Course Data Analysis with Python

In the course Data Analysis with Python you will learn how to use the Python language and Python libraries in Data Analysis projects.

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  • Course Data Analysis with Python : Content

    Python Overview

    The course Data Analysis with Python starts with a bird's eye view of the Python syntax aspects that are important in Data Analysis projects. Variables, data types, functions, flow control, comprehensions, classes, modules and packages are discussed. The operation of the Jupyter notebooks, the IPython shell and installing Python packages in Anaconda are also treated.


    Next the course Data Analysis with Python pays attention to the NumPy package with which large data sets can be processed very efficiently. NumPy's ndarray object and its methods are treated and attention is paid to the different array manipulation techniques with broadcasting and vectorized operations.


    Then use of the Pandas library for data analysis is on the schedule of the course Data Analysis with Python. The pandas library introduces two new data structures in Python that use Numpy and are therefore fast. The data structures are DataFrame and Series and extensive details are given on how to use them for data analysis when inspecting, selecting, filtering, combining and grouping data.


    Also discussed in the course Data Analysis with Python is the MatPlotlib library, which is closely integrated with NumPy and is a very powerful tool for creating and plotting complex data relationships.


    Finally attention is paid to the essentials of the Scikit-Learn library for modeling. The course Data Analysis with Python uses many practical examples and shows how one- and two- and three-dimensional data sets can be visualized.

  • Course Data Analysis with Python : Training

    Audience Course Data Analyse with Python

    The course Data Analysis with Python is intended for data analysts who want to use Python and the Python libraries in Data Analysis projects.

    Prerequisites training Data Analyse with Python

    To participate in this course knowledge of and experience with any programming language or package such as SPSS, Matlab or VBA is desirable. The course starts with a discussion of the principles of the Python programming language.

    Realization course Data Analyse with Python

    The theory is discussed on the basis of presentation slides. Illustrative demos clarify the concepts. The theory is interchanged with exercises. The Anaconda distribution with Jupyter notebooks is used as a development environment. Course times are from 9:30 to 16:30.

    Official Certificate Data Analysis with Python

    After successful completion of the course participants receive an official certificate Data Analysis with Python.

  • Course Data Analysis with Python : Modules

    Module 1 : Python Language Syntax

    Module 2 : Functions and Modules

    Module 3 : Classes and Objects

    Python Features
    Running Python
    Anaconda Distribution
    IPython Shell
    Interactive and Script Mode
    Python Data Types
    Numbers and Strings
    Sequences and Lists
    Sets and Dictionaries
    Python Flow Control
    Exception Handling
    Pass by Value and Reference
    Scope of Variables
    EFAP principle
    What are comprehensions?
    Lambda Operator
    Filter, Reduce and Map
    List comprehensions
    Set and Dictionary comprehensions
    Creating and Using Modules
    import Statement
    from…import Statement
    Creating Classes
    Creating and Using Objects
    Accessing Attributes
    Property Syntax
    Constructors and Destructors
    super Keyword
    Checking Relationships
    issubclass and isinstance
    Overriding Methods

    Module 4 : Numpy

    Module 5 : Pandas

    Module 6 : Data Manipulation

    NumPy Numerical Types
    Data Type objects
    dtype attributes
    Slicing and Indexing
    Array comparisons
    Manipulating array shapes
    Stacking and Splitting arrays
    any(),all(), slicing, reshape()
    Manipulating array shapes
    Methods of ndarray
    Views versus copies
    Pandas DataFrame
    Import Data
    Inspect Data
    Data Visualization
    DataFrame Data Types
    Indexing and selection
    Data operations in pandas
    Missing Data
    Hierarchical Indexing
    Plotting with Pandas
    Combining Datasets
    Exploratory Data Analysis
    Indexing Data Frames
    .loc and .iloc Accessor
    Slicing and Indexing a Series
    Filtering with Boolean Series
    Zeros and NaNs
    all and any Nonzeros
    Using map Function
    Hierarchical Indexing
    Rearranging Data
    Reshaping by Pivoting
    Transformation and Aggregation
    Grouping Data

    Module 7 : MatplotLib

    Module 8 : Time Series

    Module 9 : SciKitLearn Essentials

    Simple Plots
    Plot format String
    Logarithmic Plots
    Scatter plots
    Fill between
    Legend and Annotations
    Three Dimensional Plots
    Contour Plots
    Indexing Pandas Time Series
    Reading and Slicing Times
    Using a DatetimeIndex
    Reindexing the Index
    Separating and Resampling
    Rolling mean and Frequency
    Resample and Roll with it
    Manipulating Time Series
    Method chaining and Filtering
    Missing values and Interpolation
    Time Zones and Conversion
    Plotting Time Series
    SkiKit Learn library
    Machine learning essentials
    Supervised and Unsupervised
    Feature matrix
    Target array
    Estimator API
    Fit method
    Predict method
    Model Selection
    Linear Regression
    Logistic Regression
  • Course Data Analysis with Python : General

    Read general course information
  • Course Data Analysis with Python : Reviews

  • Course Data Analysis with Python : Certificate