Data Science Courses

344 Courses

Coursera

Developing Machine Learning Solutions

In this machine learning course, you will learn about the machine learning lifecycle and how to use AWS services at every stage. Additionally, you will discover the diverse sources for machine learning models and learn techniques to evaluate their performance. You will also understand the importance of machine learning operations (MLOps) in str.

Coursera

Essentials of Prompt Engineering

Coursera

Fine-tuning Language Models for Business Tasks

Fine-tuning Language Models for Business Tasks This course demystifies the concept of "LLM fine-tuning" and its critical applications in the business world. In the context of rapidly evolving AI technologies, understanding how to fine-tune Large Language Models (LLMs) is essential for businesses to stay competitive. The course covers foundationa.

Coursera

GenAI for Data Scientists

GenAI for Data Scientists GenAI for Data Scientists is tailored for professionals eager to integrate Generative AI (GenAI) into their data science practices. This introductory course simplifies the complex realm of GenAI, illustrating its remarkable impact on data analysis, predictive modeling, and more. You will gain a thorough understanding o.

LinkedIn Learning

Tableau for Data Scientists

Tableau for Data Scientists | LinkedIn Learning Tableau for Data Scientists Take your Tableau skills to the next level. Learn how to format and filter messy data, use Tableau for data analysis, and visualize data with maps and dashboards. Provider: LinkedIn Learning Categories: Data Analysis Courses, Bu.

LinkedIn Learning

Tableau 10 for Data Scientists

Tableau 10 for Data Scientists | LinkedIn Learning Tableau was made for data science. Learn how to format and filter messy data, use Tableau for data analysis, and visualize data with maps and dashboards with the LinkedIn Learning course, Tableau 10 for Data Scientists. Provider: LinkedIn Learning Categories: Data Analysis Courses B.

LinkedIn Learning

Learning Data Science: Using Agile Methodology

Unlock valuable insights in data science every two weeks by mastering Agile methodology. In this course, you will learn to navigate the data science life cycle (DSLC) efficiently, employing fundamental tools like question boards to systematically break down your tasks. Offered by LinkedIn Learning, this comprehensive program spans diverse cate.

Coursera

Responsible AI for Developers: Interpretability & Transparency - 繁體中文

Responsible AI for Developers: Interpretability & Transparency - 繁體中文 | Coursera 本課程旨在說明 AI 的可解釋性和透明度概念、探討 AI 透明度對開發人員和工程師的重要性。課程中也會介紹實務方法和工具，有助於讓資料和 AI 模型透明且可解釋。 University: Provider: Coursera Categories: Artificial Intelligence Courses, Machine Learning Courses, Data S.

AWS Skill Builder

Introduction to Machine Learning: Art of the Possible (Indonesian)

Introduction to Machine Learning: Art of the Possible (Indonesian) Kursus digital ini dirancang untuk membantu para pengambil keputusan bisnis dalam memahami hal-hal mendasar dari machine learning (ML). Tingkat kursus: Dasar Durasi: 30 menit Catatan: Kursus ini memiliki transkrip/subtitle lokal. Narasi disampaikan dalam bahasa Inggris. Untuk.

AWS Skill Builder

AWS Foundations: Machine Learning Basics (Japanese) 日本語実写版

AWS Foundations: Machine Learning Basics (Japanese) 日本語実写版 Understand the basics of AWS cloud infrastructure through this comprehensive course in Japanese.

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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

Developing Machine Learning Solutions

Essentials of Prompt Engineering

Fine-tuning Language Models for Business Tasks

GenAI for Data Scientists

Tableau for Data Scientists

Tableau 10 for Data Scientists

Learning Data Science: Using Agile Methodology

Responsible AI for Developers: Interpretability & Transparency - 繁體中文

Introduction to Machine Learning: Art of the Possible (Indonesian)

AWS Foundations: Machine Learning Basics (Japanese) 日本語実写版

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?