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This bundle is ideal for: Students seeking mastery in this field Professionals seeking to enhance their skills Anyone who is passionate about this topic Requirements This Learn C++ from Scratch: The Complete Coding Guide doesn't require prior experience and is suitable for diverse learners. Career path This Learn C++ from Scratch: The Complete Coding Guide bundle will allow you to kickstart or take your career in the related sector to the next stage. Certificates CPD Accredited Digital certificate Digital certificate - Included CPD Accredited Hard copy certificate Hard copy certificate - Included If you are an international student, then you have to pay an additional 10 GBP for each certificate as an international delivery charge.
Unlock the power of Excel like never before with our 'Excel Add-in with C# VSTO and Web' course. This innovative curriculum fuses the potential of Excel with the dynamic capabilities of C# and the web. Grasp the foundational elements of VSTO and deepen your understanding through extensive modules. Transition smoothly to the vast arena of web basics, bridging the gap between spreadsheet functionalities and online applications. Experience a synthesis of software development and data management in one cohesive learning journey. Learning Outcomes Understand the integration of Excel and C# through VSTO. Grasp foundational concepts and advanced features of VSTO. Gain knowledge on how to create powerful Excel add-ins. Delve into the essential principles of web applications in relation to Excel. Harness the potential of merging Excel with the web for enhanced functionality. Why choose this Excel Add-in with C# VSTO and Web course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments are designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Excel Add-in with C# VSTO and Web Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Who is this Excel Add-in with C# VSTO and Web course for? Developers keen on integrating Excel functionalities into applications. Data analysts aiming to amplify their Excel capabilities. IT professionals aspiring to combine web applications with Excel. Excel enthusiasts curious about the crossover with C# and web. University students pursuing a career in software development with a focus on Excel add-ins. Career path Excel VSTO Developer: £30,000 - £55,000 Web Application Engineer: £25,000 - £50,000 Data Integration Specialist: £35,000 - £60,000 C# Developer with Excel Focus: £32,000 - £58,000 IT Consultant Specialising in Office Integrations: £40,000 - £70,000 Data Analytics Manager: £45,000 - £80,000 Prerequisites This Excel Add-in with C# VSTO and Web does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Excel Add-in with C# VSTO and Web was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Introduction Introduction 00:03:00 VSTO basics Basic interactions 00:07:00 Advanced interactions 00:21:00 More on Vsto Multiple sheets 00:07:00 Charts 00:07:00 Web Basics Preparation and single cell input/output 00:23:00 Working with multiple cells 00:15:00
Duration 2 Days 12 CPD hours This course is intended for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful. Overview By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data. This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You?ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you?ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. Data Exploration and Cleaning Python and the Anaconda Package Management System Different Types of Data Science Problems Loading the Case Study Data with Jupyter and pandas Data Quality Assurance and Exploration Exploring the Financial History Features in the Dataset Activity 1: Exploring Remaining Financial Features in the Dataset Introduction to Scikit-Learn and Model Evaluation Introduction Model Performance Metrics for Binary Classification Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve Details of Logistic Regression and Feature Exploration Introduction Examining the Relationships between Features and the Response Univariate Feature Selection: What It Does and Doesn't Do Building Cloud-Native Applications Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients The Bias-Variance Trade-off Introduction Estimating the Coefficients and Intercepts of Logistic Regression Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters Activity 4: Cross-Validation and Feature Engineering with the Case Study Data Decision Trees and Random Forests Introduction Decision trees Random Forests: Ensembles of Decision Trees Activity 5: Cross-Validation Grid Search with Random Forest Imputation of Missing Data, Financial Analysis, and Delivery to Client Introduction Review of Modeling Results Dealing with Missing Data: Imputation Strategies Activity 6: Deriving Financial Insights Final Thoughts on Delivering the Predictive Model to the Client
Master Deep Learning Heuristics with R! This course will teach you how to use Deep Learning Heuristics to solve complex problems in Agriculture, Cryptocurrencies, Energy Sector, and Financial Markets. With R, you will be able to develop and implement efficient and scalable solutions.