Python for Data Science

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

Delivery Methods

Delivery Method	Duration
Classroom Instructor-led classroom-based training. Classes are scheduled at various conference centres in the Sandton area or your premises. Stationary and printed manuals or online resources are included. Refreshments, including 2 tea breaks and a cooked meal for lunch are provided. Training is between 9 am to 4 pm. Classroom	2 Days	Get a Quote
Live Virtual Training This course is delivered live via Microsoft Teams. You will be able to see and hear the instructor, view their whiteboard, and ask questions or communicate in the chat. Attend from the comfort of your own home or private office. Live Virtual Training	2 Days	Get a Quote

Discounts Available

Save up to 10% by booking and paying 10 business days before the course.

Brochure:

Information may change without notice.

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

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