Introduction to Data and Data Science

with Martin Ganchev and Iliya Valchanov

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

3 hours 22 lessons
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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


Student feedback


11434 ratings
5 stars
9707 (85%)
4 stars
1457 (13%)
3 stars
227 (2%)
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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.
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.
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 !!!
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.
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.
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Martin Ganchev

“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

Introduction to Data and Data Science

with Martin Ganchev and Iliya Valchanov

Start Course