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+46 (0)8 506 668 00
+46 (0)8 506 668 00

Introduction to Python for Data Analytics

Course 4509

  • Duration: 1 day
  • Labs: Yes
  • Language: English
  • Level: Foundation

Gain the skills you need to analyse and visualise data with Python. In this Python training, you learn the fundamentals of Python programming with a focus on data analytics, and work with popular statistical computing libraries — like numPy, Pandas, sciPy, and Scikit-learn — that allow you to begin analysing data to answer key business questions.

The eBooks and on-demand courses provided with this offering are a great way to explore your interest in the topics covered in the instructor-led course. At any time during your annual access to this offering, you may attend one of our 1-day review sessions, Course 4509: Introduction to Python for Data Analytics.

Introduction to Python for Data Analytics Delivery Methods

  • Choose from instructor-led and blended delivery methods
  • Attend online or bring this course to your facility

Introduction to Python for Data Analytics Course Information

In this course, you will learn how to:

  • Use Python for statistical data analysis.
  • Generate summary statistics with pandas.
  • Clean, transform, and reshape data.
  • Glean insights from the data through visualisation.

Prerequisites

None.

Python for Data Analytics Training Outline

  • Working with data stored in pandas data frame objects
  • Generating summary statistics with pandas
  • Statistical modelling using NumPy and SciPy
  • Plotting and visualising data using MatPlotLib
  • Making use of scikit-Learn to mine and analyse a data set

Course Outline & On-Demand Training Content

  • Managing Data Science
  • Hands-On Data Analysis with Pandas
  • Hands-On Python Natural Language Processing
  • Data Science Algorithms in a Week - Second Edition
  • Machine Learning for Algorithmic Trading - Second Edition
  • Hands-On Neural Networks
  • Mastering Machine Learning Algorithms - Second Edition
  • Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits
  • Hands-On Data Science for Marketing
  • Python Feature Engineering Cookbook
  • Artificial Intelligence By Example - Second Edition
  • Hands-On Machine Learning on Google Cloud Platform
  • Exploratory Data Analysis with Pandas and Python 3.x
  • Advanced Data Structures and Algorithms in Python
Get This Course 7 700 kr 12 900 kr /Year For Team Prices Call: +46 (0)8 506 668 00
  • 1-day instructor-led training course
  • Practice exams and prep questions
  • One-on-one after course instructor coaching
  • Exam voucher included
#4509
  • okt 30 9:00 - 16:30 CET
    Stockholm or Virtual
  • okt 30 10:00 - 17:30 CET
    London or Virtual
  • nov 27 15:00 - 22:30 CET
    Virtual
  • jan 29 9:00 - 16:30 CET
    Stockholm or Virtual
  • jan 29 10:00 - 17:30 CET
    London or Virtual
  • jan 30 15:00 - 22:30 CET
    Virtual
  • feb 26 15:00 - 22:30 CET
    Virtual
  • apr 10 15:00 - 22:30 CEST
    Virtual
  • maj 7 15:00 - 22:30 CEST
    Virtual
  • maj 7 9:00 - 16:30 CEST
    Virtual
  • maj 7 10:00 - 17:30 CEST
    Virtual
  • jul 29 15:00 - 22:30 CEST
    Virtual
  • jul 29 9:00 - 16:30 CEST
    Virtual
  • jul 29 10:00 - 17:30 CEST
    Virtual
  • sep 23 15:00 - 22:30 CEST
    Virtual

Scroll to view additional course dates

  • 12 eBooks
  • 2 on-demand video courses
  • 1-day instructor-led training course
#70I7

  • Bring this or any training to your organisation

  • Full-scale programme development

  • Delivered when, where, and how you want it
  • Blended learning models
  • Tailored content
  • Expert team coaching
#4509
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Course FAQs

Just an interest in gaining foundational knowledge of data science. This data scientist training course is designed for technical and non-technical beginners.

An outline of the content you will receive can be seen above.

Once payment is received, you will receive an email from Learning Tree with all the links and information you need to get started.

Once you are enrolled in the programme, specific details and dates will be sent to you.

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