Business Analytics with Excel | Data Science Tutorial | Simplilearn
Business Analytics with excel training has been designed to help initiate you to the world of analytics. For this we use the most commonly used analytics tool i.e. Microsoft Excel. The training will equip you with all the concepts and hard skills required to kick start your analytics career. If you already have some experience in the IT or any core industry, this course will quickly teach you how to understand data and take data driven decisions relative to your domain using Microsoft excel.
Data Science Certification Training - R Programming: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Data-Excel-W3vrMSah3rc&utm_medium=SC&utm_source=youtube
For a new-comer to the analytics field, this course provides the best required foundation. The training also delves into statistical concepts which are important to derive the best insights from available data and to present the same using executive level dashboards. Finally we introduce Power BI, which is the latest and the best tool provided by Microsoft for analytics and data visualization.
What are the course objectives?
This course will enable you to:
1. Gain a foundational understanding of business analytics
2. Install R, R-studio, and workspace setup. You will also learn about the various R packages
3. Master the R programming and understand how various statements are executed in R
4. Gain an in-depth understanding of data structure used in R and learn to import/export data in R
5. Define, understand and use the various apply functions and DPLYP functions
6. Understand and use the various graphics in R for data visualization
7. Gain a basic understanding of the various statistical concepts
8. Understand and use hypothesis testing method to drive business decisions
9. Understand and use linear, non-linear regression models, and classification techniques for data analysis
10. Learn and use the various association rules and Apriori algorithm
11. Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering
Who should take this course?
There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals:
IT professionals looking for a career switch into data science and analytics
Software developers looking for a career switch into data science and analytics
Professionals working in data and business analytics
Graduates looking to build a career in analytics and data science
Anyone with a genuine interest in the data science field
Experienced professionals who would like to harness data science in their fields
Who should take this course?
There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals:
1. IT professionals looking for a career switch into data science and analytics
2. Software developers looking for a career switch into data science and analytics
3. Professionals working in data and business analytics
4. Graduates looking to build a career in analytics and data science
5. Anyone with a genuine interest in the data science field
6. Experienced professionals who would like to harness data science in their fields
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Data Science and Big Data Analytics: Making Data-Driven Decisions
Discover how to turn big data into even bigger results in our seven-week digital course from MIT. For more info, visit https://bigdataanalytics.mit.edu/
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Cloudera: Instrumented Data Analytics
Sizing clusters for jobs, finding bottlenecks or getting insight into failures after they have run in Apache Spark isn't easy. In this episode of 'This is My Architecture' - https://amzn.to/2MCyvh1, Wing from Cloudera explains how they use telemetry to optimize analytics workloads on AWS to make those headaches disappear.
Host: Adrian De Luca, Solution Architect, AWS
Speaker: Wing Leong Ho, Senior Sales Engineer, Cloudera
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Smart Data Analytics: Die BMW Group setzt auf intelligente Nutzung von Produktionsdaten.
Bei der Produktion eines Automobils entstehen entlang der gesamten Wertschöpfungskette erhebliche Mengen an Daten. Die BMW Group analysiert in ihrem Digitalisierungsfeld Smart Data Analytics diese Daten zielgerichtet zur Weiterentwicklung ihres Produktionssystems. In allen Fertigungsbereichen und der Logistik leisten die Erkenntnisse aus intelligenten Datenanalysen einen wirksamen Beitrag zur Verbesserung der Qualität. Dank der vernetzten Produktion steigt die Anlagenverfügbarkeit spürbar: jede gewonnene Minute bedeutet ein zusätzlich gebautes Fahrzeug.
Mehr Informationen:
https://www.bmwgroup.com/produktion
Abonniere den offiziellen BMW Group YouTube Channel unter https://www.youtube.com/user/BMWGroupView
Folge uns auf Facebook:
https://www.facebook.com/BMWGroup
Instagram: https://www.instagram.com/bmwgroup
Twitter:
https://twitter.com/BMWGroup
Google+:
https://plus.google.com/+BMWGroup
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Teradata Indonesia: Tawarkan Solusi Binis Lewat Big Data Analytics
Sudah lebih dari dua tahun, teknologi cloud telah memberikan penetrasi yang tinggi di Indonesia, terutama di kalangan enterprise. Kemudian, hadirlah big data dengan sistem data analytics mampu membantu para pembuat keputusan di perusahaan untuk memberikan keputusan bisnis yang lebih cepat dan akurat.
See More Videos On Tekno TV:
www.teknopreneur.com/tekno-tv
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Learning Predictive Analytics With Python, Analyzing Election Data With Pandas [Python Statistics]
IN this Exploratory Data Analysis Tutorial, We perform predictive analytics with python by analyzing Election data from 2 candidates. Pandas data Analysis Techniques are used to learn about patterns in the election data. This is a Part of Python with Statistics Tutorial series.
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Jupyter Notebooks and Data Sets for Practice: https://github.com/theengineeringworld/statistics-using-python
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Python Graph Visualization, Statistics For Data Analytics [ Python Bar Graph Example Tutorial ] https://youtu.be/3KofFIhtjNE
Data Cleaning Steps and Methods, How to Clean Data for Analysis With Pandas In Python [Example] 🐼 https://youtu.be/GMxCL0PBHzA
Data Wrangling With Python Using Pandas, Data Science For Beginners, Statistics Using Python 🐍🐼 https://youtu.be/tqv3sL67sC8
Cleaning Data In Python Using Pandas In Data Mining Example, Statistics With Python For Data Science https://youtu.be/xcKXmXilaSw
Cleaning Data In Python For Statistical Analysis Using Pandas, Big Data & Data Science For Beginners https://youtu.be/4own4ojgbnQ
Exploratory Data Analysis In Python, Interactive Data Visualization [Course] With Python and Pandas https://youtu.be/VdWfB30QTYI
Python Describe Statistics, Exploratory Data Analysis Using Pandas & NumPy [Descriptive Statistics] https://youtu.be/6SeJH0p7n44
Data Visualization In Python, [ Plots Of Two Variables ] Statistics & Data Analysis With Python 🐍 https://youtu.be/uufMAMUEAaQ
Python Graph Visualization, Exploratory Data Analysis With Pandas & Matplotlib [ Python Statistic ] https://youtu.be/Eb9eD4aNS7o
Python Data Visualization [ Graphing Categorical Data ] Pandas Data Analysis & Statistics Tutorial https://youtu.be/M1h0pPFVy0E
Exploratory Data Analysis In Python, Email Analytics With Pandas [ Predictive Analytics Python ] 🔴 https://youtu.be/03OJrdbhor0
Learning Predictive Analytics With Python, Analyzing Election Data With Pandas [Python Statistics] https://youtu.be/sNg8VnMOAfw
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*** Complete Python Programming Playlists ***
* Python Data Science
https://www.youtube.com/watch?v=Uct_EbThV1E&list=PLZ7s-Z1aAtmIbaEj_PtUqkqdmI1k7libK
* NumPy Data Science Essential Training with Python 3
https://www.youtube.com/playlist?list=PLZ7s-Z1aAtmIRpnGQGMTvV3AGdDK37d2b
* Python 3.6.4 Tutorial can be fund here:
https://www.youtube.com/watch?v=D0FrzbmWoys&list=PLZ7s-Z1aAtmKVb0fpKyINNeSbFSNkLTjQ
* Python Smart Programming in Jupyter Notebook:
https://www.youtube.com/watch?v=FkJI8np1gV8&list=PLZ7s-Z1aAtmIVV0dp08_X-yDGrIlTExd2
* Python Coding Interview:
https://www.youtube.com/watch?v=wwtzs7vTG50&list=PLZ7s-Z1aAtmJqtN1A3ydeMk0JoD3Lvt9g
📌📌📌📌📌📌📌📌📌📌
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Big Data Analytics: Answers from Big Data
Keynote video, presented at the SAS Analytics Conference 2012, by William Hakes, CEO and Co-Founder of Link Analytics.
Big Data Analytics is not just hype.
Big Data Analytics is more than technology. It's a new way of thinking.
Here's proof as to why we need Big Data Analytics:
In 2012, the world's information totaled over 2 zetabyes. That's 2 trillion gigabytes.
By 2020, that number will be 35 trillion.
80% of this new data is unstructured.
It is too large, too complex, and too disorganized to be analyzed by traditional tools.
We need new tools and new talent to navigate this information and find value.
We are at the beginning of the Big Data Revolution.
Are YOU ready?
www.linkanalytics.com
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Big Data Analytics: Aktuelle Trends, Technologien und Lösungen
White Papers, Demos und Webinare unter http://www.sas.de
Alexander Bruder (SAS Deutschland) spricht mit den Experten Andreas Diggelmann (SAS International), Klaus Fabits und Dr. Andreas Becks (SAS DACH) über aktuelle Fragestellungen, Trends und Entwicklungen zu den Themen Datenvisualisierung, Analytics in der Cloud und für Hadoop, Customer Experience Management, Risikomanagement und mehr.
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