Online Course bestseller
Introduction to Data and Data Science

Name: Introduction to Data and Data Science Course
Price: 36 USD

This data science fundamentals course is the perfect introduction to the field. Gain an overall understanding of data analysis, machine learning, and statistical techniques. Learn how data science helps businesses solve real-world problems

4.8

862 reviews on

Start for free

246,865 students already enrolled

Skill level:

Basic

Duration:

3 hours

Lessons (2 hours)

Practice exams (26 minutes)

CPE credits:

6

CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of professionals must earn to maintain their licenses and stay current with regulations and best practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's official website: www.nasbaregistry.org

Accredited

certificate

What you learn

Acquire a clear overview of modern data science fundamentals.
Explore how analysis and data science deliver business value.
Learn key terminology and core data science concepts.
Grasp the intuition behind business intelligence and machine learning methods.
Identify data science roles and career path opportunities.

Topics & tools

TheoryData AnalysisProgrammingCareer DevelopmentData Literacy

Your instructor

Martin Ganchev

Content Creator & Content Manager at 365 Data Science | Data Science Instructor

Track record

Bringing real-world expertise from leading global companies

Academic background

Master's degree, Economic and Social Sciences

Ask Martin a question

Iliya Valchanov

CEO of Team-GPT | AI Educator | Co-founder of 365 Data Science & 3veta

Track record

Bringing real-world expertise from leading global companies

Academic background

Bachelor's degree, International Economics, Management, and Finance

Ask Iliya a question

Course overview

Accredited certificates

Course OVERVIEW

Description

CPE Credits: 6 Field of Study: Information Technology
Delivery Method: QAS Self Study

Are you looking for a well-structured data science fundamentals course?

Do you want to gain a clear understanding of the data science field?

This is the perfect course for you!

If terms like ‘traditional data,’ ‘big data,’ ‘business intelligence,’ ‘business analytics,’ ‘data analytics,’ ‘data science,’ and ‘machine learning’ sound confusing, then taking this data science fundamentals course will help you understand the meaning and practical application of these concepts—making you proficient in the language of data.

These skills can boost anyone's career. We live in the age of AI, but few people realize that data science fundamentals are the foundation upon which AI is built.

This course is suitable for aspiring data analysts and data scientists. Still, it’s also highly recommended for business executives and professionals to enhance their understanding of data-driven decision-making.

Embark on a journey through the various domains of data science—exploring the connections among them for a comprehensive understanding of the field. You’ll soon comprehend the business and analytical motivations behind specific analyses and the necessity of data science.

Additionally, you'll discover the techniques and tools data analysts and data scientists employ to analyze data effectively.

If you're considering a career in data science, this course is invaluable because it clearly and thoroughly introduces various data science roles. Completing it will clarify who does what and help you identify the role that matches your career aspirations—setting a clear path to your goals.

What sets this data science fundamentals course apart from others? Several key differences exist:

Exceptional Content Quality
Our team has spent thousands of hours to create the best learning experience for you. We develop content full-time, invest significant time to digest complex topics quickly and design a pleasant learning journey.
Top Online Data Science Intro Course
This course has become one of the most popular online courses on data science fundamentals. For example, one of the course’s flagship resources (365 Data Science Infographic) has been shared over a thousand times on LinkedIn. Over a million students worldwide have chosen this course and rated it excellent.
Engaging Animations
Learning visually is one of the best ways to understand complex topics such as data science, machine learning, and deep learning. Internalizing practical concepts is much easier when you have seen high-quality visual examples. Our courses feature stunning animations—far beyond basic PowerPoint slides.
Innovative Interactive Exercises
Our platform's innovative, interactive exercises help you consolidate your learning effectively. Experience the best of hands-on learning in this data science fundamentals course.
Comprehensive Downloadable Material
Explore essential data science resources, including course notes, key term flashcards, infographics, diagrams, and practice exams—all available within the course for easy reference.

Prerequisites

No prior experience or knowledge is required. We will start from the basics and gradually build your understanding. Everything you need is included in the course.

Advanced preparation

None

Curriculum

25 lessons 57 exercises 4 exams

1. The Different Data Science Fields

42 min

For a novice, the data science field can be rather confusing. It takes a while to make sense of all the buzz words and different areas of data science. In this section, you will learn how to distinguish between business analytics, data analytics, business intelligence, machine learning, and artificial intelligence. We will discuss all of this with the help of a specially-designed infographic, and by the end of the section, you will know exactly where data science fits into today’s society.

42 min

For a novice, the data science field can be rather confusing. It takes a while to make sense of all the buzz words and different areas of data science. In this section, you will learn how to distinguish between business analytics, data analytics, business intelligence, machine learning, and artificial intelligence. We will discuss all of this with the help of a specially-designed infographic, and by the end of the section, you will know exactly where data science fits into today’s society.

Course Introduction Free

Why are there so many business and data science buzzwords? Free

Exercise Free

Analysis vs Analytics Free

Exercise Free

Intro to Business Analytics, Data Analytics, and Data Science Free

Exercise Free

Adding Business Intelligence (BI), Machine Learning (ML), and Artificial Intelligence (AI) to the picture Free

Exercise Free

Traditional AI vs. Generative AI Free

Exercise Free

More Examples of Generative AI Free

Exercise Free

An Overview of our Data Science Infographic Free

Exercise Free
2. The Relationship between Different Data Science Fields

7 min

In this section, you will learn how data science fields relate to each other and which ones leverage on traditional and big data, business intelligence, or traditional data science methods and machine learning.

7 min

In this section, you will learn how data science fields relate to each other and which ones leverage on traditional and big data, business intelligence, or traditional data science methods and machine learning.

When are Traditional data, Big Data, BI, Traditional Data Science and ML applied? Free

Exercise Free
3. What is the Purpose of each Data Science field

4 min

Here, you will learn not only which are the various data science disciplines, but also what each discipline is used for in practice. This is really valuable for you, as it will allow you to gain an idea of the practical application of the different methods you will learn later on in our program.

4 min

Here, you will learn not only which are the various data science disciplines, but also what each discipline is used for in practice. This is really valuable for you, as it will allow you to gain an idea of the practical application of the different methods you will learn later on in our program.

Why do we Need each of these Disciplines? Free

Practice exam Free

Exercise Free
4. Common Data Science Techniques

60 min

There are different ways to approach traditional data, big data, business intelligence, traditional data science methods, and machine learning. In this part of the course, we will introduce you to some of the most common techniques to do that, and we will provide several practical examples that will make things easier and more relatable.

60 min

There are different ways to approach traditional data, big data, business intelligence, traditional data science methods, and machine learning. In this part of the course, we will introduce you to some of the most common techniques to do that, and we will provide several practical examples that will make things easier and more relatable.

Traditional Data: Techniques

Exercise

Traditional Data: Real-life Examples

Big Data: Techniques

Big Data: Real-life Examples

Exercise

Business Intelligence (BI): Techniques

Exercise

Business Intelligence (BI): Real-life Examples

Traditional Methods: Techniques

Exercise

Traditional Methods: Real-life Examples

Exercise

Machine Learning (ML): Techniques

Exercise

Machine Learning (ML): Types of Machine Learning

Exercise

Machine Learning (ML): Evolution and Latest Trends

Exercise

Machine Learning (ML): Real-life Examples

Practice exam

Exercise
5. Common Data Science Tools

6 min

Before we dive in to studying the different types of tools used in data science, we will provide a quick overview for you, so you can have a good idea of why we are studying different tools and how they interact with each other. This will greatly facilitate your learning process, as you will already know what to expect and which tools will be necessary for a specific task.

6 min

Before we dive in to studying the different types of tools used in data science, we will provide a quick overview for you, so you can have a good idea of why we are studying different tools and how they interact with each other. This will greatly facilitate your learning process, as you will already know what to expect and which tools will be necessary for a specific task.

Programming Languages & Software Employed in Data Science - All the Tools You Need

Exercise
6. Data Science Job Positions: What do they Involve and What to Look out for?

3 min

In this section, we will discuss several job positions related to the fields of data and data science, including what responsibilities they’re comprised of and what to look out for when choosing your path.

3 min

In this section, we will discuss several job positions related to the fields of data and data science, including what responsibilities they’re comprised of and what to look out for when choosing your path.

Data Science Job Positions: What do they Involve and What to Look out for?

Exercise
7. Dispelling common Misconceptions

4 min

We will conclude our Intro to Data and Data Science training with a lesson that dispels the most common misconceptions about the field of data science.

4 min

We will conclude our Intro to Data and Data Science training with a lesson that dispels the most common misconceptions about the field of data science.

Dispelling Common Misconceptions

Practice exam

Exercise
8. Course exam

30 min

30 min

Course exam

Free lessons

1.1 Course Introduction

3 min

1.2 Why are there so many business and data science buzzwords?

5 min

1.4 Analysis vs Analytics

4 min

1.6 Intro to Business Analytics, Data Analytics, and Data Science

7 min

1.8 Adding Business Intelligence (BI), Machine Learning (ML), and Artificial Intelligence (AI) to the picture

9 min

1.14 An Overview of our Data Science Infographic

4 min

Start for free

I come from a design background, but I wanted to combine creativity with data. 365 Data Science helped me do that. I started with data literacy, then moved into AI and the data analysis career track. The platform kept me motivated with hands-on projects and a learning experience that just made sense. Today, I work as a quality analyst in an AI company, reviewing the outputs of generative models and unsupervised learning. 365 enabled me to see the bigger picture, even beyond my direct role.

Melina A.

Before 365: Information Design | UAM

After 365: Data Analyst | TELUS Digital

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96%

of our students recommend

365 Data Science.

I wanted to move from IT support into solving business problems with data, but I needed structured learning and real-world practice. 365 Data Science gave me exactly that—an end-to-end path from Python to machine learning, with hands-on projects that built my confidence. Today, I analyze large datasets, build predictive models, and deliver data-driven insights as a Data Scientist. For IT professionals aiming to transition into data roles, 365 is a career-changing resource.

Basilio C.

Before 365: IT Assistant | UNDP Guinea-Bissau

After 365: Data Scientist | Orange Bissau

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94%

of AI and data science graduates

successfully change

or advance their careers.

With a background in economics and over five years of experience in project management across private and international organizations—including a European Union project in Sudan—I sought new opportunities after relocating to the UAE. Transitioning into data analytics, I began my journey with the 365 Data Science platform, which provided an outstanding and well-structured learning experience. The platform's practical approach and high-quality courses helped me progress from a beginner to a proficient data analyst.