Data Science Courses

897 Courses

YouTube

CIQA: A Coding Inspired Question Answering Model - Session W1.4

Discover innovative approaches to question answering through a coding-inspired model that enhances natural language processing and information retrieval capabilities.

On-Demand

YouTube

Langchain: Integration of ElasticSearch Vector Store and RAG with Gemini

Explore integrating Langchain with ElasticSearch for RAG applications, using ElasticSearch as a vector store and retrieval component with Gemini.

On-Demand

YouTube

Unleashing the Power of Data, Analytics and AI Technology to Improve Cardiovascular Care

Discover how data analytics and AI technologies revolutionize cardiovascular healthcare delivery, enhancing patient outcomes and transforming treatment approaches in modern cardiology.

On-Demand

YouTube

Design and Build Powerful LLM Agents - Valentina Alto

Unlock the potential of LLM agents: explore their design, construction, and evolution from single to multi-agent systems. Gain insights into GenAI workflows and key components of AI agents.

On-Demand

Duke University

Data Science for Agile Decision-Making

Unlock data science techniques for agile business decisions. Learn to collect, analyze, and interpret data, driving insights and value. Ideal for leaders seeking to leverage data effectively.

On-Demand

Pluralsight

Everything You Need to Know about the AWS Certified Machine Learning Engineer - Associate Exam

Uncover key insights and strategies for acing the AWS Certified Machine Learning Engineer - Associate exam with expert guidance from David Blocher.

On-Demand

YouTube

What's Next for Data-Driven Medicine and AI-Powered Innovation - Insights from insitro CEO

Explore AI's impact on drug discovery and targeted treatments with insitro CEO Daphne Koller. Gain insights into data challenges, company culture, and the future of AI-powered innovation in medicine.

On-Demand

YouTube

Re-Thinking Re-Ranking - Improving Efficiency and Effectiveness in Search Systems

Explore innovative re-ranking techniques that improve search efficiency and effectiveness. Learn about alternatives to cascading approaches and their impact on vector search and heavy-weight re-rankers.

On-Demand

YouTube

Smart Retailing: Optimization Techniques for Strategic Success

Dive into data-driven retail optimization techniques, leveraging analytics and machine learning to enhance decision-making, streamline processes, and boost profitability in the retail sector.

On-Demand

YouTube

How Well Does Diffusion Model Generate? Exploring Generalization in Emerging Settings

Explore the capabilities and limitations of diffusion models in generative tasks, focusing on emerging generalization settings and their implications.

On-Demand

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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

CIQA: A Coding Inspired Question Answering Model - Session W1.4

Langchain: Integration of ElasticSearch Vector Store and RAG with Gemini

Unleashing the Power of Data, Analytics and AI Technology to Improve Cardiovascular Care

Design and Build Powerful LLM Agents - Valentina Alto

Data Science for Agile Decision-Making

Everything You Need to Know about the AWS Certified Machine Learning Engineer - Associate Exam

What's Next for Data-Driven Medicine and AI-Powered Innovation - Insights from insitro CEO

Re-Thinking Re-Ranking - Improving Efficiency and Effectiveness in Search Systems

Smart Retailing: Optimization Techniques for Strategic Success

How Well Does Diffusion Model Generate? Exploring Generalization in Emerging Settings

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?