Data Science

Instructed by Eng.Sinan Kamal

Course Overview

The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge with a journey to Data Structure & Design. The second is a practical introduction to the tools and technologies that will be used in to deliver data science use-cases like Big Data, Data Visualization, Cloudification, version control, markdown, git, GitHub, Python, R, and RStudio. This Specialization covers the concepts and tools you’ll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, learners will have a portfolio demonstrating their mastery of the material.

Learning Outcomes

Build concrete understanding of Data Science and applied data use cases. Design and deliver Data science use cases. Hands-on interactions with Data science tools and techniques. Being able to industrialize data science applications in an enterprise context. Document, manage, research, and communicate business requirements from the initial stakeholder meeting to the final solution assessment and validation phase. Collect, manage, and interpret data to identify trends and issues in the workplace to create, and develop performance measures.
Welcome to Course 4: Data Science
Block 1:Principles of Data Science
Block 2:Data Structure & Design
Block 3:Data Science Programming
Block 4: Big Data
Block 5: Data Visualization
Block 6: Cloudification
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Eng.Sinan Kamal Data & AI Director at Orange