Data Science Courses

344 Courses

LinkedIn Learning

Data Science Foundations: Knowledge Graphs

Data Science Foundations: Knowledge Graphs Enhance your information management skills with our comprehensive course on knowledge graphs. Available on LinkedIn Learning, this course delves into various knowledge graph tools, techniques for networking databases, and methods to construct a robust digital knowledge base. University: Provider: Lin.

AWS Skill Builder

Foundations of Prompt Engineering (Korean)

Foundations of Prompt Engineering (Korean) 이 과정에서는 효과적인 프롬프트를 설계하기 위한 원칙, 기법 및 모범 사례에 대해 알아봅니다. 이 과정에서는 프롬프트 엔지니어링의 기본 사항을 소개하고 고급 프롬프트 기법을 계속 살펴봅니다. 프롬프트 사용 오류를 방지하는 방법과 FM과의 상호 작용 시 편향을 완화하는 방법을 알아봅니다. 과정 수준: 중급 소.

AWS Skill Builder

Foundations of Prompt Engineering (Traditional Chinese)

Prompt工程基礎（繁體中文）在本課程中，您將學到設計有效提示的原則、技巧和最佳實務。本課程將介紹提示工程設計的基礎知識，並隨著課程進展介紹進階提示技巧。您也會學習如何防範提示濫用，以及如何減輕與 FM 互動時的偏見。課程等級：中級持續時間：4 小時注意：本課程具有本地化的註釋/字幕。旁白保留英語。要顯示字幕，請按一下播放器右下角的 CC 按鈕.

AWS Skill Builder

Foundations of Prompt Engineering (Simplified Chinese)

Prompt Engineering Foundations (Simplified Chinese) 在本课程中，您将学习设计有效提示的原则、技术和最佳实践。本课程将介绍提示工程的基本知识，然后逐步过渡到高级提示技术。您还将学习如何防止提示误用，以及如何在与基础模型 (FM, Foundation Model) 互动时减少偏差。课程级别：中级时长：4 小时注意：本课程具有本地化的注释/字幕。旁白保留英语。要.

AWS Skill Builder

AWS SimuLearn: Set Up an ML Environment

AWS SimuLearn is an online learning experience that pairs generative AI-powered simulations with hands-on practice to help individuals learn how to translate business problems into technical solutions through the simulation of dialog between a customer and a technology professional. AWS SimuLearn: Set Up an ML Environment In this AWS SimuLearn assi.

Complete Machine Learning and Data Science: Zero to Mastery

Complete Machine Learning and Data Science: Zero to Mastery Complete Machine Learning and Data Science: Zero to Mastery Learn Machine Learning (Artificial Intelligence), Python, Data Science, Data Analysis, Tensorflow, Pandas & more. All using the latest in AI! Categories: Artificial Intelligence Courses, Python Courses.

Pluralsight

Assessing Your Company’s AI Talent Needs

Assessing Your Company’s AI Talent Needs Assembling the right AI team is crucial for organizational success. This course will teach you how to assess your company’s AI talent needs, identify key roles, develop a hiring plan, and foster a culture of continuous learning and innovation. In today's rapidly evolving.

Coursera

Data Science for Professionals

Data Science for Professionals Data science has become indispensable across industries, driving informed decision-making and uncovering valuable insights from data. This course is designed for professionals seeking to leverage data science techniques and tools to extract meaningful information from data and drive organizational success. From dat.

LinkedIn Learning

Develop with Python for AI and Machine Learning

AWS Skill Builder

Exam Prep Official Pretest: AWS Certified Machine Learning Engineer - Associate (MLA-C01 - English)

Exam Prep Official Pretest: AWS Certified Machine Learning Engineer - Associate (MLA-C01 - English) The Exam Prep Official Pretest: AWS Certified Machine Learning Engineer - Associate (MLA-C01 - English) includes 65 questions and has a time limit of 130 minutes. This pretest aligns with the MLA-C01 version of the exam and exam guide. About AWS Ce.

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Learning Paths for Career Advancement

Data Scientist works with company data, analyses it, looks for potential dependencies, draws conclusions on this basis and, if necessary, builds visualisations. To solve such tasks, the specialist uses mathematical algorithms, development tools and special programmes. A Data Scientist understands how to build a forecast and helps to make the right decisions.

This specialist uses Data Science methods to process large amounts of information. He builds and tests models of data behaviour. This is how he finds patterns in them and predicts future values. For example, knowing everything about the demand for a product earlier, Data Scientist helps the company to make a forecast about sales in the near future. All models are built thanks to machine learning algorithms.

Usually Data Scientists become for the following reasons:

There is a desire to learn a promising and highly paid profession.

There is experience in a related industry, but you want to move to a new, more demanded direction. Data Scientists are often programmers, marketers, financiers and business analysts.

In professional activities or scientific research, it is necessary to apply innovative technologies: big data, neural networks, artificial intelligence.

To work in Data Science you need find best data science and ai courses for improving programming skills and knowledge of maths beyond the school curriculum.

Course Benefits and Features

Why choose online artificial intelligence and data science courses?

Classes are taught by experienced programmers and analysts who explain complex material, such as probability theory or mathematical analysis in an easy-to-understand way with examples.

Recordings of all lessons are stored in your personal account. You will not have gaps in your knowledge, because webinars can be watched repeatedly at any time. The videos will stay with you forever.

After each topic there is a practical task. The tasks gradually become more complex, and by the end of the course you will build a neural network or recommendation system. That is, you will have projects ready for your portfolio.

If you cannot cope with a question on your own, the tutor will help you. He will point out mistakes and give recommendations.

Many schools help with finding a job for free, and also invite students for internships, so you will have the opportunity to gain experience in a real project.

Excellent knowledge of maths, statistics, programming languages, English, as well as creativity, communication and critical thinking: employers are willing to pay more for specialists with this set of skills.

How to Enroll in Data Science and AI Courses?

Let's tell you in detail what steps you need to go through to become a Data Science specialist:

Study mathematics and linear algebra. If you have knowledge within the school curriculum, you can start with books that explain the basic concepts in simple language: derivative, differential, matrix and so on.

Any analytics uses mathematical statistics and probability theory - these are the next big topics you should familiarise yourself with.

Working in Data Science is impossible without knowledge of programming languages. For a beginner, Python is suitable - it is relatively simple, flexible and feature-rich.

The next step is to learn machine learning algorithms: "with a teacher", "without a teacher", "with reinforcement". You need to learn how to collect data for analysis and visualise it.

Having sorted out the theory, move on to practice. For example, you can look for an assistant position or an internship in large IT companies!

Data Science Courses

Data Science Foundations: Knowledge Graphs

Foundations of Prompt Engineering (Korean)

Foundations of Prompt Engineering (Traditional Chinese)

Foundations of Prompt Engineering (Simplified Chinese)

AWS SimuLearn: Set Up an ML Environment

Complete Machine Learning and Data Science: Zero to Mastery

Assessing Your Company’s AI Talent Needs

Data Science for Professionals

Develop with Python for AI and Machine Learning

Exam Prep Official Pretest: AWS Certified Machine Learning Engineer - Associate (MLA-C01 - English)

How to train in Data Science and become one from scratch?

Learning Paths for Career Advancement

Course Benefits and Features

How to Enroll in Data Science and AI Courses?