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.

2 hours 22 lessons
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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.’

22 High Quality Lessons
17 Practical Tasks
2 Hours of Video
Certificate of Achievement

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


6678 ratings
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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.
In some questions the context was not immediately obvious to me - are talking about this info-graphic or the previous one? why is sales forecasting not ML? etc.. I have a feeling, that I would need more quiz questions to really solidify the new knowledge - for example I can repeat the examples about qualitative and quantitative analysis from part one, but I'm not really sure I got the principle right. It's really good that the course points out several times, that in many places these terms are being used differently - they really were in my previous company and it pops out in random places... I'm happy to be show other perspective.
As far as being a basic overview of the terms that are going to be used later on It's pretty decent. It's just an introduction class and does a fine job of introducing the topic. My personal opinion is those people in the comments above who are complaining about it being confusing and Whatnot aren't taking the time to effectively study or take notes, and they're expecting to just absorb everything like a sponge. You do have to interact with it somewhat. I guess what I'm saying is you're going to get what you put in.
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“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