Python Course — Complete Data Science Training Using 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 in the iPython / Jupyter environment, a powerful framework for data science analysis

• Become proficient in using the most common Python data science packages including Numpy, Pandas, Scikit and Matplotlib

• Be able to read data from various sources (including web page data) and clean data

• Perform data exploration and pre-processing tasks such as tabulation, pivoting, and summarizing data with Python

• Become proficient in working with real-world data collected from various sources

• Perform data visualization and understand which techniques should be applied when

• Perform the most common statistical data analysis techniques in Python including t-tests and linear regression

• Understand the differences between machine learning and statistical data analysis

• Apply different unsupervised learning techniques to real-world data

• Perform supervised learning (in both classification and regression) techniques on real data

• Evaluate the accuracy and generalization of machine learning models

• Build basic neural networks and deep learning algorithms

• Use the powerful H2o framework to implement deep neural networks

Requirements:

• Students should be able to use a PC at a beginner level, including being able to install programs

• A desire to learn data science

• Previous Python knowledge will be useful but is not required

Description:

• In this course, you will find 12 complete sections that cover every aspect of Python Data Science: • A complete introduction to Python Data Science and powerful Python-based frameworks for data science, Anaconda • Getting started with Jupyter notebooks to apply data science techniques with Python • Comprehensive presentation of basic analytical tools - Numpy Arrays, Operations, Arithmetic, Equation-solving, Matrices, Vectors, Broadcasting, etc. • Data Structures and Reading in Pandas, including CSV, Excel, JSON, HTML data • How to Pre-Process and "Wrangle" your Python data by removing NAs / missing data, handling conditional data, grouping by attributes, etc. • Creating data visualizations such as histograms, boxes, scatterplots, barplots, line / pie charts, and much more! • Statistical analysis, statistical inference, and relationships between variables • Machine Learning, Supervised Learning, Unsupervised Learning with Python • You will even discover how to create artificial neural networks and deep learning structures!