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Data Science

Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.

Beginning with Apache Spark Part-2

Beginning with Apache Spark Part-2

The Apache Spark DataFrame API provides a rich set of functions (select columns, filter, join, aggregate, and so on) that allow you to solve common data analysis problems efficiently...

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Beginning with Apache Spark Part-1

Beginning with Apache Spark Part-1

Apache Spark is a fast, in-memory data processing engine with elegant and expressive development APIs to allow data workers to efficiently execute streaming..

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Mean, Median, Mode & Range in Statistics

Mean, Median, Mode & Range in Statistics

The terms mean, median and mode are used to describe the central tendency of a large data set. Range provides provides context for the mean, median and mode...

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Introduction of Statistics

Introduction of Statistics

Statistics, in short, is the study of data. It includes descriptive statistics (the study of methods and tools for collecting data, and mathematical models to describe and interpret data) and inferential statistics (the systems and techniques for making probability-based decisions and accurate predictions.

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Talib - Technical Analysis Library in python

Talib - Technical Analysis Library in python

TA-Lib is widely used by trading software developers requiring to perform technical analysis of financial market data.

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