This video is for people unfamiliar with basic statistics because it focuses on introductory estimators of central tendency, types of data, and data visualization.
⭐️ Contents ⭐️ ⌨️ (0:00:00) Introduction ⌨️ (0:10:52) Statistical Data Types ⌨️ (0:25:10) Types of Averages ⌨️ (0:38:55) Spread of Data ⌨️ (0:50:54) Quantiles and Percentiles ⌨️ (0:55:52) Importance of Data Visualization ⌨️ (1:05:14) One Variable Graphs ⌨️ (1:12:04) Two Variable Graphs ⌨️ (1:25:08) Three and Higher Variable Graphs ⌨️ (1:31:20) Programming
Awesome breakdown on this topic Max! I have my roots in physics as well and went into it-consulting a few years ago. I have been looking for good introductions on this topic. For me it was so much easier to follow the explanations coming from a physicist. Thanks for that!
I wish there was a scholarship offer (or some deferred payment option) for those of us in the developing world who can't afford to pay for this training but are sooo interested in learning Data Science.
There are many free resources out there. R and Python are free and open sourced. All it takes is a computer and some dedication. When you can, buy a good book or two. Listen to success stories on the freecodecamp's podcast to probably get some ideas on how you can make your own success story.
Coffee production can indeed have a notable impact on climate change, primarily through the following factors: Firstly, coffee cultivation often requires deforestation, especially in regions like the Amazon rainforest. The removal of trees, which act as carbon sinks, results in the release of stored carbon dioxide (CO2) into the atmosphere. This contributes to higher atmospheric CO2 levels, a major driver of global warming. Secondly, coffee farming relies heavily on water resources, and inefficient water use can lead to ecosystem degradation. In many coffee-producing regions, unsustainable farming practices, such as excessive irrigation and chemical fertilizers, can contaminate water supplies and harm aquatic ecosystems. The disruption of these ecosystems can further exacerbate climate change by releasing methane (a potent greenhouse gas) and reducing the planet's natural ability to regulate its climate. Finally, coffee is often transported over long distances, consuming substantial energy and emitting greenhouse gases during transportation. This adds to the carbon footprint associated with coffee consumption, as emissions from shipping and logistics contribute to global warming. In summary, while the link between an individual's cup of coffee and immediate climate change may seem distant, the cumulative impact of widespread coffee production practices, including deforestation, water use, and transportation, can play a role in contributing to climate change when viewed at a global scale. So it may affect it will rain or not depending on no. of cups if coffee we drink .... Just kidding!! :)
Awesome video, thanks for putting in the effort to make this! It's funny, I was totally uninterested in anything to do with math or statistics while I was in school, but now I think this stuff is really cool! Being able to use data to learn from the past and guide your actions in the future...what's not to like?
@@markbaladad6710 is from a game called Deus Ex I believe, a youtuber called MaximilianMus has it and he told his followers to put this image in our profiles to clarify that we are his fans, so it is like a meme now hahaha
Quantitative Research Methodologies Q&A 1. Evaluate the scope of quantitative research methodology comparing each method and critique each technique, model, metaphor, and paradigm. 200 words Ethnographic studies are commonly used in research methods. The model of ethnographic studies is based on the researcher following participants or subjects into their culture to gain more insight into cultural issues. A metaphor of an ethnographic study is following customers to their home to understand why they purchase a given product and not another. The three paradigms of ethnographic studies include behaviorist, semiotic, and holistic styles. Interviews and surveys are the specific techniques embraced in ethnographic studies (Park, & Park, 2016). The narrative method includes the use of two main techniques; interviews or collecting information from person’s documents that include diaries, memoirs, and other personal encounters narrative documents. The narrative is selected carefully to be a metaphor of the whole population, for instance, someone narrating how a calamity strikes their community might be narrative that can tell what the whole community experienced. The models of narratives can be spoken, written, or visually represented. The paradigm is that what one person experienced does not differ significantly from what the rest of the community experienced concerning the specific problem being analyzed (Park, & Park, 2016). The third methodology is the phenomenological study which embraces multiple techniques that include interviews, surveys, literature review, and others to describe phenomena. The main models embraced in the phenomenological study include purposive sampling and systematic sampling. The metaphor of this method is that the collection of data from varying sources will give a united theme and data relevant to understanding the phenomena. The paradigm, hereby, is that the collected data about a certain phenomenon will show common features about the phenomena (Park, & Park, 2016). The fourth method is grounded theory. It is closely associated with the phenomenological study in that it embraces the use of varying sources of information to develop a theme and collect data about phenomena. Its techniques, models, metaphor, and paradigms are similar to those of the phenomenological study (described in the paragraph above), but unlike phenomenological studies that look into the essence of an event or activity, the grounded theory seeks to give theories or explanations behind an occurrence or event (Park, & Park, 2016). The fifth quantitative research methodology is the case study method. This method looks into occurrences as they affected a subject or few subjects. The techniques in this method include a single subject case study or multiple subjects’ case studies. The models of inquiry may include interviews, observations, or literature review for past events case studies. This model has a paradigm that what happens to one person applies to other people with a similar problem in society (Park, & Park, 2016). 2. Select the best quantitative method and assess the strengths and weaknesses of that selected method defending why the selected quantitative method is the best. 80 words The best qualitative method in my opinion is an ethnographic study. Given my interest in social science, I find that understanding the behavior of a given group through interacting with the group is the best model to use. Moreover, unlike case study design this method allows for interaction with a larger number of participants or subjects with similar concerns. Moreover, it is a method that develops a hypothesis that can be approved or disapproved by the research, unlike the phenomenological and grounded theory designs that look for common themes during the active research. One of the weaknesses of ethnographic studies is that it can consume a huge amount of time. The researcher can also be faced with great challenges fitting in with a new culture and gaining their trust after s/he declares interest to understand their cultural elements. 3. Compare various quantitative methods and how each method enables researchers to design the correct series of questions and eventually hypotheses to prove the theories. 120 words The ethnographic study follows researchers into their cultural roots to understand their behavior. It is appropriate for cultural studies that aim to understand behavior such as consumer behavior. The narrative aims at gaining opinions from specific subjects, or their encounters concerning a certain problem. It closely relates to the case study design which also focuses on individual’s stories. These two methods can yield results in understanding people’s reactions to certain societal problems, for instance, the experiences that parents undergo after losing a job. The phonological studies and grounded theory methodologies are similar in many aspects including the fact that they seek a common theme from multiple sources of information such as interviews, literature, surveys, and other sources. However, the grounded theory seeks to explain or develop a theory describing a certain societal concern, while the phenomenological study looks into the essence of the societal concern of interest. The ethnographic study, case study, and narrative begin with preparations that include the identification of the societal problem, development of research questions, and a hypothesis that the study will either approve or disapprove. The phenomenological study and the grounded theory designs, on the other hand, have a planning process that only includes the identification of the problem and sources from which information will be obtained. These two quantitative methodologies develop a common theme in the field of study, which gives research questions to be responded to and maybe a hypothesis which is not mandatorily essential. 4. Recommend best methods to solve different types of analysis and provide a table that outlines each method, what type variables are used, best applications, and what findings to expect. 100 words Descriptive analysis Predictive analysis Diagnostic analysis Prescriptive Analysis Recommended Method Case study, cross-sectional research, surveys, naturalistic observation, archival research, and longitudinal research Analytics techniques such as regression models, time series, and others. Case studies, questionnaires, interviews, surveys, and observations Mathematical learning and machine learning methods Types of Variables Descriptive variables such as “agree or disagree” Predictor variables; variables linked with certain outcomes such as regression variables Descriptive variables such as a scale of 0-10 in rating pain Both predictor and descriptive variables Best Application Understanding phenomena Extrapolation of past and current events into uncertain or future predictions Understanding personal outcomes such as feelings, opinions, and others Big data analytics essential in making an informed recommendation Expected Findings More understanding of the phenomena that has already occurred or is occurring Forecasts of what is to happen An understanding of personal outcomes Recommendations for courses of action
Hi Max, This is great stuff for any beginner in data science. Do you have any videos/tips on probability and logistic regression that are easy to understand as well? thanks!
Hello Max, it's a beautiful thing you are dong. I just want to ask, what are the most recommended websites, books or podcast to learn Data Science because sometimes surfing through the web to get an efficient and effective training can be a bit overwhelming. BTW I really love Data Science and I hope one day I will be training others as you are. Thanks. Love from Nigeria.
Install google podcast on your Android phone and search for Data Science, machine learning and AI channels and thank me later. Both podcast host and guests always discuss where to get materials and lessons. I am based in Nigeria but I have learned so much on the platform
دى البرامج وممكن حضرتك تضيف Ai Machine learning robotics engineering Big dataبيانات الضخمة هندسة برمجيات Analysis with sql البلوك تشين Introduction to data science
So as someone who eventually one day wants to work in data science industry, is there an entry level job role that one can apply as a starting point and just get promoted/ level up to a data science in time rather than just trying to apply for a data science position from the get go? I dont mind starting at the bottom and working my way up the ladder.
With the box and whisper plots, I wasn't clear on what the different things (boxes, vertical and horizontal lines, and symbols on the lines) represented. I liked the video a lot in general, though.
From the course, it seemed that you are more like from Data Visualization rather than Data Scientist. You didn't talk about Probability distribution or Hypothesis Testing which are basics things for basic Algorithm like Linear Regression, and the second thing box plot is used to plot single variable to check how the values are spread out. We can plot single variable like Salary in box and Whisker plot. Data Scientist is much more than just visualization.
Hi Naznin - thanks for your comment and thanks for watching! As I mentioned in the video, this is a super intro and brief look into the 3 main components of data science - the pillars, if you will, not the exact strategies or tools that I personally use in my job as a Data Scientist. 🙂 And you're absolutely right - data science is way more than just visualizations, but without the proper foundations - you can't get to the more complex, advanced techniques. Before you can apply any machine learning algorithms, you have to first understand how to approach an exploratory analysis as well as how to do your own visualizations. Imagine using a random forest algorithm without being able to correctly understand or visualize the decision boundaries or being able to perform correct exploratory analysis for feature selection and optimization. You wouldn't be able to correctly interpret your results, let alone be confident in them. Hope that makes sense! Let me know if you have any other questions. 🙂
Computer Science and Engineering 2nd Semester, Intro to Data Science, 5 weeks of lectures in one single video. Either you teach good or our professor teaches slow.
Hi Max, Very informative and interesting, Question, at 13:16 you talk about Continuous data and contrast every number from -infinity to +infinity (i.e. numerical infinity) with every number between "0" and "1", however, can those numbers be equally infinite as subdivisions can be infinite?
I don't follow quartiles: there are four quartiles, but only three listed (at (52:12). I didn't follow the explanation for that. Maybe display it by listing all four?
Hello, I want to learn more about MI and I don´t know where to start. I just watched some math videos but it became boring over time just to do the math (I am not bad at math and atm I am studying IT-Security so I am familiar with "University Math"). This Video was great but it covers just a small piece of what I want to learn. Maybe someone have far-reaching videos that one can recommend? Thank you ;D
first off you need to learn programming like Python Or R and then Sql and Machine learning with python...! get through these hurdles then you'll know what to do next ! i know this answer is 11 months late but i hope it'll help you! Adios
Before I go into this course, If all programming languages do not exist.. is data science still exists? In another way .. is pure data science independent of programming?
Very general data science intro. But it's good cuz it's not too intimidating. More examples would have been better. More guidance in terms of other free resources would have also been good. But this video is good for beginners to understand statistics behind it.
Very helpful video - we can also recommend to study Applied Data Science at MU Vienna - small class sizes and great professors! Check out our curriculum.
Why should a weight of an adult be a discrete numerical value ? I mean If I put a cheeseburger in my mouth in ones, I’m jumping from 70kg instantly to 70,3kg or am I wrong ?
What is the actual difference between this an statistics? Like truly, aside from "oh we use scientific "methods"", what is the legitimate difference aside from type of program used and end-point application of statistical practice? Algorithms, technology, etc.? That is just statistics "but with extra steps" lol.
This is a beginner's course. You just have to have basic familiarity with a programming language. You don't need any advanced math to understand this course.
Of course they both exist. Numbers can infinitely increase in the positive direction as well as the negative direction. And that's only on a one dimensional scale (i.e., a number line). Think about coordinate points infinitely going in one direction on a 2D or 3D graph.
Machine/Deep Learning is essentially trying to figure out a math equation to describe your data. In terms of application it's either statistical techniques and/or optimisation techniques to reduce errors (through calculus). Data Science is a broader term that can encapsulate machine/deep learning, but it doesn't rely on that. Data Science is all about analyzing data, making sense out of it, and taking valuable and (hopefully) actionable insights from it. So machine/deep learning is just one approach to analyzing data, and it is a part of Data Science, but there are many other approaches you can take to investigate your data and get value from it that don't rely on machine/deep learning.
This video is for people unfamiliar with basic statistics because it focuses on introductory estimators of central tendency, types of data, and data visualization.
⭐️ Contents ⭐️
⌨️ (0:00:00) Introduction
⌨️ (0:10:52) Statistical Data Types
⌨️ (0:25:10) Types of Averages
⌨️ (0:38:55) Spread of Data
⌨️ (0:50:54) Quantiles and Percentiles
⌨️ (0:55:52) Importance of Data Visualization
⌨️ (1:05:14) One Variable Graphs
⌨️ (1:12:04) Two Variable Graphs
⌨️ (1:25:08) Three and Higher Variable Graphs
⌨️ (1:31:20) Programming
Thanks
Amazing, amazing job! Nice touch with the stock photography, which looks to be original. Learned a lot from this one. Keep it up!
Awesome breakdown on this topic Max! I have my roots in physics as well and went into it-consulting a few years ago. I have been looking for good introductions on this topic. For me it was so much easier to follow the explanations coming from a physicist. Thanks for that!
I wish there was a scholarship offer (or some deferred payment option) for those of us in the developing world who can't afford to pay for this training but are sooo interested in learning Data Science.
There are many free resources out there. R and Python are free and open sourced. All it takes is a computer and some dedication. When you can, buy a good book or two. Listen to success stories on the freecodecamp's podcast to probably get some ideas on how you can make your own success story.
Thanks so much, Stefan. I am learning on freecodecamp but the training doesn't include Python. Nonetheless it's extremely helpful.
Hey! Feel free to send us an email via the website, and we'll see what we can do. I'm sure we can find something that makes sense for both!
Wow! Thanks for the reply. I'll check your website now and send an email.
Coffee production can indeed have a notable impact on climate change, primarily through the following factors:
Firstly, coffee cultivation often requires deforestation, especially in regions like the Amazon rainforest. The removal of trees, which act as carbon sinks, results in the release of stored carbon dioxide (CO2) into the atmosphere. This contributes to higher atmospheric CO2 levels, a major driver of global warming.
Secondly, coffee farming relies heavily on water resources, and inefficient water use can lead to ecosystem degradation. In many coffee-producing regions, unsustainable farming practices, such as excessive irrigation and chemical fertilizers, can contaminate water supplies and harm aquatic ecosystems. The disruption of these ecosystems can further exacerbate climate change by releasing methane (a potent greenhouse gas) and reducing the planet's natural ability to regulate its climate.
Finally, coffee is often transported over long distances, consuming substantial energy and emitting greenhouse gases during transportation. This adds to the carbon footprint associated with coffee consumption, as emissions from shipping and logistics contribute to global warming.
In summary, while the link between an individual's cup of coffee and immediate climate change may seem distant, the cumulative impact of widespread coffee production practices, including deforestation, water use, and transportation, can play a role in contributing to climate change when viewed at a global scale.
So it may affect it will rain or not depending on no. of cups if coffee we drink ....
Just kidding!! :)
Awesome video, thanks for putting in the effort to make this! It's funny, I was totally uninterested in anything to do with math or statistics while I was in school, but now I think this stuff is really cool! Being able to use data to learn from the past and guide your actions in the future...what's not to like?
same
Yay!! So excited to see this up!
Hi
Thanks I like data science , software engineer , actuarial science for resilience , to invest in it from aereospace to lithium computers
Thank you so much Max. Never stop what you are doing.
This introduction is really clear Thank you Max for your hard work, well done!
I learned all this as a psychology student. I had no idea I was also being trained as an data scientist lol
This is for beginners. It is just the basics. This is taught in a lower statistics course that is required for a lot of majors
This is the video I was waiting for!!
Patricio Reese I see that profile pic everywhere. Is there a meaning to it and who is it? Neo?
@@markbaladad6710 is from a game called Deus Ex I believe, a youtuber called MaximilianMus has it and he told his followers to put this image in our profiles to clarify that we are his fans, so it is like a meme now hahaha
@@patricioreese4468 I had the same question.Thanks for clearing my doubt.
Quantitative Research Methodologies Q&A
1. Evaluate the scope of quantitative research methodology comparing each method and critique each technique, model, metaphor, and paradigm. 200 words
Ethnographic studies are commonly used in research methods. The model of ethnographic studies is based on the researcher following participants or subjects into their culture to gain more insight into cultural issues. A metaphor of an ethnographic study is following customers to their home to understand why they purchase a given product and not another. The three paradigms of ethnographic studies include behaviorist, semiotic, and holistic styles. Interviews and surveys are the specific techniques embraced in ethnographic studies (Park, & Park, 2016).
The narrative method includes the use of two main techniques; interviews or collecting information from person’s documents that include diaries, memoirs, and other personal encounters narrative documents. The narrative is selected carefully to be a metaphor of the whole population, for instance, someone narrating how a calamity strikes their community might be narrative that can tell what the whole community experienced. The models of narratives can be spoken, written, or visually represented. The paradigm is that what one person experienced does not differ significantly from what the rest of the community experienced concerning the specific problem being analyzed (Park, & Park, 2016).
The third methodology is the phenomenological study which embraces multiple techniques that include interviews, surveys, literature review, and others to describe phenomena. The main models embraced in the phenomenological study include purposive sampling and systematic sampling. The metaphor of this method is that the collection of data from varying sources will give a united theme and data relevant to understanding the phenomena. The paradigm, hereby, is that the collected data about a certain phenomenon will show common features about the phenomena (Park, & Park, 2016).
The fourth method is grounded theory. It is closely associated with the phenomenological study in that it embraces the use of varying sources of information to develop a theme and collect data about phenomena. Its techniques, models, metaphor, and paradigms are similar to those of the phenomenological study (described in the paragraph above), but unlike phenomenological studies that look into the essence of an event or activity, the grounded theory seeks to give theories or explanations behind an occurrence or event (Park, & Park, 2016).
The fifth quantitative research methodology is the case study method. This method looks into occurrences as they affected a subject or few subjects. The techniques in this method include a single subject case study or multiple subjects’ case studies. The models of inquiry may include interviews, observations, or literature review for past events case studies. This model has a paradigm that what happens to one person applies to other people with a similar problem in society (Park, & Park, 2016).
2. Select the best quantitative method and assess the strengths and weaknesses of that selected method defending why the selected quantitative method is the best. 80 words
The best qualitative method in my opinion is an ethnographic study. Given my interest in social science, I find that understanding the behavior of a given group through interacting with the group is the best model to use. Moreover, unlike case study design this method allows for interaction with a larger number of participants or subjects with similar concerns. Moreover, it is a method that develops a hypothesis that can be approved or disapproved by the research, unlike the phenomenological and grounded theory designs that look for common themes during the active research. One of the weaknesses of ethnographic studies is that it can consume a huge amount of time. The researcher can also be faced with great challenges fitting in with a new culture and gaining their trust after s/he declares interest to understand their cultural elements.
3. Compare various quantitative methods and how each method enables researchers to design the correct series of questions and eventually hypotheses to prove the theories. 120 words
The ethnographic study follows researchers into their cultural roots to understand their behavior. It is appropriate for cultural studies that aim to understand behavior such as consumer behavior. The narrative aims at gaining opinions from specific subjects, or their encounters concerning a certain problem. It closely relates to the case study design which also focuses on individual’s stories. These two methods can yield results in understanding people’s reactions to certain societal problems, for instance, the experiences that parents undergo after losing a job. The phonological studies and grounded theory methodologies are similar in many aspects including the fact that they seek a common theme from multiple sources of information such as interviews, literature, surveys, and other sources. However, the grounded theory seeks to explain or develop a theory describing a certain societal concern, while the phenomenological study looks into the essence of the societal concern of interest.
The ethnographic study, case study, and narrative begin with preparations that include the identification of the societal problem, development of research questions, and a hypothesis that the study will either approve or disapprove. The phenomenological study and the grounded theory designs, on the other hand, have a planning process that only includes the identification of the problem and sources from which information will be obtained. These two quantitative methodologies develop a common theme in the field of study, which gives research questions to be responded to and maybe a hypothesis which is not mandatorily essential.
4. Recommend best methods to solve different types of analysis and provide a table that outlines each method, what type variables are used, best applications, and what findings to expect. 100 words
Descriptive analysis Predictive analysis Diagnostic analysis Prescriptive Analysis
Recommended Method Case study, cross-sectional research, surveys, naturalistic observation, archival research, and longitudinal research Analytics techniques such as regression models, time series, and others. Case studies, questionnaires, interviews, surveys, and observations Mathematical learning and machine learning methods
Types of Variables Descriptive variables such as “agree or disagree” Predictor variables; variables linked with certain outcomes such as regression variables Descriptive variables such as a scale of 0-10 in rating pain Both predictor and descriptive variables
Best Application Understanding phenomena Extrapolation of past and current events into uncertain or future predictions Understanding personal outcomes such as feelings, opinions, and others Big data analytics essential in making an informed recommendation
Expected Findings More understanding of the phenomena that has already occurred or is occurring Forecasts of what is to happen An understanding of personal outcomes Recommendations for courses of action
I studied with the book series "“Data Science and Engineering - A learning path" , that teaches Data Science with exercises in Orange Data Mining
Thank you so much Max !
Crystal clear explananations about the basics of DS
Congratulations
I wish this was pratical. I can't find any tutorials that use simple language when talking about data science.
Thanks for all the great work you folks do.
Thanks for the upload and for the timestamps.
So, far the most simplified lecture on data science
I am a first viewer too. So grateful!
You are not the only one LOL.
ok, whatever - I am still grateful....@super3ree
Hi Max, This is great stuff for any beginner in data science. Do you have any videos/tips on probability and logistic regression that are easy to understand as well? thanks!
There is a free one by udacity on UA-cam. It's a playlist. It's not organized tho, but goes over real life code and it's pretty good.
Good staff, I like the way you're explaining the course
Hello Max, it's a beautiful thing you are dong. I just want to ask, what are the most recommended websites, books or podcast to learn Data Science because sometimes surfing through the web to get an efficient and effective training can be a bit overwhelming. BTW I really love Data Science and I hope one day I will be training others as you are. Thanks.
Love from Nigeria.
Install google podcast on your Android phone and search for Data Science, machine learning and AI channels and thank me later. Both podcast host and guests always discuss where to get materials and lessons. I am based in Nigeria but I have learned so much on the platform
Nwanne, thanks for the information
@@simonkalu thank you.. just installed google podcasts
😂😂😂 hum..yah...I see the data goes up and down...that was funny!!!
دى البرامج وممكن حضرتك تضيف
Ai
Machine learning
robotics engineering
Big dataبيانات الضخمة
هندسة برمجيات
Analysis with sql
البلوك تشين
Introduction to data science
Thank You so much, Max! Helped out a lot!
So as someone who eventually one day wants to work in data science industry, is there an entry level job role that one can apply as a starting point and just get promoted/ level up to a data science in time rather than just trying to apply for a data science position from the get go? I dont mind starting at the bottom and working my way up the ladder.
Awesome video! I learned a lot and very useful. Thank you so much!!
With the box and whisper plots, I wasn't clear on what the different things (boxes, vertical and horizontal lines, and symbols on the lines) represented. I liked the video a lot in general, though.
From the course, it seemed that you are more like from Data Visualization rather than Data Scientist. You didn't talk about Probability distribution or Hypothesis Testing which are basics things for basic Algorithm like Linear Regression, and the second thing box plot is used to plot single variable to check how the values are spread out. We can plot single variable like Salary in box and Whisker plot. Data Scientist is much more than just visualization.
Hi Naznin - thanks for your comment and thanks for watching!
As I mentioned in the video, this is a super intro and brief look into the 3 main components of data science - the pillars, if you will, not the exact strategies or tools that I personally use in my job as a Data Scientist. 🙂
And you're absolutely right - data science is way more than just visualizations, but without the proper foundations - you can't get to the more complex, advanced techniques.
Before you can apply any machine learning algorithms, you have to first understand how to approach an exploratory analysis as well as how to do your own visualizations.
Imagine using a random forest algorithm without being able to correctly understand or visualize the decision boundaries or being able to perform correct exploratory analysis for feature selection and optimization. You wouldn't be able to correctly interpret your results, let alone be confident in them.
Hope that makes sense! Let me know if you have any other questions. 🙂
this videos motivates me to learn more about data science. such an interesting thing!
Well done!! Easy to listen to. Thank you!
Thank you so much this is the easiest explanation I hv ever seen
Computer Science and Engineering 2nd Semester, Intro to Data Science, 5 weeks of lectures in one single video. Either you teach good or our professor teaches slow.
Both
Hi Max, Very informative and interesting, Question, at 13:16 you talk about Continuous data and contrast every number from -infinity to +infinity (i.e. numerical infinity) with every number between "0" and "1", however, can those numbers be equally infinite as subdivisions can be infinite?
24:10
If you think at the molecular level, weight is probably discreet and not continuous
I'd also argue the way we one gains weight is also discrete.
Sure, if you want to use increments of the weight of a single atom.
Are you sure that is a right example for 2d histogram?
This looks like basic maths to me. What's the difference between Statistics and Data Science?
What can be the “data” can someone give an example? I understand it can be *anything* but like what? A persons personal info or what
I don't follow quartiles: there are four quartiles, but only three listed (at (52:12). I didn't follow the explanation for that. Maybe display it by listing all four?
Really solid content. Wish I'd seen this before I've graduated lol
Hi Max.. Welcome From INDIA...🇮🇳🇮🇳.
AND FROM YOU..????
Hey Manish, I'm from Germany :)
From NEPAL..😍😍
Hello,
I want to learn more about MI and I don´t know where to start. I just watched some math videos but it became boring over time just to do the math (I am not bad at math and atm I am studying IT-Security so I am familiar with "University Math"). This Video was great but it covers just a small piece of what I want to learn. Maybe someone have far-reaching videos that one can recommend? Thank you ;D
This was very helpful, thank you lots!
Nice mee too I'm learning data science after my bachelor degree in physics!!!
Hi Max ,thank you for your tips. Mima can understand that.
Upload blockchain technology fundamentals and programming crash course from scratch
Awesome course. Keep making such great videos.
How can I become a data scientist with no programming experience? What are the steps shall I follow to become one? Thanks
first off you need to learn programming like Python Or R and then Sql and Machine learning with python...! get through these hurdles then you'll know what to do next ! i know this answer is 11 months late but i hope it'll help you! Adios
Thank you! Really helpful!!
Thanks a lot for this content.
plz , make more videos like these
Adding a link to be able to download the slides can be good. Ty
THank you sir.
I am writing to formally request the assistance of a tutor in the field of data science.
I don't know how I would apply this. How many years does one need to study to apply for work?
weird how i cannot hear anything from this video. Damn how do i fix this...
thanks
a good summary of middle school statistics
Resume at 38:55
Before I go into this course,
If all programming languages do not exist.. is data science still exists?
In another way .. is pure data science independent of programming?
yeah
Very general data science intro. But it's good cuz it's not too intimidating. More examples would have been better. More guidance in terms of other free resources would have also been good. But this video is good for beginners to understand statistics behind it.
Very helpful video - we can also recommend to study Applied Data Science at MU Vienna - small class sizes and great professors! Check out our curriculum.
*This video is awesome*
Excuse me im kinda new to this thing i have a question do i need paper for all of your data videos or anything or can i just watch the video??
love it !!!!!!! 😍😍😍😍😍😍😍😍
please make complete course on laravel 5.8
How did you teach yourself to do all of the stuff?
1:03:00
17:40 i got some news for you in 2023
Thank you!!!
I want crash course on Tensorflow
Hopefully soon. While you are waiting, you can check out this course on Tensorflow.js: ua-cam.com/video/EoYfa6mYOG4/v-deo.html
Fire content!
Why should a weight of an adult be a discrete numerical value ? I mean If I put a cheeseburger in my mouth in ones, I’m jumping from 70kg instantly to 70,3kg or am I wrong ?
U R AWESOME.
Also please make course on AI
What is the actual difference between this an statistics? Like truly, aside from "oh we use scientific "methods"", what is the legitimate difference aside from type of program used and end-point application of statistical practice? Algorithms, technology, etc.? That is just statistics "but with extra steps" lol.
Amazing
Is there anyone in your channel who can post Blockchain developer course.. Please??
what to know before this course plz tell meh
This is a beginner's course. You just have to have basic familiarity with a programming language. You don't need any advanced math to understand this course.
You won't need that much programming knowledge for this course since it mainly covers concepts.
Hai Max I want become data science plz help me I am a beginner I completed degree bsc
Good content
Good. Video
Hi Max
How can I continue learning with you
I wish I had taken this in college.
Nice intro, please kindly help me,l wants to continue in this line.
30:00
Thank you!
13:30 Hold up! Hold up! Does negative and positive infinity exist???
Of course they both exist. Numbers can infinitely increase in the positive direction as well as the negative direction. And that's only on a one dimensional scale (i.e., a number line). Think about coordinate points infinitely going in one direction on a 2D or 3D graph.
10:00
22:53
Https://en.wikipedia.org/wiki/Growth_chart
I need subtitles 😭😭
bottom right, button on the left
blessess
Did you mean Gaus Sian?
please upload tableau full course
Bo'Oh'O'Wa'er
can anyone please tell differance between data science and machine/deep learning?
Data science is a mix of statistics and machine learning
Machine/Deep Learning is essentially trying to figure out a math equation to describe your data. In terms of application it's either statistical techniques and/or optimisation techniques to reduce errors (through calculus).
Data Science is a broader term that can encapsulate machine/deep learning, but it doesn't rely on that. Data Science is all about analyzing data, making sense out of it, and taking valuable and (hopefully) actionable insights from it.
So machine/deep learning is just one approach to analyzing data, and it is a part of Data Science, but there are many other approaches you can take to investigate your data and get value from it that don't rely on machine/deep learning.
I have experience in us recruitment and bpo and i wan to be s data scientist... how can i become data scientist in next 6 months.
Devalued the content by ad-libbing technical concepts with incorrect words and phrases. Should have followed a script.
No its me the first
Really? ?????????