Next Level Python in Data Science [TTPS4876]

Dauer
Ausführung
Vor Ort, Online
Startdatum und Ort

Next Level Python in Data Science [TTPS4876]

Global Knowledge Network Netherlands B.V.
Logo von Global Knowledge Network Netherlands B.V.
Bewertung: starstarstarstarstar_border 7,8 Bildungsangebote von Global Knowledge Network Netherlands B.V. haben eine durchschnittliche Bewertung von 7,8 (aus 127 Bewertungen)

Tipp: Haben Sie Fragen? Für weitere Details einfach auf "Kostenlose Informationen" klicken.

Startdaten und Startorte

placeNieuwegein (Iepenhoeve 5)
10. Okt 2022 bis 14. Okt 2022
Details ansehen
event 10. Oktober 2022, 09:00-16:30, Nieuwegein (Iepenhoeve 5), NL200277.1
event 11. Oktober 2022, 09:00-16:30, Nieuwegein (Iepenhoeve 5), NL200277.2
event 12. Oktober 2022, 09:00-16:30, Nieuwegein (Iepenhoeve 5), NL200277.3
event 13. Oktober 2022, 09:00-16:30, Nieuwegein (Iepenhoeve 5), NL200277.4
event 14. Oktober 2022, 09:00-16:30, Nieuwegein (Iepenhoeve 5), NL200277.5
computer Online: VIRTUAL TRAINING CENTRE
10. Okt 2022 bis 14. Okt 2022
Details ansehen
event 10. Oktober 2022, 09:00-16:30, VIRTUAL TRAINING CENTRE, NL200277V.1
event 11. Oktober 2022, 09:00-16:30, VIRTUAL TRAINING CENTRE, NL200277V.2
event 12. Oktober 2022, 09:00-16:30, VIRTUAL TRAINING CENTRE, NL200277V.3
event 13. Oktober 2022, 09:00-16:30, VIRTUAL TRAINING CENTRE, NL200277V.4
event 14. Oktober 2022, 09:00-16:30, VIRTUAL TRAINING CENTRE, NL200277V.5

Beschreibung

Ontdek de verschillende trainingsmogelijkheden bij Global Knowledge

Online of op locatie er is altijd een vorm die bij je past.

Kies op welke manier jij of je team graag een training wilt volgen. Global Knowledge bied je verschillende trainingsmogelijkheden. Je kunt kiezen uit o.a. klassikaal, Virtueel Klassikaal (online), e-Learning en maatwerk. Met onze Blended oplossing kun je de verschillende trainingsvormen combineren.

OVERVIEW

Next Level Python for Data Science and /or Machine Learning covers the essentials of using Python as a tool for data scientists to perform exploratory data analysis, complex visualizations, and large-scale distributed processing on “Big Data”. In this course we cover essential mathematical and statistics libraries such as NumPy, Pandas, SciPy, SciKit-Learn, frameworks like TensorFlow and Spark, as well as visualization tools like matplotlib, PIL, and Seaborn.  This course is ‘intermediate level’ as it assumes that attendees have solid data analytics and data science background and have basic Python knowledge.  Topics are introductory in nature, but are covered in-depth, geared for e…

Gesamte Beschreibung lesen

Frequently asked questions

Es wurden noch keine FAQ hinterlegt. Falls Sie Fragen haben oder Unterstützung benötigen, kontaktieren Sie unseren Kundenservice. Wir helfen gerne weiter!

Noch nicht den perfekten Kurs gefunden? Verwandte Themen: Data Science Python, TensorFlow, Python, Data Science und Big Data.

Ontdek de verschillende trainingsmogelijkheden bij Global Knowledge

Online of op locatie er is altijd een vorm die bij je past.

Kies op welke manier jij of je team graag een training wilt volgen. Global Knowledge bied je verschillende trainingsmogelijkheden. Je kunt kiezen uit o.a. klassikaal, Virtueel Klassikaal (online), e-Learning en maatwerk. Met onze Blended oplossing kun je de verschillende trainingsvormen combineren.

OVERVIEW

Next Level Python for Data Science and /or Machine Learning covers the essentials of using Python as a tool for data scientists to perform exploratory data analysis, complex visualizations, and large-scale distributed processing on “Big Data”. In this course we cover essential mathematical and statistics libraries such as NumPy, Pandas, SciPy, SciKit-Learn, frameworks like TensorFlow and Spark, as well as visualization tools like matplotlib, PIL, and Seaborn.  This course is ‘intermediate level’ as it assumes that attendees have solid data analytics and data science background and have basic Python knowledge.  Topics are introductory in nature, but are covered in-depth, geared for experienced students.

OBJECTIVES

This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises.  Our engaging instructors and mentors are highly experienced practitioners who bring years of current "on-the-job" experience into every classroom. 

Working in a hands-on learning environment, guided by our expert team, attendees will learn how to:

  • How to work with Python in a Data Science Context
  • How to use NumPy, Pandas, and MatPlotLib
  • How to create and process images with PIL
  • How to visualize with Seaborn
  • Key features of SciPy and Scikit Learn
  • How to interact with Spark using DataFrames
  • How to use SparkSQL, MLib, and Streaming in Big Data

AUDIENCE

This course is geared for experienced data analysts, developers, engineers or anyone tasked with utilizing Python for data analytics or eventual machine learning tasks. 

NEXT STEP

Our Python tracks include a wide variety of follow-on courses and learning paths for leveraging Python for next-level web development, data science / machine learning, networking, task automation, security and other topics. Please see the attached Python Training Suite list of courses, or inquire for recommendations based on your specific role and goals.

 

CONTENT

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most. Topics, agenda and labs are subject to change, and may adjust during live delivery based on audience needs and skill-level.

 
Section 1: Python for Data Science

1.Python Review (Optional)

  • Python Language
  • Essential Syntax
  • Lists, Sets, Dictionaries, and Comprehensions
  • Functions
  • Classes, Modules, and imports
  • Exceptions

2.iPython

  • iPython basics
  • Terminal and GUI shells
  • Creating and using notebooks
  • Saving and loading notebooks
  • Ad hoc data visualization
  • Web Notebooks (Jupyter)

3.numpy

  • numpy basics
  • Creating arrays
  • Indexing and slicing
  • Large number sets
  • Transforming data
  • Advanced tricks

4.scipy

  • What can scipy do?
  • Most useful functions
  • Curve fitting
  • Modeling
  • Data visualization
  • Statistics

5.A tour of scipy subpackages

  • Clustering
  • Physical and mathematical Constants
  • FFTs
  • Integral and differential solvers
  • Interpolation and smoothing
  • Input and Output
  • Linear Algebra
  • Image Processing
  • Distance Regression
  • Root-finding
  • Signal Processing
  • Sparse Matrices
  • Spatial data and algorithms
  • Statistical distributions and functions
  • C/C++ Integration

6.pandas

  • pandas overview
  • Dataframes
  • Reading and writing data
  • Data alignment and reshaping
  • Fancy indexing and slicing
  • Merging and joining data sets

7.matplotlib

  • Creating a basic plot
  • Commonly used plots
  • Ad hoc data visualization
  • Advanced usage
  • Exporting images

8.The Python Imaging Library (PIL)

  • PIL overview
  • Core image library
  • Image processing
  • Displaying images

9.seaborn

  • Seaborn overview
  • Bivariate and univariate plots
  • Visualizing Linear Regressions
  • Visualizing Data Matrices
  • Working with Time Series data

10.SciKit-Learn Machine Learning Essentials

  • SciKit overview
  • SciKit-Learn overview
  • Algorithms Overview
  • Classification, Regression, Clustering, and Dimensionality Reduction
  • SciKit Demo

11.TensorFlow Overview

  • TensorFlow overview
  • Keras
  • Getting Started with TensorFlow
  • Session: Python on Spark

12.PySpark Overview

  • Python and Spark
  • SciKit-Learn vs. Spark MLib
  • Python at Scale
  • PySpark Demo

13.RDDs and DataFrames

  • DataFrames and Resilient Distributed Datasets (RDDs)
  • Partitions
  • Adding variables to a DataFrame
  • DataFrame Types
  • DataFrame Operations
  • Dependent vs. Independent variables
  • Map/Reduce with DataFrames

14.Spark SQL

  • Spark SQL Overview
  • Data stores: HDFS, Cassandra, HBase, Hive, and S3
  • Table Definitions
  • Queries

15.(Optional): Spark MLib

  • MLib overview
  • MLib Algorithms Overview
  • Classification Algorithms
  • Regression Algorithms
  • Decision Trees and forests
  • Recommendation with ALS
  • Clustering Algorithms
  • Machine Learning Pipelines
  • Linear Algebra (SVD, PCA)
  • Statistics in MLib

16.(Optional) Spark Streaming

  • Streaming overview
  • Integrating Spark SQL, MLlib, and Streaming

Werden Sie über neue Bewertungen benachrichtigt

Es wurden noch keine Bewertungen geschrieben.

Schreiben Sie eine Bewertung

Haben Sie Erfahrung mit diesem Kurs? Schreiben Sie jetzt eine Bewertung und helfen Sie Anderen dabei die richtige Weiterbildung zu wählen. Als Dankeschön spenden wir € 1,00 an Stiftung Edukans.

Es wurden noch keine FAQ hinterlegt. Falls Sie Fragen haben oder Unterstützung benötigen, kontaktieren Sie unseren Kundenservice. Wir helfen gerne weiter!

Bitte füllen Sie das Formular so vollständig wie möglich aus

(optional)
(optional)
(optional)
(optional)
(optional)
(optional)

Haben Sie noch Fragen?

(optional)
Damit Ihnen per E-Mail oder Telefon weitergeholfen werden kann, speichern wir Ihre Daten.
Mehr Informationen dazu finden Sie in unseren Datenschutzbestimmungen.