HemSök efter kurserPractical Data Science with Python

Practical Data Science with Python


Utbildningsformer
Remote

Längd
5 dagar

Pris
59995 kr

An introduction to Statistics, 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 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.

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 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 Python programs to analyse data
  • Understand a Python program in the context of data analytics
  • Explore and visualise data using Python
  • Build working machine learning models

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

  • Introduction to Data Science
  • 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
  • Introduction in Machine Learning