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The main reason for the course is to enable understudies to add investigation capacities to Excel spreadsheets and to give understudies an establishment to learn further developed information examination with Excel or Power BI.
Learning Style
Provider
Difficulty
Course Duration
The main reason for the course is to enable understudies to add investigation capacities to Excel spreadsheets and to give understudies an establishment to learn further developed information examination with Excel or Power BI.
The main reason for the course is to enable understudies to add investigation capacities to Excel spreadsheets and to give understudies an establishment to learn further developed information examination with Excel or Power BI.
This course is designed for any individual who needs to take the information analysis innovations in Excel beyond formulas and include further developed capacities, for example, hierarchies, dashboards, and connections.
Prior to attending this course, understudies must have:
Fundamental information on the MS Windows operating framework and its main usefulness.
Propelled working information on Excel spreadsheets including formulas.
Brand | Microsoft |
---|---|
Subjects | Business Productivity |
Lab Access | No |
Technology | Microsoft |
Learning Style | Virtual Classroom |
Learning Type | Course |
Difficulty | Intermediate |
Course Duration | 2 Days |
Language | English |
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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