Data Science Courses

413 Courses

AWS Skill Builder

Foundations of Prompt Engineering (Japanese) (Sub) 日本語字幕版

Foundations of Prompt Engineering (Japanese) (Sub) 日本語字幕版このコースでは、効果的なプロンプトを設計するための原則、テクニック、ベストプラクティスについて学習します。このコースでは、プロンプトエンジニアリングの基礎を紹介し、高度なプロンプトテクニックへと進みます。また、基盤モデル (FM) を操作する際に、プロンプトの誤用を防ぎ、バイアスを軽減する.

Pluralsight

Using Neural Networks for Image and Voice Data Analysis

Using Neural Networks for Image and Voice Data Analysis Neural networks can be configured in various ways depending on the type of data and objectives. This course will help you understand how to properly choose a neural network architecture for image or audio data. Deep learning, as opposed to machine learning, allows a more robust way to dea.

Pluralsight

Gemini for Data Scientists and Analysts

Gemini for Data Scientists and Analysts Course Title: Gemini for Data Scientists and Analysts Course Description: In this comprehensive course, you will discover how to leverage Gemini, a generative AI-powered collaborator from Google Cloud, to analyze customer data and predict product sales. Learn the techniques to identify, categorize, and dev.

Pluralsight

Building Data Science Skills with Generative AI

Building Data Science Skills with Generative AI Upgrade your data science skills with Generative AI tools. This course will teach you to integrate Generative AI into your workflow, enhancing efficiency and analytical precision. Data science is evolving rapidly, and staying ahead means embracing the latest AI advancements. In this.

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.

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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

Foundations of Prompt Engineering (Japanese) (Sub) 日本語字幕版

Using Neural Networks for Image and Voice Data Analysis

Gemini for Data Scientists and Analysts

Building Data Science Skills with Generative AI

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

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?