This course sheds light on advanced concepts and topics of data analytics and visualization in Microsoft Excel.
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Course Duration
This course sheds light on advanced concepts and topics of data analytics and visualization in Microsoft Excel.
This intermediate-level course is specifically designed for IT Professionals and Experts striving to learn more about Excel Data Analytics and Visualization Tools. This course provides a comprehensive overview of Microsoft Excel's core features and functionalities for analyzing business data and creating compelling visualizations. Professionals get to learn about Data Importation from Multiple Sources, Databases, and File Systems. Besides this, Developing Mash-Ups, Data Collection & Exploration, and Data Analysis & Visualizations are also elaborated in detail.
This course sheds light on advanced concepts and topics of data analytics and visualization in Microsoft Excel. Through this course, candidates can learn the art of utilizing data and information to help businesses maximize productivity and identify loopholes and weaknesses.
The core objective of this course is to help professionals develop a better understanding and sound knowledge of the following key concepts:
Professionals planning to enroll in the Analyzing and Visualizing Data with Excel Course must comply with the following prerequisites:
| Brand | Microsoft |
|---|---|
| Subjects | Business Productivity |
| Lab Access | No |
| Technology | Microsoft |
| Learning Style | Self-Paced Learning |
| Learning Type | Course |
| Difficulty | Intermediate |
| Course Duration | 24 Hours |
| Language | English |
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Tom is an innovator first, and then a Data Scientist & Software Architect. He has integrated expertise in business, product, technology and management. Tom has been involved in creating category defining new products in AI and big data for different industries, which generated more than hundred million revenue cumulatively, and served more than 10 million users.
As a Data Scientist and Software Architect Tom has extensive experience in data science, engineering, architecture and software development. To date Tom has accumulated over a decade of experience in R, Python & Linux Shell programming.
Tom has expertise on Python, SQL, and Spark. He has worked on several libraries including but not limited to Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, SciPy, NLTK, Keras, and Tensorflow.
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