Python in Data Analysis: Data Processing & Analysis, Machine Learning, Big Data and Cloud Computing

Recommended Duration 4 days (minimum 6 hours + breaks each day)
Requirements basic Python skills
Intended Audience Python Developers, Analysts, Financists, Scientists, Researchers
Language Polish or/and English
Main Technologies IPython, Jupyter Notebook, Pandas, matplotlib, scikit-learn, dask, Amazon Cloud

Python has always been popular among scientists and researchers. Thanks to that, it has one of the best environments for data analysis. A lot of advanced libraries and tools are developed. However, variety of all of them confuses newbies. This workshop removes this confusion.


Syllabus

  1. Tools
    1. IPython
    2. Jupyter Notebook
    3. IDE (PyCharm, Visual Studio Code)
    4. pip - Packet Manager
    5. Working with virtualenv
    6. Executing Programs
  2. Data Visualisation with matplotlib and Seaborn
    1. Useful Links
    2. Idioms
    3. Plot Types
    4. Plot Customization
    5. Advanced Plots with Seaborn
  3. Data Processing with Pandas
    1. Loading and Exporting Data
    2. Working with Data Series
    3. Basic Data Structure: DataFrame
    4. Processing Dates
    5. Processing Strings
    6. Processing Missing Values
    7. Joins
    8. Grouping and Aggregation (Split-Apply-Combine Pattern)
    9. Pivot Tables
    10. Working with Indexes
  4. Machine Learning with scikit-learn
    1. Supervised and Unsupervised Learning
    2. Features
    3. Features Normalization
    4. Classification, Regression, Clustering and Other Classes of Problems
    5. Evaluation and Cross-validation
    6. Choosing Model Parameters
    7. Choosing Right Features
    8. Support Vector Machines
    9. Bayess Filter
    10. Decision Trees
    11. Neural Networks
    12. k-means Algorithm
    13. Principial Component Analysis
  5. Distributed Processing with Dask
    1. Basic Principles and Good Practices of Parallelizing Computations
    2. Configuring Computing Cloud on Amazon EC2
    3. Creating Local Cloud in Dask
    4. Creating Amazon Cloud in Dask
    5. Executing Code on Nodes
    6. Basics Data Structures in Dask
    7. Loading Data
    8. Data Aggregation
    9. Debugging Dask Cloud
    10. Profiling Cloud

Are you interested?

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Don’t hesitate to contact me if you’re interested or you want to ask a question. You can expect:

No risk – if you were disappointed after my workshop, you would not pay for it.

A customized syllabus after a free discovery call. During the call, I’ll analyze your needs and we’ll decide on next steps. Please include your availability for the call.

   chris@medrela.com