Python Data Analysis class

First lesson Python Getting Started
Knowledge Point 1:python Installation
Knowledge point 2: Common data Analysis Library NumPy, Scipy, Pandas, matplotlib installation
Knowledge point 3: Common Advanced Data Analysis library Scikit-learn, NLTK installation
Installation and use of Knowledge point 4:ipython
A brief introduction to the difference between knowledge point 5:python2 and Python3
Practical projects: Python's common scientific calculations
Second lesson preparing data and NumPy
Knowledge point 1: Multidimensional array objects
Knowledge point 2: element level processing functions
Knowledge point 3: Using Arrays for data processing
Knowledge point 4: File input and output
Knowledge point 5: linear algebra-related functions and basic knowledge of linear algebra
Knowledge Point 6: generation of random numbers
Real-World project: Demonstrate NumPy's multidimensional array and linear algebra matrix operation with actual code, and data input and output
Lesson three Python data analysis main pandas
Knowledge point 1: Basic data structure
Knowledge point 2: Basic functions: Index, select, Filter, sort ...
Knowledge point 3: basic statistical functions
Knowledge point 4: Missing data processing
Point of Knowledge 5: Hierarchical index
Real-World project: Demonstrate pandas processing and statistics through actual code
Lesson four data acquisition and processing
Knowledge point 1: Common data formats and patterns in industry
Knowledge point 2:python to read and write data in different formats
Review of Knowledge point 3:pandas data processing
Knowledge point 4: Simple data crawling and parsing
Knowledge point 5: Regular Expressions: Quickly capture the information you want
Practical project: Simple Web crawling and data parsing processing
Lesson Five data Visualization matplotlib
Knowledge point 1: Fundamentals of Information visualization and data visualization
Knowledge point 2: common ways to visualize
Knowledge point 3: How to design visualization solutions for data characteristics
Actual combat project: A typical visual implementation (provide classroom Ipython code example)
Lesson VI using NLTK for python text analysis
Knowledge point 1: participle
Knowledge point 2: POS tagging
Knowledge point 3: Sentiment analysis
Knowledge point 4: Word reduction
Knowledge point 5: Spell check
Knowledge Point 6: Text categorization
Real-Combat project: A typical text categorization process implementation
Seventh Lesson Python Social network analysis Igraph
Knowledge point 1: Introduction to social network analysis metrics
Knowledge Point 2:pagerank algorithm
Introduction of multiple community discovery algorithms in Knowledge point 3:igraph
Real-life projects: How to construct a graph, node, edge operation, and basic graph algorithm use and visualization cases
Eighth Lesson Python machine learning Scikit-learn
Introduction to Knowledge point 1:scikit-learn
Knowledge Point 2: The process of machine learning: taking Scikit-learn as an example
Optimization method of Knowledge point 3:scikit-learn (parallel processing, cython use, etc.)
Real-Combat project: Using Sklearn to perform machine learning feature transformation, modeling, visualization, and final model evaluation with handwritten numeral recognition and price estimation as an example
Nineth Course Data Science complete case
Knowledge point 1: Data acquisition and analysis: The football team you love
Knowledge point 2: Use the "data" eye to see the ball: "A person to complete the CCTV data statistics"
Knowledge point 3: Player stats and Visualizations: "Who is the best player in the end?" ”
Real-world projects: from crawling data, parsing data, analyzing data, to visualization, modeling complete walk through, from the actual case of a glimpse into the complete work flow of mathematics science
Tenth Lesson Python distributed computing
Knowledge Point 1:python Multi-process module multiprocessing
Knowledge Point 2:python using the Hadoop distributed computing Library Mrjob
Knowledge point 3:python using the Spark distributed computing Library Pyspark
Practical projects: Using MapReduce and spark to implement WordCount


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Python Data Analysis class