Teach a man to fish, instead of giving him a fish. I came here for the 10 minute overview...the rest of my time will be split between digging into the manual and writing code. But, I guess you're right: some people want to be spoon-fed...
Honestly I kind of like writing the code that cleans my data. It's very satisfying when I can build a nice sklearn pipeline that does everything I need. Sometimes that's not possible though. Or I'm not good enough.
I've been using Pandas, Dask, SQL, and Scala for all sorts of data processing. Enjoyed the video cause I loved the relaxed and conversational manner in which you presented this tutorial. And I saw tea and nice lighting in the autoplay. Good stuff.
I have a suggestion - make a video describing how to use the documentation efficiently. I am a relatively new programmer, and it's a struggle for me to understand how to use methods and whatnot properly. An example would be the read_csv method and its associated documentation, shown at 8:04 in the video. Surely there must be a method to understand methods?
Very useful with the emphasis on documentation, thanks. Many instructions are far too action oriented, which triggers bad learning habits. Better read, think and do instead of beginning with doing - it saves you lots of time. (Also it saves lots of text book costs!)
Hello Sir, I am willing to become a data scientist and I am thinking of getting enrolled into one of the courses from different learning website such as Coursera,edx etc. I found Dataquest more interesting as compared to other side may I know is Dataquest is worth and if I follow data quest( data science) track where I will reach by end of the course ?
Thanks for this.. iloc was something I just couldn't figure out last night.. life easier again... and yea read documentation seems to ge a great idea :D
I think that it's good for everyone to know at least the basics of data science. The same way that learning the fundamentals of language, math, physics, biology, history, philosophy and so on can help you better understand the world and its people 😊, learning the fundamentals of coding and data science can help you understand the logic behind using computers and machine learning. Contrary to what many people think, I don't believe AI and automation will make us jobless. I think we'll have different jobs and data science will be one of the skills needed to do those jobs well. Plus, data science is fascinating. You could learn it just for the fun of it. 😊
@@miguelcastillo1742 Do you mean resources for learning data science? If yes, Siraj Raval has a RU-vid video called "Learn data science in three months." In its description you'll find links to a good, doable plan for learning the basics of data science. It's a good start. 😊
@@BiancaAguglia I'm definitely learning it for fun. Currently working on a masters in computation analytics and love it. Just a little concerned about recent articles stating there's a glut of applicants. You're correct though these skills will still be valuable no matter what.
@@kevinayers7144 It can't be true because data scientist is the number 2 in demand/paying job for 2018, just after ML engineer. If 4th industrial revolution is real every single business has to learn DS and ML or be swept away by automated competition.
I am not an expert, but let me explain the little I know. ML is data science automated. When a company builds a ML product they automate what the data scientists have already done. So the the real design of the ML intelligence comes from the data scientist's work. The ML expert's job is automate that just like a programmer automates a spreadsheet that an accountant is preparing each month manually. At the end of the day a commercial ML product like Alexa has 3000 people working on it, and the MLE, DSE and SE designations start to blur fast into hundreds of different sub tasks. So it depends on where you want to get to. Entry level ML assistant in a team of 20 - basics only. Head engineer of ML at Amazon - super advanced.
I paused this video to comment: Please stop with inserts of your hands on keyboard or a shot over the shoulder when you are talking business. I personally find it distracting. Of course just my opinion. Thank you for making so many useful videos, always appreciated.
Hi, Is it possible to convert below the input Customer_Name,Product_1,Price_1,Product_2,Price_2 Zayn,Milk,30,Chocolate,40 Peter,Cheese,190,Oil,80 Andrew,Coconut,10,Milk,60 Dwayne,Soya,100,Butter,120 to this output where the Product name should be ascending and it should also have its price in the next column Customer_Name,Product_1,Price_1,Product_2,Price_2 Zayn,Chocolate,40,Milk,30 Peter,Cheese,190,Oil,80 Andrew,Coconut,10,Milk,60 Dwayne,Butter,120,Soya,100 Can you please help
What a refreshingly good video! Was just at the right level, without irrelevant self-important waffle - or the almost omnipotent self-promoting a-la "Please smash the like button, hit the notification bell, subscribe for more content, it really helps my channel / it helps me able to create more videos [...]". IMHO an excellent 10 min intro, which has made me more excited about working with PANDAS.
Question. After I've downloaded the panda feature using " Anaconda", I want to uplaed CSV file to the pyton-panda program and can't. where is my problam? the CSV file should be under the same windows folder? and if so, what is this windows folder name?
Can you please share a link where you have done these functions: .loc,.iloc,.drop,.append,.groupby? I am struggling a bit with the syntax and use of arguements within those brackets. Thanks in advance :)
Hello! I am a beginner in data scientist so please tell me about the resource where I can learn eda to produce insights on the variables to prepare my data well.
great video! 30,000 foot overview with dips into code for the most popular features. can we get links to those articles you mentioned in the beginning?
Joseph A , as with any question in such a broad field, the answer is “it depends”. Most likely, you will not use ordinary or partial differential equations in data science. However, if you are working with physical data, then you may. For instance, in pharmacology, having those tools as a basis for models is important. That said, there are far more applications of data science in which knowledge of systems of differential equations is not necessary. If you’re interested, One Blue Three Brown has a series of very intuitive videos on differential equations as well as vector algebra.
Alright good explanation, but you need to stop cutting what you are doing for useless parts like filming your keyboard. Yes it keeps people's attention up, but it also makes us frustrated when we are trying to read the code on the page and understanding it as you go. Instead we have a look on your hands doing not much on the keyboard and it's just frustrating. Instead you should use parts where you are talking in front of the camera if you really want to make those cuts.
