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Course Data Analysis with Python

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  • Course Data Analysis with Python : Content

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

    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.

    Numpy

    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.

    Pandas

    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.

    MatPlotLib

    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.

    Scikit-Learn

    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
  • 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
    Encapsulation
    Inheritance
    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
    ravel(),flatten(),transpose()
    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
    Subplots
    Histograms
    Logarithmic Plots
    Scatter plots
    Fill between
    Legend and Annotations
    Three Dimensional Plots
    Contour Plots
    Transformations
    Projections
    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
    Hyperparameters
    Fit method
    Predict method
    Model Selection
    Linear Regression
    Logistic Regression
  • Course Data Analysis with Python : General

    Course Forms

    All our courses are classroom courses in which the students are guided through the material on the basis of an experienced trainer with in-depth material knowledge. Theory is always interspersed with exercises.

    Customization

    We also do custom classes and then adjust the course content to your wishes. On request we will also discuss your practical cases.

    Course times

    The course times are from 9.30 to 16.30. But we are flexible in this. Sometimes people have to bring children to the daycare and other times are more convenient for them. In good consultation we can then agree on different course times.

    Hardware

    We take care of the computers on which the course can be held. The software required for the course has already been installed on these computers. You do not have to bring a laptop to participate in the course. If you prefer to work on your own laptop, you can take it with you if you wish. The required software is then installed at the start of the course.

    Software

    Our courses are generally given with Open Source software such as Eclipse, IntelliJ, Tomcat, Pycharm, Anaconda and Netbeans. You will receive the digital course material to take home after the course.

    Lunch

    The course includes lunch that we use in a restaurant within walking distance of the course room.

    Locations

    The courses are planned at various places in the country. A course takes place at a location if at least 3 people register for that location. If there are registrations for different locations, the course will take place at our main location, Houten which is just below Utrecht. A course at our main location also takes place with 2 registrations and regularly with 1 registration. And we also do courses at the customer’s location if they appreciate that.

    Evaluations

    At the end of each course, participants are requested to evaluate the course in terms of course content, course material, trainer and location. The evaluation form can be found at https://www.klantenvertellen.nl/reviews/1039545/spiraltrain?lang=en. The evaluations of previous participants and previous courses can also be found there.

    Copyright

    The intellectual property rights of the published course content, also referred to as an information sheet, belong to SpiralTrain. It is not allowed to publish the course information, the information sheet, in written or digital form without the explicit permission of SpiralTrain. The course content is to be understood as the description of the course content in sentences as well as the division of the course into modules and topics in the modules.

  • Course Data Analysis with Python : Reviews

  • Course Data Analysis with Python : Certificate