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Data Science A-Z™: Real-Life Data Science Exercises :
This course will give you a full overview of the Data Science journey. Upon completing this course you will know:
- How to clean and prepare your data for analysis
- How to perform basic visualisation of your data
- How to model your data
- How to curve-fit your data And finally, how to present your findings and wow the audience
This highly 5 Star rated course is extremely hands-on and incredibly practical.
Learning Python for Data Analysis and Visualization
Through this course, you’ll get a full understanding of how to program with Python and how to use it in conjunction with scientific computing modules and libraries to analyze data.
By the end of this course you will:
- Have an understanding of how to program in Python.
- Know how to create and manipulate arrays using numpy and Python.
- Know how to use pandas to create and analyze data sets.
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- Have an amazing portfolio of example python data analysis projects!
- Have an understanding of Machine Learning and SciKit Learn!
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A Beginner’s Guide to Deep Neural Networks http://t.co/VmjAMgZuEn
100 Most Popular Machine Learning / Datascience Videos http://t.co/EyeW32UYLY
Attribution Modelling Simplified http://t.co/AK2CDiWbw3
Free Data Science Books http://t.co/wy8yDmQB1A
Cheatsheet – Python & R codes for common Machine Learning Algorithms http://t.co/akLwA6pQJ7
Data Scientist has been touted as the most sexiest title in the recent past. Have you ever thought of what’s it would be like to be a Data Scientist? How does their day looks like? What is that makes them excited and not so happy about? What challenges they face and overcome? What are the tools of their craft? Crowdflower recently polled around 150 data scientists and had recently published the results via a report. Check out the below infographic for a quick look at how a Data Scientist’s life would look in the year 2015.
Microsoft has gotten into a great course correction since Satya Nadella took over as the CEO. In its effort to transform itself into a platform company per the vision of the new CEO, Microsoft has been truly taking some critical steps in the Big Data analytics space. Since the launch of its Azure Machine Learning platform, Microsoft has been quietly focussing on building the foundational blocks and consolidating the Azure MLs adoption. It’s easy drag, drop and predict approach on a cloud based Machine Learning platform has won a solid following. It has also been helping developers to jump start Machine Learning through its Microsoft Virtual Academy led courses such as :
Microsoft has also recently made a couple of rather significant acquisitions to bolster its case further. It’s acquisition of Equivio, a Text Analysis / Machine Learning based eDiscovery / compliance vendor whose main product is Zoom, a court approved machine learning platform. The deal is stated to be around $200 Million. Microsoft is reportedly planning to utilize Equivio’s machine learning technology to further improve its Office 365’s eDiscovery and information governance capabilities. It would help quickly search and find relevant information from the unstructured data present in documents.
Yet another recent acquisition had been Revolution Analytics – a commercial software and services provider for R, the world’s most widely used programming language for statistical computing and predictive analytics. It has also extended its support for open source communities by adopting Linux and also partnered with Hortonworks, a open source Hadoop distribution vendor to extend Big Data to the enterprise through its Azure HDInsight Big Data platform offering. Microsoft had also open sourced its REEF to provide a big data analytics framework for YARN.
Microsoft is also facing off with IBM Watson through its ease of use Machine Learning approach. It’s attacking IBM Watson’s biggest issue – the steep learning curve by providing an easy to use and adapt data model / APIs. In its recent post, ZDNet reports that Microsoft’s goal for Azure Machine Learning is to develop data models that can be plugged directly into apps that will take that data, analyze and query it, and turn it into information that will be greater than the sum of its parts for a user. Point in case its prebuilt data model for Microsoft Band which is its first fitness device. The device monitored data is used for measuring, analyzing the health parameters. It functions as a hub for data analysis of all the data collected through the device monitor.
Interesting times ahead for Microsoft since it seems to have placed its bets big on Analytics, Big Data, Cloud & Machine Learning in a big way.