In the interests of protecting our staff, trainers and you, our clients, we will be suspending all instructor-led training from 15th July 2020 until it is safe again.
Our company is working remotely to serve our clients and we are still offering courses via webinar. If you feel your online course didn’t meet that standard, you are welcome to re-attend your course in class when we re-open.
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
|Delivery Method||Duration||Price (excl. VAT)|
|Fulltime||2 Days||R 5,500.00|
|Webinar||2 Days||R 4,600.00|
Save up to 10% by booking and paying 10 business days before the course.
Would be data scientists, scientists, engineers, data analysts.
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)
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)
Convert from array to list or tuple
Standard Linear Algebra operations
Numpy for Matlab users
Matplotlib 2D plotting
Matplotlib and Pylab
Fine tuning your plot
Plotting more than one curve
Visualising matrix data
Numerical Methods (scipy)
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
Supervised Learning, Naive Bayes, Decision Trees, Random Forests.