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Python is the preferred language for new technologies such as Data Science and Machine Learning
Data Science and Analytics (DSA) job listings is projected to grow by nearly 364,000 listings in 2020 - IBM
According to the TIOBE index, Python is one of the most popular programming languages in the world.
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It's continued to be a favourite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging programs is a breeze in Python with its built in debugger.
It runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
It has evolved as the most preferred Language for Data Analytics and the increasing search trends on Python also indicates that it is the " Next Big Thing " and a must for Professionals in the Data Analytics domain.
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Module 1: Introduction to Python
Python for Data Science Certification
Edureka’s Python for Data Science Professional Certificate Holders work at 1000s of companies like
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Industry: Social Media
Problem Statement: You as ML expert have to do analysis and modeling to predict the number of shares of an article given the input parameters.
Actions to be performed:
Load the corresponding dataset. Perform data wrangling, visualization of the data and detect the outliers, if any. Use the plotly library in Python to draw useful insights out of data. Perform regression modeling on the dataset as well as decision tree regressor to achieve your Learning Objectives. Also, use scaling processes, PCA along with boosting techniques to optimize your model to the fullest.
Problem Statement: You as an ML expert have to cluster the countries based on various sales data provided to you across years.
Actions to be performed:
You have to apply an unsupervised learning technique like K means or Hierarchical clustering so as to get the final solution. But before that, you have to bring the exports (in tons) of all countries down to the same scale across years. Plus, as this solution needs to be repeatable you will have to do PCA so as to get the principal components which explain the max variance.
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