Description for Crash Course in Data Science
Fundamentals of Data Science and Big Data: The course encompasses fundamental terminology and concepts in data science and big data, elucidating their significance in thriving businesses.
Targeted for Non-Technical Managers: This course is tailored for individuals who will oversee data scientists, prioritizing practical knowledge rather than technical specifics.
Focused, Time-Efficient Learning: This course is designed for accelerated learning, providing a thorough overview in under one week while maintaining vital content.
Convenience-Oriented Design: The course is designed for convenience and efficiency, emphasizing essential ideas to keep learners motivated and informed.
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by Johns Hopkins University
Duration: 6 hours (approximately)
Schedule: Flexible
Pricing for Crash Course in Data Science
Use Cases for Crash Course in Data Science
FAQs for Crash Course in Data Science
Reviews for Crash Course in Data Science
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Crash Course in Data Science
Relationchips is an AI data agent that automates dashboards and actions, incorporates tools, and queries data in natural language, all without the need for SQL.
Chat2Report facilitates the conversational AI-driven analysis of over a decade of SEC financial reports for US-listed companies.
EquityResearch.ai offers AI-driven stock analysis and business insights, facilitating investment evaluation through impartial, data-centric reports.
NeoBase is an AI-powered assistant that facilitates natural language interaction, optimization, and administration across multiple databases with complete self-hosting capabilities.
From fundamental concepts to advanced methods such as deep learning and ensemble techniques, this program provides a comprehensive examination of machine learning techniques.
Acquire practical expertise in the integration of machine learning models into pipelines, optimizing performance, and efficiently managing versioning and artifacts.
In this course, students gain the skills necessary to use Python for data science, machine learning, and foundational applications of artificial intelligence.
Study the ethical consequences of AI development and implementation, emphasizing generative AI, AI governance, and pragmatic ethical decision-making in practical contexts.
Gain an extensive understanding of TinyML applications, fundamental principles, and the ethical development of artificial intelligence.
This training offers essential knowledge in the domains of data, computation, cryptography, and programming, with a focus on the Ruby on Rails framework.
Featured Tools
A practical guide to the use of generative AI for the purpose of composing, refining, and planning, utilizing structured and context-driven inputs.
A structured guide to the study of business opportunities in the chatbot space, as well as the comprehension, design, and deployment of chatbots using Watson Assistant.
An insightful introduction of the foundational models, generative AI concepts, and their applications on a variety of platforms.
The material equips data engineers to incorporate machine learning models into pipelines while adhering to best practices in collaboration, version control, and artifact management.
To enhance machine learning models, this course offers fundamental understanding of artificial intelligence, machine learning methods like classification, regression, and clustering.
Compare & Review AI Tool Directory AI Course Marketplace
