course, where you will see how to implement the methods discussed in the course (using Python) and to perform various hypothesis testing using real world datasets. Your will also learn and master how to interpret results in a broader context.In addition, quiz is added at the end of each section. The objective of these quizzes

OpenFOAM for Absolute Beginners______________________________________________________________________________________________________*** If you consider buying the course, please add the below syntax between the quotes at the end of your URL as a token of support to the creator through affiliate link. www [dot] udemy [dot] com/course/openfoam-for-absolute-beginners/?referralCode=F63024DD6569B9442D4D ***Thankyou______________________________________________________________________________________________________Course Description:Welcome to "OpenFOAM for Absolute Beginners," a comprehensive course designed to introduce you to the powerful world of computational fluid dynamics (CFD) using OpenFOAM. Whether you're a student, engineer, or hobbyist, this course will guide you through the fundamentals of OpenFOAM, ensuring you develop a solid foundation to tackle real-world CFD problems.What You'll Learn:1. Introduction to CFD and OpenFOAM:Introduction to OpenFOAMInstallation and SetupFile Structure and Basic Commands2. Geometry Preparation and Mesh Generation Geometry and Mesh Generation with blockMeshBoundary and Initial Conditions Solvers and Running the SimulationPost-Processing Results with ParaView3. Mesh Generation Using blockMesh and snappyHexMeshblockMesh Tool for snappyHexMeshsnappyHexMesh Tool Introduction Creating Basic MeshesQuality Checks using checkMesh4. Types of Solvers and Case Study 1Incompressible SolversCompressible SolversMultiphase SolversCase Study Introduction: Lid Driven Cavity5. Boundary Conditions and Turbulence