Saturday, February 10, 2018

Complete Data Science Training with Python for Data Analysis

Complete Data Science Training with Python for Data Analysis

Complete Guide to Practical Data Science with Python: Learn Statistics, Visualization, Machine Learning & More.

What Will I Learn?
  • Install Anaconda and work within the iPytjhon/Jupyter environment, a powerful framework for data science analysis
  • Become proficient in the use of thee most common Python data science packages including Numpy, Pandas, Scikit and Matplotlib
  • Be able to read in data from different sources(including webpage data) and clean the data
  • Carry out data exploratory and pre-processing tasks such as tabulation, pivoting and data summarizing in Python
  • Become proficient in working with real life data collected from different sources
  • Carry out data visualization and understand which techniques to apply when
  • Carry out the most common statistical data analysis techniques in Python including t-tests and linear regersiion
  • Understand the difference between machine learning and statistical data analysis
  • Implement different unsupervised learning techniques on real life data
  • Implement supervised learning (both in form of classification and regression) techniques on real data
  • Evaluate the accuracy and generality of machine learning models
  • Build basic neural networks and deep learning learning algorithms
  • Use the powerful H2o framework for implementing deep neural networks
  • Students should be able to use PC at a beginner's level, including being able to install programs
  • Desire to learn data science
  • Prior knowledge of Python will be useful but not necessary
  • 12.5 hours on-demand video
  • 5 Articles
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion

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