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Home » Technology » Big Data & Data Science » SciPy Recipes

Course Skill Level:

Foundational

Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:

    SCIPYRL21E09

Who should attend & recommended skills:

Developers with basic Python & IT skills

Who should attend & recommended skills

  • Developers who want to tackle the most sophisticated problems associated with scientific computing and data manipulation using SciPy.
  • Skill-level: Foundation-level SciPy Recipes skills for Intermediate skilled team members. This is not a basic class.
  • Python: Basic (1-2 years’ experience)
  • IT skills: Basic (1-2 years’ experience)

About this course

With the SciPy Stack, you get the power to effectively process, manipulate, and visualize your data using the popular Python language. Utilizing SciPy correctly can sometimes be a very tricky proposition. This course provides the right techniques so you can use SciPy to perform different data science tasks with ease. This course includes hands-on recipes for using the different components of the SciPy Stack such as NumPy, SciPy, matplotlib, and pandas, among others. You will use these libraries to solve real-world problems in linear algebra, numerical analysis, data visualization, and much more. The recipes included in the course will ensure you get a practical understanding not only of how a particular feature in SciPy Stack works, but also of its application to real-world problems. The independent nature of the recipes also ensure that you can pick up any one and learn about a particular feature of SciPy without reading through the other recipes, thus making the course a very handy and useful guide.

Skills acquired & topics covered

  • A wide range of data science tasks using SciPy, NumPy, pandas, and matplotlib
  • Effective recipes on advanced scientific computations, statistics, data wrangling, data visualization, and more
  • Solving your data-related problems using SciPy, on-the-go
  • Getting a solid foundation in scientific computing using Python
  • Mastering common tasks related to SciPy and associated libraries such as NumPy, pandas, and matplotlib
  • Performing mathematical operations such as linear algebra and work with the statistical and probability functions in SciPy
  • Mastering advanced computing such as Discrete Fourier Transform and K-means with the SciPy Stack
  • Implementing data wrangling tasks efficiently using pandas
  • Visualizing your data through various graphs and charts using matplotlib

Course breakdown / modules

  • Introduction
  • Installing Anaconda on Windows
  • Installing Anaconda on macOS
  • Installing Anaconda on Linux
  • Checking the Anaconda installation
  • Installing SciPy from a binary distribution on Windows
  • Installing SciPy from a binary distribution on macOS
  • Installing SciPy from source on Linux
  • Installing optional packages with conda
  • Installing packages with pip
  • Setting up a virtual environment with conda
  • Creating a virtual environment for development with conda
  • Creating a conda environment with a different version of a package
  • Using conda environments to run different versions of Python
  • Creating virtual environments with venv
  • Running SciPy in a script
  • Running SciPy in Jupyter
  • Running SciPy in Spyder
  • Running SciPy in PyCharm

  • Introduction
  • Creating NumPy arrays
  • Querying and changing the shape of an array
  • Storing and retrieving NumPy arrays
  • Indexing
  • Operations on arrays
  • Using masked arrays to represent invalid data
  • Using object arrays to store heterogeneous data
  • Defining, symbolically, a function operating on arrays

  • Introduction
  • Creating two-dimensional plots of functions and data
  • Generating multiple plots in a single figure
  • Setting line styles and markers
  • Using different backends to display graphs
  • Saving plots to disk
  • Annotating graphs
  • Generating histograms and box plots
  • Creating three-dimensional plots
  • Generating interactive displays in the Jupyter Notebook
  • Object-oriented graph creation using Artist objects
  • Creating a map with cartopy

  • Creating Series objects
  • Creating DataFrame objects
  • Inserting and deleting columns to a DataFrame
  • Inserting and deleting rows to a DataFrame
  • Selecting items by row indexes and column labels
  • Selecting items by integer location
  • Selecting items using mixed indexing
  • Accessing, selecting, and modifying data
  • Selecting rows using Boolean selection
  • Reading and storing data in different formats
  • Data displays employing different kinds of visual representation
  • How to apply numerical functions and operations to Series and DataFrame objects
  • Computing statistical functions on Series and DataFrame objects
  • How to sort data in Series and DataFrame objects
  • Performing merging, joins, concatenation, and grouping

  • Introduction
  • Matrix operations and functions on two-dimensional arrays
  • Solving linear systems using matrices
  • Calculating the null space of a matrix
  • Calculating the LU decompositions of a matrix
  • Calculating the QR decomposition of a matrix
  • Calculating the eigenvalue and eigenvector of a matrix
  • Diagonalizing a matrix
  • Calculating the Jordan form of a matrix
  • Calculating the singular value decomposition of a matrix
  • Creating a sparse matrix
  • Computations on top of a sparse matrix

  • Introduction
  • Non-linear equations and systems
  • System of equations and how to solve it
  • Choosing the solver used to find the solution of equations
  • Solving constrained non-linear optimization problems in several variables
  • Solving one-dimensional optimization problems
  • Solving multidimensional non-linear equations using the Newton-Krylov method
  • Solving multidimensional non-linear equations using the Anderson method
  • Finding the best linear fit for a set of data
  • Doing non-linear regression for a set of data
  • Regression

  • Introduction
  • Physical and mathematical constants available in SciPy
  • Using constants in the CODATA database
  • Bessel functions
  • Error functions
  • Orthogonal polynomials functions
  • Gamma function
  • The Riemann zeta function
  • Airy and Bairy functions
  • The Bessel and Struve functions

  • Introduction
  • Integration
  • Computing integrals using a Gaussian quadrature
  • Computing integrals with weighting functions
  • Computing multiple integrals
  • Interpolation
  • Computing a polynomial interpolation for a set of data points
  • Univariate interpolation
  • Finding a cubic spline that interpolates a set of data
  • Defining a B-spline for a given set of control points
  • Differentiation
  • Solving a one-dimensional ordinary differential equation
  • Solving a system of ordinary differential equations
  • Solving differential equations and systems with parameters
  • Using ode and the objected-oriented interface to solve differential equations

  • Introduction
  • Computing the probability mass function of a discrete random variable
  • Computing the probability density function of a continuous random variable
  • Computing the cumulative distribution function for a random variable
  • Computing the values of inverse probabilities associated with a random variable
  • Computing the average, standard deviation, and higher moments of a distribution
  • Computing probabilities associated with the multivariate Gaussian distribution
  • Computing the summary statistics of a dataset

  • Discrete Fourier transforms
  • Computing the discrete Fourier transform (DFT) of a data series using the FFT algorithm
  • Computing the inverse DFT of a data series
  • Computing signal construction
  • Getting started with filters
  • Computing the DFT for two-dimensional data
  • How to find the DFT of the derivative of a function
  • Computing the convolution of two functions
  • Mathematical imaging
  • Computing pairwise distances from a dataset, using different distance metrics
  • How to identify neighborhoods and nearest neighbors for a dataset and a given metric
  • Nearest neighbor’s regression

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