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Professional Data Science

Course Details

Turn Data into Treasure

Python Programming

Python Programming

Python is the most popular programming language for data science nowadays because of its widely supported data science and machine learning libraries. It is thus important to understand the in and out of Python before diving deep into the ocean of data science.

It covers in-depth knowledge of the following:

  • Python
  • Jupyter Notebook
Python Programming
Python Programming
Data Science Libraries

Data Science Libraries

There are lots of libraries in existence to make working with data science an easier job. With the help of these libraries, data scientists can extract, cleanse and visualise the datasets in hand.

Making good use of the data science libraries paves a good foundation for further advancement in your career.

This course covers the in-depth knowledge of the following:

  • Numpy
  • Pandas
  • Seaborn
  • Matplotlib
Data Science Libraries
Data Science Libraries
Data Science Libraries
Machine Learning Libraries

Machine Learning Libraries

Most web applications are significantly more complex on the serverside than it may seem from the user interface. The domain of developing backend applications which performs data processing, storage and analysis is known as backend development.

This course covers the in-depth knowledge of the following:

  • Scikit-learn
  • K-means Clustering
  • Decision Tree
Machine Learning Libraries
Machine Learning Libraries
Deep Learning Libraries

Deep Learning Libraries

A web application will not be successful without providing users with an interactive user interface and seamless user experience. The knowledge and use of Single Page Application (SPA) technology is necessary to achieve this.

This course covers the in-depth knowledge of the following:

  • Neural network
  • Tensorflow
  • Keras
Deep Learning Libraries
Deep Learning Libraries
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