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SciPy Recipes

  • Course Code: Data Analysis / BI - SciPy Recipes
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 3 Days Audience: This course is geared for those who wants to Tackle the most sophisticated problems associated with scientific computing and data manipulation using SciPy.

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level SciPy Recipes skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Tackle the most sophisticated problems associated with scientific computing and data manipulation using SciPy.  
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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. 

Working in a hands-on learning environment, led by our SciPy Recipes expert instructor, students will learn about and explore: 

  • Covers 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 
  • A must-have course if you’re looking to solve your data-related problems using SciPy, on-the-go 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Get a solid foundation in scientific computing using Python 
  • Master common tasks related to SciPy and associated libraries such as NumPy, pandas, and matplotlib 
  • Perform mathematical operations such as linear algebra and work with the statistical and probability functions in SciPy 
  • Master advanced computing such as Discrete Fourier Transform and K-means with the SciPy Stack 
  • Implement data wrangling tasks efficiently using pandas 
  • Visualize your data through various graphs and charts using matplotlib 

Audience & Pre-Requisites 

This course is geared for attendees who wish to Tackle the most sophisticated problems associated with scientific computing and data manipulation using SciPy 

Pre-Requisites:  Students should have  

  • developers with some knowledge of Python and IT skills.  

Course Agenda / Topics 

  1. Getting to Know the Tools 
  • Getting to Know the Tools 
  • 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 
  1. Getting Started with NumPy 
  • Getting Started with NumPy 
  • 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 
  1. Using Matplotlib to Create Graphs 
  • Using Matplotlib to Create Graphs 
  • 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 
  1. Data Wrangling with pandas 
  • Data Wrangling with pandas 
  • 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 
  1. Matrices and Linear Algebra 
  • Matrices and Linear Algebra 
  • 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 
  1. Solving Equations and Optimization 
  • Solving Equations and Optimization 
  • 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 
  1. Constants and Special Functions 
  • Constants and Special Functions 
  • 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 
  1. Calculus, Interpolation, and Differential Equations 
  • Calculus, Interpolation, and Differential Equations 
  • 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 
  1. Statistics and Probability 
  • Statistics and Probability 
  • 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 
  1. Advanced Computations with SciPy 
  • Advanced Computations with SciPy 
  • 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 neighbors regression 
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