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Introduction to Data Science: How to “Big Data” with Python

Dataconomy

Katharine Jarmul and Data Natives are joining forces to give you an amazing chance to delve deeply into Python and how to apply it to data manipulation, and data wrangling. By the end of her workshop, Learn Python for Data Analysis, you will feel comfortable importing and running simple Python analysis on your.

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Big Data Creates Massive Changes for the Game of Golf

Smart Data Collective

We recently talked about some of the changes that data has created in the game of golf. Big Data and Golf Game. Every aspect of golf in the modern form is being transformed through data analysis, cloud technologies, machine learning, and scientific advances. Alternatively, you can buy a golf simulator.

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Technical Analysis is Changing Quickly in the Era of Big Data

Smart Data Collective

Big data technology has undoubtedly changed the financial industry in extraordinary ways. We usually talk about the benefits of big data from the perspective of financial institutions such as hedge fund managers, insurance companies and banks. The law of big numbers reinforces the reliability and accuracy of our analyses.

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Object-centric Process Mining on Data Mesh Architectures

Data Science Blog

The trend towards powerful in-house cloud platforms for data and analysis ensures that large volumes of data can increasingly be stored and used flexibly. New big data architectures and, above all, data sharing concepts such as Data Mesh are ideal for creating a common database for many data products and applications.

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How artificial intelligence went from science fiction to science itself?

Dataconomy

In 2016, Google’s AI AlphaGo defeated Lee Sedol and Fan Hui, the European and world champions in the game of Go. Instead of manually coding rules as in expert systems, the focus shifted to allowing computers to independently discover patterns and correlations through large-scale data analysis.

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How to tackle lack of data: an overview on transfer learning

Data Science Blog

Presumably due to this fact, Andrew Ng, in his presentation in NeurIPS 2016, gave a rough and abstract predictions of how transfer learning in machine learning would make commercial success like white lines in the figure below. ” That might have been said only because big data is sources of various industries.

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How to optimize your LinkedIn as a Data Scientist?

Pickl AI

For example, if you are a Data Scientist, then you should add keywords like Python, SQL, Machine Learning, Big Data and others. These are some of the key skills that one needs to have if you are eyeing a fulfilling career as a Data Scientist. Highlight Your Experience Don’t miss this part.