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Practical Data Science with Python

  • CMDBID 105325
  • Course Code QADMPPDS
  • Duration 5 Days
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Business Intelligence Course

course overview

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Overview

An introduction to Statistics, R, Python, Analytics, Data Science and Machine Learning. Sets up practitioners with working knowledge of whole field of data science, along with immediate practical knowledge of key analytical tasks.

This 5-day course is hands-on, practical and workshop based. It is the start of an experienced developer’s journey towards becoming a Data Scientist. If you are a software engineer, in business intelligence, or you are an SQL specialist, this is the course for you.

By attending this course you will learn how to become a professional Data Scientist. You're going to be able to demystify and understand the language around data science and understand the core concepts of analytics and automation. You'll also develop practical, hands-on, advanced skills in Python, targeted towards data analysis and Machine Learning and R, so you can create sophisticated statistical models.

Target

Audience

For fledging data science practitioners, and for IT professionals who wish to move to the exciting world of data analytics and machine learning.

Skills Gained

At the end of the course attendees will know:

  • Fundamental concepts of Data Science
  • Methodologies used in Machine Learning
  • Summary statistics and how to use statistical inference to analyse data
  • Hands on R programming language for numerical analysis
  • Hands on Python programming language for numerical analysis
  • Most used simple machine learning algorithms

At the end of the course attendees will be able to:

  • Speak the language of data scientists
  • Write R or Python programs to analyse data
  • Understand a R or Python program in the context of data analytics
  • Explore and visualise data using R or Python
  • Build working machine learning models

Prerequisites

  • GCSE level mathematics or above. Alternatively, familiar and comfortable with logical and mathematical thinking
  • Familiar with basic knowledge of programming: variables, scope, functions

Outline

  • Introduction to Data Science
  • Methods in Machine Learning
  • Introduction to Mathematics
  • Statistics for Data Science
  • Introduction to Python
  • Programming in Python
  • Numerical Python
  • Data Manipulation with Pandas
  • Data Visualisation with Matplotlib and Seaborn
  • Data Exploration
  • Machine Learning Algorithms
  • Python Machine Learning
  • NoSQL Databases
  • Hadoop and Ecosystems

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Thinking about Onsite?

If you need training for 3 or more people, you should ask us about onsite training. Putting aside the obvious location benefit, content can be customised to better meet your business objectives and more can be covered than in a public classroom. Its a cost effective option. One on one training can be delivered too, at reasonable rates.

Submit an enquiry from any page on this site, and let us know you are interested in the requirements box, or simply mention it when we contact you.

All $ prices are in USD unless it’s a NZ or AU date

SPVC = Self Paced Virtual Class

LVC = Live Virtual Class

Please Note: All courses are availaible as Live Virtual Classes

Trusted by over 1/2 million students in 15 countries

Our clients have included prestigious national organisations such as Oxford University Press, multi-national private corporations such as JP Morgan and HSBC, as well as public sector institutions such as the Department of Defence and the Department of Health.