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

Requirements

• 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

Includes:

• 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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