Conclusion of Introduction: Next Steps 1:36:33 Data Sourcing 1:39:45 intro 1:40:35 measurement: metrics 1:46:49 measurment: accuracy 1:50:40 measurement: social context 1:54:17 getting data; existing data 2:01:26 getting data: api 2:07:50 getting data: scraping 2:13:09 making data: 2:37:07 data sourcing conclusion Coding In Data Science 2:32:42 intro
Part 1: Data Science: An Introduction: Foundations of Data Science (0:00) content - Welcome (1.1) - Demand for Data Science (2.1) - The Data Science Venn Diagram (2.2) - The Data Science Pathway (2.3) - Roles in Data Science (2.4) - Teams in Data Science (2.5) - Big Data (3.1) - Coding (3.2) - Statistics (3.3) - Business Intelligence (3.4) - Do No Harm (4.1) - Methods Overview (5.1) - Sourcing Overview (5.2) - Coding Overview (5.3) - Math Overview (5.4) - Statistics Overview (5.5) - Machine Learning Overview (5.6) - Interpretability (6.1) - Actionable Insights (6.2) - Presentation Graphics (6.3) - Reproducible Research (6.4) - Next Steps (7.1)
⌨️ Part 2: Data Sourcing: Foundations of Data Science (1:39:46) content - Welcome (1.1) - Metrics (2.1) - Accuracy (2.2) - Social Context of Measurement (2.3) - Existing Data (3.1) - APIs (3.2) - Scraping (3.3) - New Data (4.1) - Interviews (4.2) - Surveys (4.3) - Card Sorting (4.4) - Lab Experiments (4.5) - A/B Testing (4.6) - Next Steps (5.1) ⌨️ Part 3: Coding (2:32:42) content - Welcome (1.1) - Spreadsheets (2.1) - Tableau Public (2.2) - SPSS (2.3) - JASP (2.4) - Other Software (2.5) - HTML (3.1) - XML (3.2) - JSON (3.3) - R (4.1) - Python (4.2) - SQL (4.3) - C, C++, & Java (4.4) - Bash (4.5) - Regex (5.1) - Next Steps (6.1)
⌨️ Part 4: Mathematics (4:01:09) content - Welcome (1.1) - Elementary Algebra (2.1) - Linear Algebra (2.2) - Systems of Linear Equations (2.3) - Calculus (2.4) - Calculus & Optimization (2.5) - Big O (3.1) - Probability (3.2)
That's very important when you have along video you have to have an index so the people don't get bored, that should include the time in each subtitle, a thing that you have to correct.
I got into data science through nursing, I was an ICU nurse looking for a hiatus in the job, that’s how I broke into the field, via the hospital system but I do have a secondary degree in bio chemistry which helped significantly on the quantitative side of things. Bio statistics is a staple in an biochemistry program and if anyone else’s prof’s were like mine, you’d swear they were teaching engineering in the amount of quantitative topics I was put through. I was however very weak on the he business end of things and had to be paired with someone for about a solid 8 months. But it was an awesome transition to another career.
8:41 diagram venn 15:08 DS pathway 19:40 role of DS 23:41 team in data science 48:37 Method 1:02:22 statistic method 1:06:20 ML method 1:09:00 communicatting (interpretability etc..)
This is my first time in data science. I've listened to thousands of lectures in my life. Barton Poulson explains it very well, in a very understandable and motivating way. Thank you very much to him. I recommend to those who want to take this course.
@@vengalrao5772 I would say it is. Though he gives working examples of hte methods and tools he talks about but even if you cannot actually use it yet you get a grasp of what it is, how it works and whether you need to learn it. To me the course seems fantastic - I now know which direction to move further, which elements I already know and which I need to learn.
I'm deeply moved with this presentation. I spent some 40 years in various IT areas, ending up as an IT manager and retired. Now I'm 81 years old. During my IT career, I had always put questions to myself: what all this IT complex is for? Any goal for all these IT things? The answer is here! Thank you, Dr. Barton.
This is the only RUclips video that I have ever commented. I think it is simply brilliant! How easy is to understand with the explanation and the speed of presentation is simply amazing
I came from a business school, fell in love with statistics, and decided to become a teaching assistant there... Little did I know back then that it's the reason why I'm in this data science rabbit hole. And I'm loving it so far!!!! Edit: Just discovered I'm on the rare category. 🤯
I have gone through the whole video and am really grateful for the time you've invested in this. The vivid pictures and friendly speaking pace were truly refreshing and helped balance the ubiquity of the text. Cheers from Abidjan!
I am only 2 hours in but I LOVE the way you demystify what I once thought was so out of my league/ability and perhaps interest as well. Thank you so much!!!
Hey cassandra, was this helpful for beginners? I want to start, and i needed to know whether this is where u started or there is another video i may watch to begin before this
Quincy if u are reading this......We all campers really appreciate from bottom of our heart for whatever u r doing for us. I just want to say that nobody gives a damn if u start putting ads in between the videos. I have an ad blocker but for the sake of this channel I'll disable it, as that's the only way I can contribute right now and there are many more like me. Start putting ads.
This guy is motivating and teaching you data science in under 6 hours that no one could teach you. 10-12 ads are totally fair, others hire paid teachers and still can't understand a thing. This guy is making many's life. Watching this video before starting and between your data science career, you can be the data scientist that everyone wants. Thank you, Barton Poulson and Freecodechamp for this life-changing course.
Hey 👋.....Can an Msc Biotechnology Graduate learn data science and get certified and can they be hired by companies to become data scientist? Or only computer science background students can get this job??
@@Dariusrae45636You can be a data scientist if you don't even have a strong background from computer science. Even many people who are data scientist now are mostly from other fields, sometook enginnering fields and some took other.
Half way in and this looks more like an overview than a training. Not sure you will learn data science by watching this but you will get the picture of what’s involved.
I'm not gonna lie, there is no chance I'm going to watch all of this, but from what I've seen so far, this is an AMAZING beginners guide to understand every facet of data science. Thanks for this awesome resource. I'm excited to see more resources popping up showcasing more projects and real world experience beginners can learn from
please try to upload some basic projects of how data scientist do their work professionally in the industry it will literally help students to learn in real time.
A fully-worked example project would be wonderful. Is there a particular kind of project that would be most useful to you? And is there a language that would be best?
@@datalabcc all the jobs that are link to data uses python quite vast so working and practicing every attribute of this language helpful and beneficial for all the novice students for instance we know how to manipulate basic data set with some python commands but we dont know all the working stages of data scientist or data analyst in the real time industry and how a project is to be done and what possible outcomes does the industry make after when a data scientist completed their job on a given data set and what makes them worth to earn thousands of money believe me this gonna work like a big motivation for the students.all in all let me give an example take basic data set like uci-iris and do all the necessary work on it upto maximum extent and what possible results comes out from this especially what we looking in it and why and how to make final report of any particular data set after completing it and what decision could be made when the company manager sees it.
Omg! I’m a psychologist that is interested in Data Science too. I thought that I was the only one. Even I’m learning English, in exhausting form, because there’s lack of information in Spanish. Thanks for the content and all your job in the channel!
I did it , and survive So generous to have made this, Thank you ! the Plus , excellent narrative, but i would suggest to take a real life DATA science project and stick one by one the concepts you have patiently teach for 6 hours . if not the concept are TOO ABSTRACT to be RETAIN. OVERALL , Great but adding a real DATA science project from A to Z would be AWESOME ! Thanks
A fully-worked example project would be wonderful. Is there a particular kind of project that would be most useful to you? And is there a language that would be best?
This is a great video! Very well explained concepts. As an established data scientist myself, it helps to have resources like these to brush up on my fundamentals. I also have a few videos on my channel that talk more about the experience working as a data scientist for those that are interested!
Amazing course! I'm having a turn in my career after 10 years of school teaching, and this course is just what I needed to complement my studies in DS. Just an _errata_ : at 2:13:20 you mentioned "data de novo" as your personal expression for the concept of "new data". I don't know you if it meant for this expression to be extracted from portuguese language or latin, but if you took it from portuguese, the adequate expression should be "Nova Data" or "Novo Dado". In portuguese "de novo" means the same as "again" or to do something one more time.
As a student of political science and international affairs with some background in anthropology and sociology, I was abnormally interested in this area, specially explained in such beautiful manner, but when you added the social sphere everything clicked, and hence why they are considering me for an internship. This would be fun if with a proper team.
Hello! I just finished the whole video and would like to say thanks! I hope this starts out my journey in learning more about data science. This provided a wide overview of the concepts, tools, and thinking that will be needed in DS. Without being daunting and yet not mind-numbingly dumbed down. Again, thank you!
This Course is made more towards for what the real and practical advice for work that needs to be done in one video, not for actual learning of ML, DL, LLM, CV or really anything specific as a skill not exactly what I was Looking for.
Please keep in mind that this course was originally published in 2016. It seems like it's a wonderful take on the foundations of Data Science, but some of the information may be missing and/or outdated.
⌨ Part 1: Data Science: An Introduction: Foundations of Data Science - Welcome (1.1) - Demand for Data Science (2.1) - The Data Science Venn Diagram (2.2) - The Data Science Pathway (2.3) - Roles in Data Science (2.4) - Teams in Data Science (2.5) - Big Data (3.1) - Coding (3.2) - Statistics (3.3) - Business Intelligence (3.4) - Do No Harm (4.1) - Methods Overview (5.1) - Sourcing Overview (5.2) - Coding Overview (5.3) - Math Overview (5.4) - Statistics Overview (5.5) - Machine Learning Overview (5.6) - Interpretability (6.1) - Actionable Insights (6.2) - Presentation Graphics (6.3) - Reproducible Research (6.4) - Next Steps (7.1)
⌨ Part 2: Data Sourcing: Foundations of Data Science (1:39:46) - Welcome (1.1) - Metrics (2.1) - Accuracy (2.2) - Social Context of Measurement (2.3) - Existing Data (3.1) - APIs (3.2) - Scraping (3.3) - New Data (4.1) - Interviews (4.2) - Surveys (4.3) - Card Sorting (4.4) - Lab Experiments (4.5) - A/B Testing (4.6) - Next Steps (5.1) ⌨ Part 3: Coding (2:32:42) - Welcome (1.1) - Spreadsheets (2.1) - Tableau Public (2.2) - SPSS (2.3) - JASP (2.4) - Other Software (2.5) - HTML (3.1) - XML (3.2) - JSON (3.3) - R (4.1) - Python (4.2) - SQL (4.3) - C, C++, & Java (4.4) - Bash (4.5) - Regex (5.1) - Next Steps (6.1)
⌨ Part 4: Mathematics (4:01:09) - Welcome (1.1) - Elementary Algebra (2.1) - Linear Algebra (2.2) - Systems of Linear Equations (2.3) - Calculus (2.4) - Calculus & Optimization (2.5) - Big O (3.1) - Probability (3.2)
@@datalabcc Thanks for this incredible lecture. I was wondering if you could also let me know where I can get the data sheet (excel) from. I did download a few samples from Kaggle as you suggested, but was hoping to work on your datasheet first and then on the rest. Most of the ones on kaggle do not have a defined datatype in the rows. So it's being a bit tricky there especially the ranged columns.
I know this is a late comment, and I'm not sure how far along in your learning. That being said, my answer to you is this: fortunately, the programming fundamentals between Data Science and Software Engineering are interchangeable. However, it depends on how you want to use those programming languages. If you prefer building infrastructure and pipelines, full-stack websites, and data environments, software engineering is more for you. In contrast, if you enjoy cleaning and dissecting data, and optimizing company workflow with information being processed across several statistical measures, data science is more for you. Both are algorithm and math heavy, the deeper you go. From what I've seen/read (as I'm not someone there yet) at the higher levels, data scientists and data engineers choose which they want to prioritize. Both fields are highly attractive, highly sought, and highly necessary.
Over so many instructors, the speed and clarity of this instruction is the best. Some of the professors that teach course over MOOC, they were chasing for the next train. TQ FCC...
A hearty thanks, ❤ I just completed my 10th boards, And was in search of a best career option for self with all the clarifications , And you this video, made me get it perfectly with no doubt remained in me , And really the ones who are contributed in makeing this video are psychologists, who actually knows how we (students) do think ! Really meant alot ❤❤❤
A must watch video for anyone who is interested in Data Science. * Excel is highly used in reality, VBA based on Excel should be considered as a programming language
This is Bart Poulson, who created the video. I spend a truly inordinate amount of time telling all of the data science people I work with that they need to be fluent in spreadsheets, first and foremost. VBA is absolutely a programming language and I apologize if I suggested otherwise. It's a fabulous tool!
This is excellent even for people already working in related fields because it talks about many tools and concepts they may have overlooked. The organization, clarity, and Paulson's breadth of knowledge are impressive!
finished the full 6 hour course. very imformative . i was thinking of learning R for my research analysis. before that i thought i need some foundation on data science and i saw this video. thanks a lote❤
Great course! Content is informative, well structured, and explained clearly. It is evident that Barton Poulson is very knowledgeable and his personality really shows through. His spirit is encouraging and I thoroughly enjoyed his humour. Keep up the inspiring work!
Sir, you are one of the best lecturers whose class I've had the good fortune of stumbling across. Your philosophical backdrops and psychological insights made the experience ever so more pleasant and freshly original.
Thank you ❤. I'm not a techy person. However the way you handle the material is so smooth, so compelling that my fears have disappeared and I'm ready to start learning
i know you dont want to interfere your students with ads when they are watching videos but dude there is a options in monetization section where you can specify which ads you want to show on your videos leki you can enable sideview ads (ads above the video recommendations). I love that you are providing all of this stuff without earning a penny. I really want to give you some economic boost but i seriously dont have money. enable that ads to make more money to produce more quality content like this Thanks again!!
I’m a financial analyst right now but I already know SQL and Power BI. I’d like to learn Python and R so I can move to a role where I can combine my existing finance knowledge with data science
Hai sir ....your work in this data science field ......if you know can you explain please.... I am willing to start my new career's in data science field
Best video ever for 360 degree understanding of the field of data science!! I’ve only watched 20-odd minutes, but already I have so much clarity. Plan to do the entire series. Thank you!
Love your voice I could hear for the next 6 hours without any distraction... lot of misunderstandings N abilities many more were covered with ease... completed in two days the course... As you said that u were from psychology did very well in implementing things on very one cud be part of data science.. so much encouragement... Love your work N following you... All the best for your future N i update my Data science enhancement from being a beginner..
Hi, In Part 3, Coding, Prof Poulson refers to downloading spreadsheets and other downloadable data to test with tableau, please can you give me the url so I can download them?
I spent over 10 years in marketing (Web analytics, A/B testing, Conversion Rate Optimization, online ads etc. - so basically I always was on this technical/business side of marketing) and 1.5 years with Python (web scrapping, automation) but I always sucked at math. Big time. Data Science and ML are extremely interesting for me, I can see many applications for my day-to-day work in marketing but I'm scared shitless of this math part :)
Michael, this is Bart Poulson, who created this course. It sounds to me like you already have most of what you need and, unless you're going into algorithm development, huge amounts of math may not be necessary. I imagine that your familiarity with analytics and experience with automation already does a lot for you. But what specific plans do you have?
Thank you all for an amazing video. Could you please upload a video how we can implement data science on real life data. Like using python or R programming with SQL and these concepts. thanks again.
First off, thank you for this course. This was a huge eye opener for me about all that goes into data science. They say you don’t know what you don’t know and I realize now that I don’t know much at all. I was really enjoying using SQL and Python to pull data down and “play” with it but after watching this entire video I think I need to find something else to do. This course was so incredibly dense with theories, concepts and terminology that learning this just feels impossible to me. Maybe if I had taken collage level algebra, statistics and calculus this may have been a great refresher but without that prior knowledge, this just seems unattainable. I thought that data annalist roles are less intense than this but now I'm just not sure about anything. Regardless of my ability to understand this, the video is amazing and incredibly well done. I’m sure it will help a lot of people getting into data science.
Drew, this is Bart Poulson, who created the video. It sounds to me like you're doing exactly the right thing. The math background is nice but far from essential. Curiosity about data, along with some facility with tools like SQL and Python, all of which you have, are an excellent foundation.
@@uizzximoti5734 yes, you could. For example, you could train neural networks on pure c++ or java/javascript. You could use ML models from R and do the whole analytics just with R.
@@nicolasramirez865 RUclips won't auto-subtitle any video over 4 hours, unfortunately. But we would welcome your help adding Spanish subtitles. There are several ways you can make the freeCodeCamp community's videos more accessible for Spanish-speakers: contribute.freecodecamp.org
*Introducing Data Science*
0:02 Data Science, An Introduction, by Barton Poulson
0:22 "Data Science is too techy" some people say.
0:44 Data Science is creative, using code/statistics/math tools,
1:05 to solve problems and get insight.
1:35 Everything signifies.
*Defining Data Science, What is Data Science? What do Data Scientists Do?*
2:17 Data Science Is
• Coding
• Statistics
• Domain Knowledge
*Promoting Data Science as Rare and Highly Demanded as a Skillset*
3:11 Harvard Business Review.
3:37 + Rare Qualities
4:03 + High Demand + Competitive Advantage.
4:46 People need Data Scientists to work.
5:18 Learn how to speak the language of Data Science.
5:40 LinkedIn Article promoting Statistics and Data Science.
6:05 Glass Door Article promoting Data Analysis.
*The Data Science Venn Diagram*
7:47 Drew Conway created The Data Science Venn Diagram
8:22 Coding, Stats, Domain Knowledge
• Coding 8:44
• Statistics 9:30
• Domain Knowledge 10:59
• Statistical Coding
• Database Coding
• Command Line Coding
• Search Coding
10:20 Math
• Probability
• Algebra
• Regression
+ Math helps to understand the various problems dealt with in Data Science.
*Machine Learning*
11:37 Black Box Models
*Traditional Research*
12:27 Structured Data
*The Danger Zone* ⚠️
13:07 Coding and Domain without Math.
*Data Science Introduction*
14:45 The Data Science Pathway
Step 1 -> Step 2 -> and so on
First: Planning 15:10
Second: Data Prep 16:10
Third: Modeling 16:58
• Ex. Regression Analysis
• Ex. Neural Network
+ Validate The Model
+ Evaluate The Model
+ Refine The Model
Fourth: Follow Up 17:45
19:00 Data Science involves
+ Contextual Skills
+ One Step At A Time
*Data Science Engineers, Database Developers & Administrators*
19:55 Data Engineers
21:50 Business relevant questions.
22:20 Entrepreneurs, Data Startup businessmen.
22:44 Full stack “Unicorn”
23:44 Many Tools 🧰
Coding
Statistics
Design
Business
• it takes a team, although “the unicorn” could do it all.
24:44 Talent Assessment on 5 Areas of Data Science.
27:20 Similar but not the same.
*Big Data*
28:33
32:50 Coding & Data
34:30 Data Science is NOT = Coding
37:39 Most Data Scientists are…
37:56 Data Science and Science both do Analytical assessments but in different niches.
41:06 Data Science and Business Intelligence
*Ethics in Data Science*
42:44
Do not share confidential information without permission.
43:43 Anonymity
44:40 Copyright ©️ Data Restrictions
45:20 Data Security
46:08 Potential Bias
47:04 Overconfidence
48:03 Good Judgement is vital to Good Data Science.
*Data Science Method: How To Do Data Science Procedures*
49:22
52:47 Interviewing, Surveys. 53:36 Metrics, KPIs, SMART goals, Classification Accuracy.
54:47 Coding in Data Science.
56:35 Coding Languages.
58:00 Data Science Math.
1:00:30
Elementary Algebra
Systems of Linear Equations
Calculus
Big O
Probability
Bayes Theorem
1:02:00 Statistics 📊
Finding Patterns
1:03:00 Inference
1:03:40 Feature Selection, Model Validation. Estimators. How well the model fits the data.
1:06:05 Machine Learning.
1:07:39 Prediction.
*Communicating Clearly*
1:08:55 Interpretability.
1:10:55 Egocentrism, put it in terms someone else can understand on that person’s knowledge.
1:12:15
State question
Give answer
Qualify as needed
Go in order.
1:13:08 Simplify into the greatest value.
1:14:14 More charts, less text. 📊
1:15:20 There are details that color the data shown in the chart. Make sure to get those details to get the truth.
1:17:45 Be concise and clear.
1:18:40 Data is for doing.
“We’re lost but we’re making good time.”
1:21:47 Social Understanding.
• Mission
• Identity
• Business Industry
• Context
1:23:30 Speed and Responsive Data Analytics
1:24:25 Clarity
1:26:15 Get the point across.
1:29:25 Simple Bar Charts answering 1 question each. Put together they lend support to a thesis.
*Reproducible Research* “play that song again.”
Show your work.
1:30:20
1:31:31 Open Data Science Conference.
*Matrix Algebra*
4:07:24 Matrix Algebra
Can you suggest what to learn next after this
Thankyou
'Thanks' isn't enough!
Conclusion of Introduction: Next Steps
1:36:33
Data Sourcing
1:39:45 intro
1:40:35 measurement: metrics
1:46:49 measurment: accuracy
1:50:40 measurement: social context
1:54:17 getting data; existing data
2:01:26 getting data: api
2:07:50 getting data: scraping
2:13:09 making data:
2:37:07 data sourcing conclusion
Coding In Data Science
2:32:42 intro
Thank you very much.
Part 1: Data Science: An Introduction: Foundations of Data Science
(0:00)
content
- Welcome (1.1)
- Demand for Data Science (2.1)
- The Data Science Venn Diagram (2.2)
- The Data Science Pathway (2.3)
- Roles in Data Science (2.4)
- Teams in Data Science (2.5)
- Big Data (3.1)
- Coding (3.2)
- Statistics (3.3)
- Business Intelligence (3.4)
- Do No Harm (4.1)
- Methods Overview (5.1)
- Sourcing Overview (5.2)
- Coding Overview (5.3)
- Math Overview (5.4)
- Statistics Overview (5.5)
- Machine Learning Overview (5.6)
- Interpretability (6.1)
- Actionable Insights (6.2)
- Presentation Graphics (6.3)
- Reproducible Research (6.4)
- Next Steps (7.1)
⌨️ Part 2: Data Sourcing: Foundations of Data Science (1:39:46)
content
- Welcome (1.1)
- Metrics (2.1)
- Accuracy (2.2)
- Social Context of Measurement (2.3)
- Existing Data (3.1)
- APIs (3.2)
- Scraping (3.3)
- New Data (4.1)
- Interviews (4.2)
- Surveys (4.3)
- Card Sorting (4.4)
- Lab Experiments (4.5)
- A/B Testing (4.6)
- Next Steps (5.1)
⌨️ Part 3: Coding (2:32:42)
content
- Welcome (1.1)
- Spreadsheets (2.1)
- Tableau Public (2.2)
- SPSS (2.3)
- JASP (2.4)
- Other Software (2.5)
- HTML (3.1)
- XML (3.2)
- JSON (3.3)
- R (4.1)
- Python (4.2)
- SQL (4.3)
- C, C++, & Java (4.4)
- Bash (4.5)
- Regex (5.1)
- Next Steps (6.1)
⌨️ Part 4: Mathematics (4:01:09)
content
- Welcome (1.1)
- Elementary Algebra (2.1)
- Linear Algebra (2.2)
- Systems of Linear Equations (2.3)
- Calculus (2.4)
- Calculus & Optimization (2.5)
- Big O (3.1)
- Probability (3.2)
⌨️ Part 5: Statistics (4:44:03)
content
- Welcome (1.1)
- Exploration Overview (2.1)
- Exploratory Graphics (2.2)
- Exploratory Statistics (2.3)
- Descriptive Statistics (2.4)
- Inferential Statistics (3.1)
- Hypothesis Testing (3.2)
- Estimation (3.3)
- Estimators (4.1)
- Measures of Fit (4.2)
- Feature Selection (4.3)
- Problems in Modeling (4.4)
- Model Validation (4.5)
- DIY (4.6)
- Next Step (5.1)
Thanks
God bless you man
Why can I not locate the course files here?!
That's very important when you have along video you have to have an index so the people don't get bored, that should include the time in each subtitle, a thing that you have to correct.
Thanks man
Thanks to every single person who contributed their time to make this video.
Agreed!
@@Enatural_7 i dont dummy
Data science from scratch to advance. This video is sufficient? Or to go other source?
I got into data science through nursing, I was an ICU nurse looking for a hiatus in the job, that’s how I broke into the field, via the hospital system but I do have a secondary degree in bio chemistry which helped significantly on the quantitative side of things. Bio statistics is a staple in an biochemistry program and if anyone else’s prof’s were like mine, you’d swear they were teaching engineering in the amount of quantitative topics I was put through. I was however very weak on the he business end of things and had to be paired with someone for about a solid 8 months. But it was an awesome transition to another career.
8:41 diagram venn
15:08 DS pathway
19:40 role of DS
23:41 team in data science
48:37 Method
1:02:22 statistic method
1:06:20 ML method
1:09:00 communicatting (interpretability etc..)
Thanks!
Thanks
For the one who did the subtitles, god bless you
First bless yourself and do for others as well
This is my first time in data science. I've listened to thousands of lectures in my life. Barton Poulson explains it very well, in a very understandable and motivating way. Thank you very much to him. I recommend to those who want to take this course.
Yup..l Agree.
@@vengalrao5772 I would say it is. Though he gives working examples of hte methods and tools he talks about but even if you cannot actually use it yet you get a grasp of what it is, how it works and whether you need to learn it. To me the course seems fantastic - I now know which direction to move further, which elements I already know and which I need to learn.
I am interested in Data science, This is my first time in data science.
@@henrysiafa5524 same here, how is it going now?
And it's free :')
I'm deeply moved with this presentation. I spent some 40 years in various IT areas, ending up as an IT manager and retired. Now I'm 81 years old. During my IT career, I had always put questions to myself: what all this IT complex is for? Any goal for all these IT things?
The answer is here! Thank you, Dr. Barton.
This is the only RUclips video that I have ever commented. I think it is simply brilliant! How easy is to understand with the explanation and the speed of presentation is simply amazing
You know what... with stuff this valuable... an Ad or two or 3 wouldn't be so bad.
They just can't. because this is not their content they have provided from someone else's RUclips channel. Ofc with their permission.
well, why don't you send them a donation?
Cyb-beebies
🤫
I guess because it's categorized as educational it cannot include ads.
neither would a job or 3
Mark 1 - @1:39:50
Mark 2 - @2:52:09
Mark 3 - @4:05:01
Mark 4 - @5:02:44
Mark 5 - done. 🔥
This has to be one of the best videos Ive ever seen. Ever. It was like listening to an interactive audio book. Thank you so much
2:12:19 so far and cannot imagine how much effort these guys have put to make this. This is really a beautiful attempt. This is great. Thank you, FCC.
How can you watch this and *not* leave a thumbs up? Brilliant, even for practicing ML engineers!
I came from a business school, fell in love with statistics, and decided to become a teaching assistant there... Little did I know back then that it's the reason why I'm in this data science rabbit hole. And I'm loving it so far!!!!
Edit: Just discovered I'm on the rare category. 🤯
I have gone through the whole video and am really grateful for the time you've invested in this. The vivid pictures and friendly speaking pace were truly refreshing and helped balance the ubiquity of the text. Cheers from Abidjan!
Ok sidenote, this guy has the most calming voice ever. Like he could talk about literally anything and I'd listen to it
You can say that again!!!
Great course we need its updated version ! Who is with me smash like button
One the most calming voice and tone I've heard. I need this guy in my life for daily calm ! :-))
I did'nt mean to brag but my voice is just like him.. is this a coincidence? because i dont think so
@@hmfet6921🤣
U are not serious 🌚🌚🌚
@@hmfet6921 😂😂😂
Barely 10 minutes in and can already appreciate the time and consideration put into this video. Thanks so much.
Wow am so 😀 glad
Bro gave up 4 years ago lamo
1:05:41 so far it's the best and most clearly explained video on data science I've watched so far. Awesome job.
may i ask u ..because im also confused ..which video to watch .. like ..data science from Edureka,simplilearn,intellipat
I am only 2 hours in but I LOVE the way you demystify what I once thought was so out of my league/ability and perhaps interest as well. Thank you so much!!!
Hey cassandra, was this helpful for beginners? I want to start, and i needed to know whether this is where u started or there is another video i may watch to begin before this
@@trevormaina9093 same question to you
@@trevormaina9093 yesss
There is nothing out of your league , hope things are going well for you ...
I started learning data science today, June 27 2022. This is very helpful. Thnk you
1:30:22
I got through everything and I have to say: thank you very sooo much for all the value you supplied us for free!!! This is just amazing
Quincy if u are reading this......We all campers really appreciate from bottom of our heart for whatever u r doing for us. I just want to say that nobody gives a damn if u start putting ads in between the videos. I have an ad blocker but for the sake of this channel I'll disable it, as that's the only way I can contribute right now and there are many more like me. Start putting ads.
Just finished this 6 hours videos. Thank you for your kind sharing.
Really easy to understand and very useful.
This guy is motivating and teaching you data science in under 6 hours that no one could teach you. 10-12 ads are totally fair, others hire paid teachers and still can't understand a thing. This guy is making many's life. Watching this video before starting and between your data science career, you can be the data scientist that everyone wants. Thank you, Barton Poulson and Freecodechamp for this life-changing course.
Hey 👋.....Can an Msc Biotechnology Graduate learn data science and get certified and can they be hired by companies to become data scientist? Or only computer science background students can get this job??
@@Dariusrae45636You can be a data scientist if you don't even have a strong background from computer science. Even many people who are data scientist now are mostly from other fields, sometook enginnering fields and some took other.
@@epicsizzly thank you so much
@@epicsizzlywhat about business administration
@@chisomprisca3688 Yeah, I've heared that most of the people have been in a relation with business stuff. So yes!
I love that you call this a "movie". Thank you for all your hard work. This is great!
Half way in and this looks more like an overview than a training. Not sure you will learn data science by watching this but you will get the picture of what’s involved.
I'm not gonna lie, there is no chance I'm going to watch all of this, but from what I've seen so far, this is an AMAZING beginners guide to understand every facet of data science. Thanks for this awesome resource. I'm excited to see more resources popping up showcasing more projects and real world experience beginners can learn from
this 6-hour course just explained my whole semester
Intro to data science and ai?
I have it mext sem
@@AryanGupta-xf9lz A+, let's go
This video was perfect. I am speechless. Thank you so much. The narrator is brilliant.
please try to upload some basic projects of how data scientist do their work professionally in the industry it will literally help students to learn in real time.
Yes please. That'll be very useful
A fully-worked example project would be wonderful. Is there a particular kind of project that would be most useful to you? And is there a language that would be best?
@@datalabcc all the jobs that are link to data uses python quite vast so working and practicing every attribute of this language helpful and beneficial for all the novice students for instance we know how to manipulate basic data set with some python commands but we dont know all the working stages of data scientist or data analyst in the real time industry and how a project is to be done and what possible outcomes does the industry make after when a data scientist completed their job on a given data set and what makes them worth to earn thousands of money believe me this gonna work like a big motivation for the students.all in all let me give an example take basic data set like uci-iris and do all the necessary work on it upto maximum extent and what possible results comes out from this especially what we looking in it and why and how to make final report of any particular data set after completing it and what decision could be made when the company manager sees it.
Yes That will be very helpful
Omg! I’m a psychologist that is interested in Data Science too. I thought that I was the only one. Even I’m learning English, in exhausting form, because there’s lack of information in Spanish.
Thanks for the content and all your job in the channel!
I did it , and survive So generous to have made this, Thank you ! the Plus , excellent narrative, but i would suggest to take a real life DATA science project and stick one by one the concepts you have patiently teach for 6 hours . if not the concept are TOO ABSTRACT to be RETAIN. OVERALL , Great but adding a real DATA science project from A to Z would be AWESOME ! Thanks
A fully-worked example project would be wonderful. Is there a particular kind of project that would be most useful to you? And is there a language that would be best?
The best tutorial on Data Sci Intro..hands down! He is a psychologist - he knows how to engage a student. Kudos!
This is a great video! Very well explained concepts. As an established data scientist myself, it helps to have resources like these to brush up on my fundamentals. I also have a few videos on my channel that talk more about the experience working as a data scientist for those that are interested!
A link to your channel please
@@nwaodufranklin5530 You can just click on my picture :)
day 01
59:40 Motivation
34:40 data science != coding
50:55 sourcing data
01:45:10 day 2
This is an absolutely great intro to data science. Sadly most job posts seem to want you to also be a full cycle software developer.
They going to start retiring soon and the crumbling will be crazy
Add prompt engineer title as well with AI
Amazing course! I'm having a turn in my career after 10 years of school teaching, and this course is just what I needed to complement my studies in DS. Just an _errata_ : at 2:13:20 you mentioned "data de novo" as your personal expression for the concept of "new data". I don't know you if it meant for this expression to be extracted from portuguese language or latin, but if you took it from portuguese, the adequate expression should be "Nova Data" or "Novo Dado". In portuguese "de novo" means the same as "again" or to do something one more time.
As a student of political science and international affairs with some background in anthropology and sociology, I was abnormally interested in this area, specially explained in such beautiful manner, but when you added the social sphere everything clicked, and hence why they are considering me for an internship. This would be fun if with a proper team.
Terima kasih diatas penjelasan anda.
Agak rumit juga perkembangan ilmuan kini.
Hello! I just finished the whole video and would like to say thanks! I hope this starts out my journey in learning more about data science. This provided a wide overview of the concepts, tools, and thinking that will be needed in DS. Without being daunting and yet not mind-numbingly dumbed down. Again, thank you!
How is it
How has your learning been thus far?
How is it going??
This Course is made more towards for what the real and practical advice for work that needs to be done in one video, not for actual learning of ML, DL, LLM, CV or really anything specific as a skill not exactly what I was Looking for.
Please keep in mind that this course was originally published in 2016. It seems like it's a wonderful take on the foundations of Data Science, but some of the information may be missing and/or outdated.
⌨ Part 1: Data Science: An Introduction: Foundations of Data Science
- Welcome (1.1)
- Demand for Data Science (2.1)
- The Data Science Venn Diagram (2.2)
- The Data Science Pathway (2.3)
- Roles in Data Science (2.4)
- Teams in Data Science (2.5)
- Big Data (3.1)
- Coding (3.2)
- Statistics (3.3)
- Business Intelligence (3.4)
- Do No Harm (4.1)
- Methods Overview (5.1)
- Sourcing Overview (5.2)
- Coding Overview (5.3)
- Math Overview (5.4)
- Statistics Overview (5.5)
- Machine Learning Overview (5.6)
- Interpretability (6.1)
- Actionable Insights (6.2)
- Presentation Graphics (6.3)
- Reproducible Research (6.4)
- Next Steps (7.1)
⌨ Part 2: Data Sourcing: Foundations of Data Science (1:39:46)
- Welcome (1.1)
- Metrics (2.1)
- Accuracy (2.2)
- Social Context of Measurement (2.3)
- Existing Data (3.1)
- APIs (3.2)
- Scraping (3.3)
- New Data (4.1)
- Interviews (4.2)
- Surveys (4.3)
- Card Sorting (4.4)
- Lab Experiments (4.5)
- A/B Testing (4.6)
- Next Steps (5.1)
⌨ Part 3: Coding (2:32:42)
- Welcome (1.1)
- Spreadsheets (2.1)
- Tableau Public (2.2)
- SPSS (2.3)
- JASP (2.4)
- Other Software (2.5)
- HTML (3.1)
- XML (3.2)
- JSON (3.3)
- R (4.1)
- Python (4.2)
- SQL (4.3)
- C, C++, & Java (4.4)
- Bash (4.5)
- Regex (5.1)
- Next Steps (6.1)
⌨ Part 4: Mathematics (4:01:09)
- Welcome (1.1)
- Elementary Algebra (2.1)
- Linear Algebra (2.2)
- Systems of Linear Equations (2.3)
- Calculus (2.4)
- Calculus & Optimization (2.5)
- Big O (3.1)
- Probability (3.2)
⌨ Part 5: Statistics (4:44:03)
- Welcome (1.1)
- Exploration Overview (2.1)
- Exploratory Graphics (2.2)
- Exploratory Statistics (2.3)
- Descriptive Statistics (2.4)
- Inferential Statistics (3.1)
- Hypothesis Testing (3.2)
- Estimation (3.3)
- Estimators (4.1)
- Measures of Fit (4.2)
- Feature Selection (4.3)
- Problems in Modeling (4.4)
- Model Validation (4.5)
- DIY (4.6)
- Next Step (5.1)
🎯 Key Takeaways for quick navigation:
00:02 Data *Science Creativity*
02:48 Data *Inclusivity Insight*
03:42 Data *Science Demand*
08:07 Data *Science Ingredients*
11:49 Data *Science Pathway*
19:34 Data *Science Roles*
Diverse Data *Science*
Teamwork Makes *Unicorns*
Data Science *vs. BI*
Privacy, Anonymity, *Proprietary*
Copyright, Data *Security*
Potential Bias, *Overconfidence*
01:05:08 Statistical *models utility.*
01:06:01 Machine *learning overview.*
01:09:08 Clear *communication crucial.*
01:14:10 Simplify *presentation graphics.*
01:19:33 Actionable *insights importance.*
Clear, simple *charts*
Storytelling with *data*
Reproducible research
01:46:31 Metrics *& Methods Balance*
01:48:24 Accuracy *Metrics Overview*
01:51:00 Social *Context Awareness*
01:54:14 Data *Sourcing Methods*
02:01:23 Utilizing *APIs in Data Retrieval*
APIs simplify *web data*
Scraping retrieves *web data*
Mind copyright *laws*
Experimental Research *Benefits: Random assignment minimizes confounds.*
Challenges of *Experimentation: Training, time-consuming, expensive.*
A/B Testing *Overview: Compare webpage versions for optimization.*
A/B Testing *Tools: Optimizely, VWO for statistical analysis.*
Data Sourcing: *Explore, consider vendors, create new data.*
Importance of *Spreadsheets: Ubiquitous, versatile, essential for data manipulation.*
Tidy Data *Concept: Structured format crucial for analysis.*
Tableau for *Visualization: Powerful, insightful, available in free version.*
Download Tableau, *Install*
Bring in *Data*
Create Graphs
03:08:10 Collaborative *OSF Analysis*
03:09:09 Diverse *Software Choices*
03:18:43 Web *Data Basics*
Structure Data *with JSON*
R: Language *of Data*
Python: General *Purpose*
SQL: Language *of Databases*
C/C++/Java: Fast, *Reliable*
Bash: Command *Line*
Command line *interaction predates monitors.*
Shells wrap *around computer interaction.*
Bash and *PowerShell are common shells.*
Bash utilities *focus on simplicity.*
Regular expressions *are powerful search tools.*
Mathematics is *vital for data science.*
Algebra is *foundational in data science.*
Linear algebra *is key for manipulating data.*
04:10:26 Matrix *representation explained.*
04:12:14 Linear *algebra benefits.*
04:17:34 Graphical *system solutions.*
04:21:10 Derivative *calculation.*
04:28:14 Maximizing *revenue.*
04:29:59 Optimize *Price Revenue*
04:31:41 Big *O Growth*
04:44:03 Arithmetic *Probability*
04:49:04 Test *result probability: 81.6%*
04:49:57 Positive *test: 32.1%*
04:57:37 Explore *data thoroughly*
05:07:48 Robust *statistics stability*
05:09:10 Resampling *principle explanation*
05:10:06 Transforming *variables concept*
05:26:55 Hypothesis *Testing Basics*
05:28:17 False *Positive Concept*
05:29:13 False *Negative Concept*
05:31:06 Critiques *of Hypothesis Testing*
05:31:55 Hypothesis *Testing Value*
05:32:49 Estimation *Introduction*
05:33:42 Confidence *Intervals Overview*
05:36:03 Accuracy *vs Precision*
05:37:21 Interpreting *Confidence Intervals*
05:40:52 Estimators *Overview*
05:46:08 Measures *of Fit Explanation*
05:47:01 R2: *Measure variance.*
05:47:30 -2 *Log-likelihood: Nested model fit.*
05:47:55 Model *variations: AIC, BIC.*
05:48:24 Chi-squared: *Observed vs. expected.*
05:48:53 Feature *selection: Reduce overfitting.*
05:49:19 Multicollinearity: *Predictor overlap.*
05:50:12 P *values: Individual predictor significance.*
05:50:40 Betas: *Standardized coefficients.*
05:51:10 Newer *methods: Dominance, Commonality, Relative Importance.*
05:51:40 Common *modeling problems: Non-Normality, Non-Linearity, Multicollinearity, Missing Data.*
05:52:09 Dimensionality: *Reducing variables.*
05:52:38 Model *validation: Bayes, Replication, Holdout, Cross-Validation.*
05:53:07 DIY *attitude: Start now.*
05:53:36 Beware *critics: Mistakes happen.*
05:53:56 Data *value: All data matters.*
05:54:05 Continuous *improvement mindset.*
05:54:42 Explore *and analyze.*
05:55:01 Domain *expertise matters.*
05:55:20 Start *now.- **05:54:05** Continuous improvement mindset.*
05:54:05 Additional *conceptual courses.*
05:54:05 Practical *hands-on tutorials.*
05:54:05 "Write *what you know".*
05:54:05 Domain *expertise importance.*
05:54:05 You *don't have to be perfect.*
05:54:05 Just *get started.*
Made with HARPA AI
Starting my journey towards DS. its my first lecture. Thanks for this … i also need volunteer mentor to guide me.
This is an AMAZING COURSE!!!!!!!! WOAH!!! I feel like I gained a TON of value from this!!!
This is Barton Poulson, who created the video. Thanks so much for the kind words; I'm glad it was helpful!
@@datalabcc Thanks for this incredible lecture. I was wondering if you could also let me know where I can get the data sheet (excel) from. I did download a few samples from Kaggle as you suggested, but was hoping to work on your datasheet first and then on the rest. Most of the ones on kaggle do not have a defined datatype in the rows. So it's being a bit tricky there especially the ranged columns.
...got a job from this? No? Who cares.
@@datalabcc Thank you for the awesome content!!
@@clerpington_the_fifth I feel like it is helping me make a good impression on my internship at the moment though
i am in a DS bootcamp working on a project. Still not good coding but I am enjoying the "creative" and free process of resolving problems.
34:45 -" Data Science is not equal to Coding." Can I practice both at the same time. Can I be the master of both at same time.
I know this is a late comment, and I'm not sure how far along in your learning. That being said, my answer to you is this: fortunately, the programming fundamentals between Data Science and Software Engineering are interchangeable. However, it depends on how you want to use those programming languages.
If you prefer building infrastructure and pipelines, full-stack websites, and data environments, software engineering is more for you. In contrast, if you enjoy cleaning and dissecting data, and optimizing company workflow with information being processed across several statistical measures, data science is more for you.
Both are algorithm and math heavy, the deeper you go. From what I've seen/read (as I'm not someone there yet) at the higher levels, data scientists and data engineers choose which they want to prioritize. Both fields are highly attractive, highly sought, and highly necessary.
Over so many instructors, the speed and clarity of this instruction is the best. Some of the professors that teach course over MOOC, they were chasing for the next train. TQ FCC...
Who is watching in 2024 ✌️
I am
me 😅
Mee 😂
Me
ME
A hearty thanks,
❤
I just completed my 10th boards,
And was in search of a best career option for self with all the clarifications ,
And you this video, made me get it perfectly with no doubt remained in me ,
And really the ones who are contributed in makeing this video are psychologists, who actually knows how we (students) do think !
Really meant alot ❤❤❤
One of the best introductions ever in data science and big data! Great job!
A must watch video for anyone who is interested in Data Science.
* Excel is highly used in reality, VBA based on Excel should be considered as a programming language
This is Bart Poulson, who created the video. I spend a truly inordinate amount of time telling all of the data science people I work with that they need to be fluent in spreadsheets, first and foremost. VBA is absolutely a programming language and I apologize if I suggested otherwise. It's a fabulous tool!
This is excellent even for people already working in related fields because it talks about many tools and concepts they may have overlooked. The organization, clarity, and Paulson's breadth of knowledge are impressive!
Like
THANK YOU SO MUCH! I just finished this course and i'm sure going to watch it the second time. alot to still be learnt.
I want to use data science to effectively torture my enemies, thanks for this free courses.
I watched this on 1.75x speed. still good! Thank you!
The best data science class I have seen in RUclips
I'm so glad to hear it!
finished the full 6 hour course. very imformative . i was thinking of learning R for my research analysis. before that i thought i need some foundation on data science and i saw this video. thanks a lote❤
Great course! Content is informative, well structured, and explained clearly. It is evident that Barton Poulson is very knowledgeable and his personality really shows through. His spirit is encouraging and I thoroughly enjoyed his humour. Keep up the inspiring work!
Bro is this beginner Friendly and full course ?
Barton Poulson: best data science educator on the internet!
Hello from Brazil!! I just would like to say THANK YOU so much!! This video help me in many quests that a had about this area! Thank you! :)
you are more than talented.
it takes me two days to complete it. it's so useful to anybody who wants to enter the field.
have you applied for job yet?
I have 101 udemy courses in my account that is half completed lol . I will complete almost all within this year . staying motivated is hard
101😮😮man u seriously are crazy!!
Your videos are a big inspiration! just started out my own youtube (from my experience as a data analyst) All the best!
Call me your 10th subscriber
@@rafaykhan9579 Thanks Rafay!!! Means alot :)
Sir, you are one of the best lecturers whose class I've had the good fortune of stumbling across. Your philosophical backdrops and psychological insights made the experience ever so more pleasant and freshly original.
This is Barton Poulson, who created the video. I'm so glad you enjoyed the course! Thanks for the wonderful feedback.
Thank you ❤. I'm not a techy person. However the way you handle the material is so smooth, so compelling that my fears have disappeared and I'm ready to start learning
i know you dont want to interfere your students with ads when they are watching videos
but dude there is a options in monetization section where you can specify which ads you want to show on your videos leki you can enable sideview ads (ads above the video recommendations).
I love that you are providing all of this stuff without earning a penny. I really want to give you some economic boost but i seriously dont have money.
enable that ads to make more money to produce more quality content like this
Thanks again!!
I’m a financial analyst right now but I already know SQL and Power BI. I’d like to learn Python and R so I can move to a role where I can combine my existing finance knowledge with data science
That sounds cool
Good luck 🍀
Anyone who wants to enter in Data Science field, needs to watch the first 25 minutes to better understand the possibilities.
Hai sir ....your work in this data science field ......if you know can you explain please.... I am willing to start my new career's in data science field
OMG JASP and OSF is amazing!!! Thank you Bart!!!
Best video ever for 360 degree understanding of the field of data science!! I’ve only watched 20-odd minutes, but already I have so much clarity. Plan to do the entire series. Thank you!
Love your voice I could hear for the next 6 hours without any distraction... lot of misunderstandings N abilities many more were covered with ease... completed in two days the course... As you said that u were from psychology did very well in implementing things on very one cud be part of data science.. so much encouragement... Love your work N following you... All the best for your future N i update my Data science enhancement from being a beginner..
Now that's a voice I could hear for the next 6 hours :P just kidding I'll watch this over the next week.
Hi, In Part 3, Coding, Prof Poulson refers to downloading spreadsheets and other downloadable data to test with tableau, please can you give me the url so I can download them?
He sounds a bit like Neil DeGrasse Tyson
instead of a Indian person from India talking for 6 hours, sorry i couldn't be able to handle it
Me who watched the entire session with x1.5 speed 😶
Great content, your voice is soo soothing and interesting to listen to
I spent over 10 years in marketing (Web analytics, A/B testing, Conversion Rate Optimization, online ads etc. - so basically I always was on this technical/business side of marketing) and 1.5 years with Python (web scrapping, automation) but I always sucked at math. Big time. Data Science and ML are extremely interesting for me, I can see many applications for my day-to-day work in marketing but I'm scared shitless of this math part :)
Michael, this is Bart Poulson, who created this course. It sounds to me like you already have most of what you need and, unless you're going into algorithm development, huge amounts of math may not be necessary. I imagine that your familiarity with analytics and experience with automation already does a lot for you. But what specific plans do you have?
Watch it in 1.5 speed but with breaks in between. Easy to digest and fast.
I hope you all land the job you are looking to apply for, good luck!
Thanks to great people behind this God Bless you guys!
Binge watching this better than Netflix
I like how much material is looked over before even coding is even introduced, a good insight into the applications of data science.
This is the most clarifying video about data science. Thank you so much!
This is great as a "from couch to data science" but also need Data Science in 2 hours
Thank you all for an amazing video.
Could you please upload a video how we can implement data science on real life data. Like using python or R programming with SQL and these concepts.
thanks again.
Just WOW! This is university level material. Impressive tutorial.
Thanks so much to everyone doing this amazing job. Hoping to contribute freecodecamp in near future. THANKS AND ALL HAIL DATA SCIENCE.
Just completed the whole video. I’m glad I did.
4.3k views, 432 likes and not a single dislike! Not something I have seen before.
1:37:16 Data sourcing
1:56:44 Open data
First off, thank you for this course. This was a huge eye opener for me about all that goes into data science. They say you don’t know what you don’t know and I realize now that I don’t know much at all. I was really enjoying using SQL and Python to pull data down and “play” with it but after watching this entire video I think I need to find something else to do. This course was so incredibly dense with theories, concepts and terminology that learning this just feels impossible to me. Maybe if I had taken collage level algebra, statistics and calculus this may have been a great refresher but without that prior knowledge, this just seems unattainable. I thought that data annalist roles are less intense than this but now I'm just not sure about anything. Regardless of my ability to understand this, the video is amazing and incredibly well done. I’m sure it will help a lot of people getting into data science.
Drew, this is Bart Poulson, who created the video. It sounds to me like you're doing exactly the right thing. The math background is nice but far from essential. Curiosity about data, along with some facility with tools like SQL and Python, all of which you have, are an excellent foundation.
I think I couldn't have found a better way of discovering the field of Data science... What an incredible video, thanks sooo much!
The data scientists in the Intergalactic Imperial Data Center should watch this tutorial for the next Death Star project plan.
Best 6-hour speech I ever heard.
thank you so much for creating these free, in depth courses. You really deserve to put ads in here :)
In my opinion, you could be successfull data scientist just with Python, no R, c++, HTMl etc are required
Can I become data scientist without Python?
@@uizzximoti5734 yes, you could. For example, you could train neural networks on pure c++ or java/javascript. You could use ML models from R and do the whole analytics just with R.
Why only 1 million subs for such an awesome channel??
because it does not have subtitles in Spanish :(
@@nicolasramirez865 RUclips won't auto-subtitle any video over 4 hours, unfortunately. But we would welcome your help adding Spanish subtitles. There are several ways you can make the freeCodeCamp community's videos more accessible for Spanish-speakers: contribute.freecodecamp.org
Adding chapter is important,
This seems kinda motivation video,
I'm looking for actionable tutorials.