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Excel For Data Science And Machine Learning

Candidates for the Azure Data Scientist Associate certification should have subject matter expertise in applying data science and machine learning to implement and run machine learning workloads on Azure.

Excel for Data Science and Machine Learning

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Responsibilities for this role include designing and creating a suitable working environment for data science workloads; exploring data; training machine learning models; implementing pipelines; running jobs to prepare for production; and managing, deploying, and monitoring scalable machine learning solutions.

The Data Science Virtual Machine (DSVM) is a customized VM image on the Azure cloud platform built specifically for doing data science. It has many popular data science tools preinstalled and pre-configured to jump-start building intelligent applications for advanced analytics.

In the DSVM, your training models can use deep learning algorithms on hardware that's based on graphics processing units (GPUs). By taking advantage of the VM scaling capabilities of the Azure platform, the DSVM helps you use GPU-based hardware in the cloud according to your needs. You can switch to a GPU-based VM when you're training large models, or when you need high-speed computations while keeping the same OS disk. You can choose any of the N series GPUs enabled virtual machine SKUs with DSVM. Note GPU enabled virtual machine SKUs are not supported on Azure free accounts.

Learn to perform Data Analytics using excel in this course. This free course has been designed to prepare you for meaningful opportunities in the future pertinent to Data Analytics. The course starts with the Introduction to Data Analytics and explains the phases of the Data Analytics project. It will further guide you to use different functions of excel with regard to Data Analytics like Sort and Filter, Lookup functions, conditional formatting, Pivot tables, and more. The final phase of this course will cover Data Visualization, in which you will be able to learn how to represent data using pictorial charts and tables. Using various examples, the instructor explains Data Visualization in the course. Attend a quiz after completing the course that lets you test your knowledge and claim your course certificate once you pass it.

This module will introduce you to Data Analytics, which is nowadays everywhere. It is becoming increasingly important to analyze the data in different industries, which is why it is great for you to learn Data Analytics and how we can do it using excel. This module will only introduce you to the importance of learning Data Analytics.

But mastering machine learning is a difficult process. You need to start with a solid knowledge of linear algebra and calculus, master a programming language such as Python, and become proficient with data science and machine learning libraries such as Numpy, Scikit-learn, TensorFlow, and PyTorch.

Naturally, not everyone needs to become a machine learning engineer. But almost everyone who is running a business or organization that systematically collects and processes can benefit from some knowledge of data science and machine learning. Fortunately, there are several courses that provide a high-level overview of machine learning and deep learning without going too deep into math and coding.

But in my experience, a good understanding of data science and machine learning requires some hands-on experience with algorithms. In this regard, a very valuable and often-overlooked tool is Microsoft Excel.

To most people, MS Excel is a spreadsheet application that stores data in tabular format and performs very basic mathematical operations. But in reality, Excel is a powerful computation tool that can solve complicated problems. Excel also has many features that allow you to create machine learning models directly into your workbooks.

Linear regression is a simple machine learning algorithm that has many uses for analyzing data and predicting outcomes. Linear regression is especially useful when your data is neatly arranged in tabular format. Excel has several features that enable you to create regression models from tabular data in your spreadsheets.

In addition to exploring the chart tool, Learn Data Mining Through Excel takes you through several other procedures that can help develop more advanced regression models. These include formulas such as LINEST and LINREG, which calculate the parameters of your machine learning models based on your training data.

Beyond regression models, you can use Excel for other machine learning algorithms. Learn Data Mining Through Excel provides a rich roster of supervised and unsupervised machine learning algorithms, including k-means clustering, k-nearest neighbor, naive Bayes classification, and decision trees.

In the decision tree chapter, you will go through the process calculating entropy and selecting features for each branch of your machine learning model. Again, the process is slow and manual, but seeing under the hood of the machine learning algorithm is a rewarding experience.

The courses are laid out in a learning path that makes it easy to progress through the content. The track starts with a Data Science Orientation course that sets the stage for the remaining courses. The orientation course is a great way to get a high-level over view of the discipline of data science with some practical examples and exercises to help you relate to your application development work:

Continuing the logical progression of the data science chain, the next course addresses the topics of analyzing and visualizing data. In this case, two options are offered, one course on using Excel for analysis and visualization, and the other focusing on Power BI.

The next course in the track explores the use of code to analyze large datasets. The course offers options to focus on either the R language or Python. This course is where developers can really dig their teeth into data science.

The latter half of the Data Science track is a build-up to a final project that showcases all of the knowledge and skills you have acquired. This build-up starts with a course that brings together all the essential topics of data science, from statistical analysis, data cleansing and transformation, and data visualization with R, Python, to Microsoft Azure Machine Learning.

Machine Learning is a topic that is becoming critical for application developers. The next course in the data science track focuses on this important topic, using Azure Machine Learning as the driving technology. In this course, you get hand-on experience building, evaluating, and optimizing machine learning models in Azure Machine Learning Studio. You will learn to answer key data science questions like classification, regression, clustering, and recommendation.

The next topic in the data science track is also of great interest to developers: Using code to manipulate and model data. Two options are offered for using the R language or Python. You will learn to read and write data from a variety of sources, and work with that data programmatically to summarize, transform, and visualize the data.

The track and others in the Microsoft Professional program is designed for busy professionals. The courses are broken into 5-10 minute short videos with interspersed knowledge check and practical exercises. You can easily start the courses and progress at your own pace. The courses are all free to audit, or you can elect to pay for a course certificate that can be shared online. So, you have no excuse not to sharpen your data science skills and get started on the Microsoft Professional Program. Get started now at:

Machine learning algorithms can automatically analyze hundreds and thousands of rows of text data in a fast, consistent and scalable way. In other words, machine learning algorithms are able to quantify words and phrases in Excel, by assigning topics, keywords, entities, and even sentiment to each row of text.

In short, machine learning helps teams sift through huge amounts of unstructured data (survey responses, online reviews, emails, and more) in spreadsheets, saving them time and energy and allowing them to focus on more fulfilling tasks.

Machine learning is a field within Artificial Intelligence (AI) that refers to algorithms that allow computers to learn how to perform specific tasks. These algorithms can learn from examples we provide as training data.

Real-time analysis of your survey responses could help you quickly craft a product roadmap for your company, addressing urgent issues that might be leading to customer churn.So, the main benefits of using machine learning to analyze text data in Excel include:

How many NPS responses does a company receive each quarter? Hundreds? Maybe thousands? Most customer surveys are easy to analyze, but open-ended answers are harder to batch-process because the data in them is unstructured. It consists of natural language in the form of sentences that machines are unable to understand.

Why is this important? Companies are investing more and more into researching and developing prediction models using machine learning. Allowing access to these models in Excel opens up a whole range of possibilities.

PyXLL, the Python Excel Add-In embeds Python in Excel, allowing us to extend Excel with Python. Using this, we can add user defined functions, macros, menus and more with just Python code. We can take advantage of the entire Python ecosystem, which is perfect for bringing machine learning to Excel.

The Python programming language is well suited for machine learning. It has a huge array of well supported packages that make coding simpler and reduce development time. Machine learning, deep learning and artificial intelligence are extremely well catered for by several Python packages, therefore making Python an ideal choice.

The scikit-learn package exposes a concise and consistent interface to the common machine learning algorithms, making it simple to bring ML into production systems. The library combines quality code and good documentation, ease of use and high performance and is de-facto industry standard for machine learning with Python. 041b061a72


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