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
Base Cost
$550.00
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If you are currently resident in South Africa you will need to pay VAT at 15%.
Duration:
2 Days
Brochure:
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. 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
To apply for a booking, click on the
relevant "Book Now" button below.
Note: places are only fully secured once payment has been
made.
| Start Date | Branch | Duration | Status | |
|---|---|---|---|---|
| Mon 23 Mar 2020 | Woodmead | 2 Days | Provisional | |
| Mon 29 Jun 2020 | Woodmead | 2 Days | Provisional | |
| Mon 28 Sep 2020 | Woodmead | 2 Days | Provisional |