Introduction to Data and Data Science

with Martin Ganchev and Iliya Valchanov

4.8/5

(10294)

Introducing you to the field of data science and building your understanding of the key data science terms and processes.

3 hours 22 lessons

Start course

22 High Quality Lessons

24 Practical Tasks

3 Hours of Content

Certificate of Achievement

Course Overview

Working with data is an essential part of maintaining a healthy business. This course will introduce you to the field of data science, help you understand the various processes and distinguish between terms such as: ‘traditional data,’ ‘big data,’ ‘business intelligence,’ ‘business analytics,’ ‘data analytics,’ ‘data science,’ and ‘machine learning.’

Topics covered

Career developmentdata analysisProgrammingTheory

What You'll Learn

You are interested in starting a career in data science? Begin with the very basics by receiving the proper introduction to the world of data with this course!

Distinguish between various data science related fields

Understand the terms traditional and big data

Integrate common data science techniques

Use specific data science tools

What are the most common data science programming languages

Become familiar with data science job positions and alternatives

Curriculum

The Different Data Science Fields Free

6 Lessons 34 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

Analysis vs Analytics Free

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

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

An Overview of our Data Science Infographic Free
The Relationship between Different Data Science Fields Free

1 Lesson 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
What is the Purpose of each Data Science field Free

1 Lesson 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
Common Data Science Techniques

11 Lessons 54 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

Traditional Data: Real-life Examples

Big Data: Techniques

Big Data: Real-life Examples

Business Intelligence (BI): Techniques

Business Intelligence (BI): Real-life Examples

Traditional Methods: Techniques

Traditional Methods: Real-life Examples

Machine Learning (ML): Techniques

Machine Learning (ML): Types of Machine Learning

Machine Learning (ML): Real-life Examples
Common Data Science Tools

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

1 Lesson 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?
Dispelling common Misconceptions

1 Lesson 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

Student feedback

4.8/5

10294 ratings

5 stars

8706 (85%)

4 stars

1342 (13%)

3 stars

207 (2%)

2 stars

25 (0%)

1 star

14 (0%)

mahadi alam

11.09.2023

I'd be happy to provide a rating for a data science course, but I would need specific information about the course you're referring to. Data science courses can vary widely in content, quality, and difficulty, so it's essential to consider the specific course in question. Here are some factors to consider when rating a data science course: Course Content: Does the course cover a comprehensive range of data science topics, including data cleaning, analysis, visualization, and machine learning? Is the content up-to-date and relevant to current industry trends? Instructor: Was the instructor knowledgeable and effective in delivering the course material? Did they provide clear explanations and examples? Learning Resources: Were there ample learning resources such as lecture notes, textbooks, online tutorials, or practical exercises? Assignments and Projects: Did the course include assignments, projects, or real-world applications that allowed you to apply what you learned? Difficulty Level: Was the course appropriately challenging for your skill level? Did it provide a good balance between theory and practical applications? Support and Community: Did the course offer support through forums, discussion boards, or office hours? Was there an active community of learners to collaborate with? Certification: Did the course offer a recognized certification upon completion, and is it valuable in your field or industry? Cost and Accessibility: Was the course affordable and accessible to you? Did it offer financial aid or scholarships if needed? Personal Learning Experience: How well did the course meet your personal learning goals and expectations? Without specific information about the course you're referring to, it's challenging to provide a rating. If you can provide more details about the course or its specific aspects, I'd be happy to offer a more informed assessment.

Manuel Antonio Hernandez

07.10.2023

The course is well written, with no errors, and the visuals are accurate, but these are just the standard of professional work - it is noticeable that the videos are made with AI, and that the voice is also AI generated. I understand that this is to save time, however it does make the journey rather emotionally disconnected and there is no instructor and no real journey. These should be integrated into your program, because every single person has a heros journey, a process of development they are on, a story, if you will. And if you are part of that story, you will build customers who feel emotionally attached to your education and your platform, and will be loyal to you. Also, adding some more interesting visualizations (the infographic was excellent, and so we're the robots, actually really nice, but they were rare) - and perhaps a minigame of some kind to play with the different parts. One idea 💡 was a data analyst builder, where you can mix and match different pieces and see what's made. For example, I want to learn SQL, Python, and I want remote work and storytelling, tableau - I'm essentially building a BI Developer / Consultant. This would give a much more in depth understanding of not only the skills in this field, but where they fit and show up in the infographic, and it would also clarify the links between skills and jobs, really bringing this entire section home.

rodney akuffo

22.03.2023

I liked the videos. They kept me engaged throughout the short course, providing bite-sized notes aided by images of the application of the different technical concepts. The explanation is very good, simple choice of words, with clarification of technical terms, covering differences and similarities between the various concepts. As accustomed as I am to hard paper studying, I tend to do a lot of transcription and paraphrases to present differences and similarities between concepts myself. In these Videos, they highlight the keywords and link them for you. On the other hand, Some flaws I identified are in the degree of the explanation. They were conveyed quite swiftly, and a bit shallowly I would say, yet as it is mentioned in the title (Introduction), you shouldn't expect in-depth coverage of the concept, but just a presentation of the key ideas and terminology you will encounter in the full career path. Hence, For the points that I have stated, I think is already a good incipit, thus I wouldn't put it down as much, and although it may seem tedious from an outside view, I can assure you it's worth all of its time. I recommend it !!!

Daniel

12.07.2022

Much of the information seemed overwhelming and not important at the same time. Personally, I felt the semantics of the definitions (data scientist vs data analyst) to be very boring and not particularly useful, and therefore could not remember the difference between the terms. The structure I like. The videos and explanations were clear. I enjoyed the course more when it went more into the graphs and process of machine learning. The questions just had to do mainly with memorization. The brief videos were not enough to engrain the terms and subtle differences into my memory. I failed almost all the quizzes the first time and had to retake them to get a passing grade. Maybe an aniki flashcard setup to prep for the quizzes would be a solution to get the buzzwords to stick in memory. I think I'll like the rest of the course better when the topics are more interesting and get more into specifics.

Element Zeroth (ElementZeroth)

02.11.2022

I really like the course, but maybe it is just the choppy audio (the voice is fine just the sudden start and start) or maybe it is just me and I am not in learning mood as (I am kinda tired) I am a little bored. Though, this is the introductory course so it isn’t to the interactive part of it yet, so that might be a thing which is fine. Without a foundation, how can you build a multistory house? I say 4 stars for several reasons. Not because of the course, though. Here is one example: Some people will not look at 5 stars and because of possible bias (and kinda the same with 1 stars) they will look at 2-4 star reviews first. (I am not saying this is a majority, just some). The course’s info-graph itself is going to be extremely helpful for remembering the terms, that doesn’t include the breakdown of the info graph and other information in the course.

“The first step is the most important. Intro to Data and Data Science focuses on the field of data and data science as a whole. I will guide you through the terms, disciplines, and tools involved, and discuss the possible career paths in front of you.”

Martin Ganchev

Worked at the European Commission