Data Science using Python Instructor-led

Overview

Data science is an interdisciplinary field that uses statistical modelling to uncover patterns in data that lead to key insights about businesses and their operations, and can test how changes to particular variables will influence markets, performance and other factors.

In this course you will learn the maths behind data science, how to use Python to model, analyse and visualise data, and how to apply machine learning and deep learning.

You will also learn which tools to use for enterprise-level data science projects, and tackle your own data science project.

Who is it for?

Solution architects, software developers, business analysts, engineers, heads of delivery, engineering managers.

Certificates are awarded on the following basis:

• Certificate of completion: Cumulative average of 80% in online tests
• Certificate of participation: Cumulative average of 50%-79% for online tests
• Certificate of attendance: Cumulative average of less than 50% for online tests

Learning outcomes

You should emerge from this course understanding:

• Data science and your role as an efficient data scientist;
• Mathematical and statistical fundamentals for data science;
• Mathematical techniques to handle and to prepare data;
• Machine learning models and evolution techniques;

And able to:

• Use Python for data science applications;
• Use various libraries for data science applications;
• Execute use cases in Python; and
• Execute real-world projects.

You will cover

Python for data science; the maths behind data science; data analysis and data visualisation; exploratory data analysis; feature engineering; exploratory data analysis use cases; machine learning; deep learning; applied AI; tools for an enterprise-level data science project;. You will also tackle a data science project.

Course prerequisites

• Basic knowledge of algebra, geometry, object-oriented programming or object-oriented programming language; and a grasp of statistical and mathematical terms.

Job opportunities

• Data mining engineer, machine learning engineer, data architect, data warehouse architect, commercial intelligence manager, business analyst.