Basics of HiStudy Theme

Building Scalable Data Pipelines with Cloud Technologies

Learn how to design and implement scalable data pipelines using modern cloud services like AWS, enabling seamless data integration and real-time analytics.

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4 students
Free
  • Last Updated: September 16, 2024
  • English , Persian
Basics of HiStudy Theme

About Course

Learn how to design and implement scalable data pipelines using modern cloud services like AWS, enabling seamless data integration and real-time analytics.

Instructor

Hoofar Pourzand
Senior Data Scientist
5.00
(2 Ratings)

With over 11 years of experience in data science, AI, and machine learning, Mr. Pourzand has been at the forefront of developing cutting-edge solutions for startups and established brands alike.

Free
30-Day Money-Back Guarantee
  • Update: September 16, 2024
  • Enrolled4
  • Lectures11
  • Skill LevelIntermediate
  • LanguageEnglish , Persian
  • Course Duration: 40h 30m

Your Instructors

Hoofar Pourzand

Senior Data Scientist

With over 11 years of experience in data science, AI, and machine learning, Mr. Pourzand has been at the forefront of developing cutting-edge solutions for startups and established brands alike.

  • 0 Courses
  • 2 Reviews
  • 0 Students

tryroadmap.com

  • 0 Courses
  • 2 Reviews
  • 0 Students

Mehran

English Teawvher

Histudy The standard chunk of Lorem Ipsum used since the 1500s is reproduced below for those interested.

  • 0 Courses
  • 18 Reviews
  • 0 Students

Requirements

  • Familiarity with fundamental statistical concepts like mean, median, and standard deviation.
  • Basic understanding of R programming language and RStudio.
  • Ability to work with data sets in formats such as CSV, Excel, or SQL databases.
  • Familiarity with machine learning concepts like regression, classification, and clustering.
  • Knowledge of data visualization techniques using ggplot2 or similar libraries.

Target Audience

  • Data scientists looking to expand their skills in predictive modeling.
  • Business analysts seeking to leverage data for forecasting trends.
  • Engineers and developers interested in integrating machine learning into their solutions.
  • Professionals aiming to use R for building and optimizing predictive models.
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