Data science is a very popular term these days and it gets applied to so many things that its meanings have become very vague. So I would like to start this lecture by giving you the definition that I use. I have found that this one gets right to the heart of what sets it apart from other disciplines. Here goes.
Data science means doing analytics work that for one reason or another requires a substantial amount of software engineering skaters sometimes the final deliverable is that kind of thing a statistical or b genius and at least it might provide. But achieving that goal demands up dirty skills that your typical analyst simply doesn't have. For example, our data set might be so large that you need to use distributed computing to analyze it or show up on video. Limited in its format that many lines of code are required to pass it.
In many cases that scientists also have to write big chunks of products and shoppers that implement their analytics ideas in real-time. In practice, there are usually other differences as well. For example data, scientists usually have to extract features from raw data which means that they tackle very open-ended problems such as how to quantify the spam meanness of an email it's very hard to find people who can construct well in statistical models. While Wiley disruptor and relate this all in a meaningful way to be genius problems. It's a lot of heads to where these individual visuals are so rare that recruiters often call them unicorns.
The message of this lecture is that it is not only possible but also relatively straightforward to become a unicorn. It's just a question of acquiring the particular balance of skills required very few educational programs take all teach all of the skills which is why unicorns are rare. In part that's mostly a historical accident. It is perfectly reasonable for a single person to have the whole palette of abilities provided they are willing to ignore that traditional boundary between different disciplines this lecture aims to teach you everything you will need to know to be a computer and data scientist.
I guess that you are either a computer programmer looking to learn about analytics or more of a mathematician trying to bone up on their coding. You might also be a business person who needs the technical skills to answer your business question or simply an interested layman whoever you are for this lecture will teach you the concepts you need this lecture is not comprehensive the science is to be an area for any persons to cover all of it and besides the field is changing so fast that any comprehensive lecture would be out of date before it came of deep process. Instead, I have aimed for two goals.
First I want to give a solid grounding in the big picture of what data science is how to go about doing it and the foundational concepts that will stand the test of time.
Second I want to give a complete skill set in the sense that you have the nuts and bolts knowledge to go out and do the science who are you can put in Python. You know the library is to use most of the big machine learning modules EDC even if particular projects are companies might require that you pick up a new skillset from somewhere else. Ardent data scientist just over raid is statisticians Nate Silver a statistician famous for accurate forecasting of US elections once famously said I think that US scientist e.g. 6 to apt term 40 statisticians he has a point but what he said is only partly true that discipline up statistics deals mostly with the rigorous mathematical methods for solving well-defined problems.
Data Scientists spend most of their time getting data into a form where statistical methods could even be applied. This involves making sure that the analytics problem is a good match to business objectives extracting meaning full features from the raw data and coping with any pathologies of the data or word edge cases.
#machine_learning_algorithms #machine_learning #machine_learning_course #machine_learning_projects #machine_learning_jobs #machine_learning_applications #data_science
Data science means doing analytics work that for one reason or another requires a substantial amount of software engineering skaters sometimes the final deliverable is that kind of thing a statistical or b genius and at least it might provide. But achieving that goal demands up dirty skills that your typical analyst simply doesn't have. For example, our data set might be so large that you need to use distributed computing to analyze it or show up on video. Limited in its format that many lines of code are required to pass it.
In many cases that scientists also have to write big chunks of products and shoppers that implement their analytics ideas in real-time. In practice, there are usually other differences as well. For example data, scientists usually have to extract features from raw data which means that they tackle very open-ended problems such as how to quantify the spam meanness of an email it's very hard to find people who can construct well in statistical models. While Wiley disruptor and relate this all in a meaningful way to be genius problems. It's a lot of heads to where these individual visuals are so rare that recruiters often call them unicorns.
The message of this lecture is that it is not only possible but also relatively straightforward to become a unicorn. It's just a question of acquiring the particular balance of skills required very few educational programs take all teach all of the skills which is why unicorns are rare. In part that's mostly a historical accident. It is perfectly reasonable for a single person to have the whole palette of abilities provided they are willing to ignore that traditional boundary between different disciplines this lecture aims to teach you everything you will need to know to be a computer and data scientist.
I guess that you are either a computer programmer looking to learn about analytics or more of a mathematician trying to bone up on their coding. You might also be a business person who needs the technical skills to answer your business question or simply an interested layman whoever you are for this lecture will teach you the concepts you need this lecture is not comprehensive the science is to be an area for any persons to cover all of it and besides the field is changing so fast that any comprehensive lecture would be out of date before it came of deep process. Instead, I have aimed for two goals.
First I want to give a solid grounding in the big picture of what data science is how to go about doing it and the foundational concepts that will stand the test of time.
Second I want to give a complete skill set in the sense that you have the nuts and bolts knowledge to go out and do the science who are you can put in Python. You know the library is to use most of the big machine learning modules EDC even if particular projects are companies might require that you pick up a new skillset from somewhere else. Ardent data scientist just over raid is statisticians Nate Silver a statistician famous for accurate forecasting of US elections once famously said I think that US scientist e.g. 6 to apt term 40 statisticians he has a point but what he said is only partly true that discipline up statistics deals mostly with the rigorous mathematical methods for solving well-defined problems.
Data Scientists spend most of their time getting data into a form where statistical methods could even be applied. This involves making sure that the analytics problem is a good match to business objectives extracting meaning full features from the raw data and coping with any pathologies of the data or word edge cases.
#machine_learning_algorithms #machine_learning #machine_learning_course #machine_learning_projects #machine_learning_jobs #machine_learning_applications #data_science
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