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Details

Python has a fantastic array of modules that are useful to data scientists. Easily enabling data processing, modelling, linear algebra and much more. Python has rich data visualization capabilities, fast numerical methods, statistical and data analysis tools and machine learning libraries.

In this course you will learn how to:

  • Perform complex maths in Python
  • Solve mathematical equations symbolically using python
  • Use numerical methods to find solutions to mathematical problems
  • Analyze data
  • Create amazing visualizations
  • Interact with Power BI
  • Introduce machine learning

Cost

$550.00 If you are currently resident in South Africa you will need to pay VAT at 15%.

Duration:

2 Days

Brochure:

Download Brochure


Prices have changed from 1 March 2018. Information may change without notice.

Delivery Method

Instructor Led classroom based training. Scheduled classes are normally held in Woodmead - near to Sandton in Johannesburg, Gauteng, South Africa. Stationary and textbook included. Refreshments, including 2 tea breaks and a cooked meal for lunch are provided for full time courses. Contact hours are between 9am to 4pm.

Audience

Would be data scientists, scientists, engineers, data analysts.

Pre-Requisites

Introduction to programming using Python or equivalent knowledge.  See https://www.leadingtraining.co.za/introduction_to_programming_training_course

Course Outline / Curriculum

Symbolic Math (symy)

Symbols

isympy

Numeric types

Differentiation and Integration

Ordinary differential equations

Series expansions and plotting

Linear equations and matrix inversion

Non linear equations

Output: LATEX interface and pretty-printing

Numerical Python (numpy)

Introduction

Arrays (Matrices)

Convert from array to list or tuple

Standard Linear Algebra operations

Numpy for Matlab users

Visualising Data

Matplotlib 2D plotting

Matplotlib and Pylab

pyplot, numpy

Fine tuning your plot

Plotting more than one curve

Histograms

Visualising matrix data

3D Plots

 

 

Numerical Methods (scipy)

Overview

Numerical integration

Solving ordinary differential equations

Finding roots using the bisection and fsolve method

Fast Fourier Transforms

Interpolation and curve fitting, including Linear regression

Intro to Machine Learning

Predictive models

Supervised Learning, Naive Bayes, Decision Trees, Random Forests.

Unsupervised Learning

 

 

Schedule Dates and Booking

There are currently no scheduled dates.

Please note that this course needs a minimum of 6 delegates to schedule a course. You can choose to be added to the waiting list by clicking the button below and we will contact you when we have enough delegates interested. Should we not get enough delegates, we will refund or credit your paid booking.

Add me to the waiting list

Should you need this course urgently, the following options are available:

  1. Pay for 6 delegates (whether you have them or not) and we will schedule the course as soon as possible.
  2. If you have fewer delegates and cannot pay for 6, we can negotiate a shortened course where some of the time will be spent in blended learning - watching videos and doing tutorials and exercises with some contact time with the trainer. We would want to discuss what your core needs are so that we cover those aspects. You need to have paid for 3 delegates at least.