Data Science Topics and Algorithms

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来自 IBM 的课程

What is Data Science?

116 个评分

IBM

What is Data Science?

116 个评分

课程 1（共 4 门，Specialization Introduction to Data Science）

The art of uncovering the insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and they accurately predicted the flooding of the Nile river every year. Since then, people working in data science have carved out a unique and distinct field for the work they do. This field is data science. In this course, we will meet some data science practitioners and we will get an overview of what data science is today.

从本节课中

Defining Data Science and What Data Scientists Do

In this module, you will go over the course syllabus to learn what will be taught in this course. Also, you will hear from data science professionals to learn what data science is, what data scientists do, and what tools and algorithms data scientists use on a daily basis. Finally, you will be required to complete a reading assignment to learn why data science is considered the sexiest job in the 21st century.

A day in the Life of a Data Scientist3:53

Data Science Topics and Algorithms3:47

What is the cloud?3:15

与讲师见面

Alex Aklson
Ph.D., Data Scientist
IBM Cognitive Class

I really enjoy regression I'd say regression was maybe one of the

first concepts that I that really helped me understand data so I enjoy a

regression. I really like data visualization I think it's a key element

for people to get across their message to people that don't understand that

well what data science is. Artificial neural networks. I'm really passionate

about neural networks because we have a lot to learn with nature so when we are

trying to mimic our our brain I think that we can do some applications with

this behavior with this biological behavior in algorithms. Data

visualization with R I love to do this. Nearest neighbor. It's the simplest but

it just gets the best results so many more times than some overblown

overworked algorithm that's just as likely to overfit as it is to make a

good fit. So structured data is more like tabular data things that you're familiar

with in Microsoft Excel format you've got rows and columns and that's called

structured data. Unstructured data is basically data that is coming from

mostly from web where it's not tabular it is not it's not in rows and columns

it's text it's sometimes it's video and audio so you would have to deploy more

sophisticated algorithms to extract data and in fact a lot of times we take

unstructured data and spend a great deal of time and effort to get some structure

out of it and then analyze it. So if you have something which fits nicely into

tables and columns and rows go ahead that's your structured data but if

you see if it's a weblog or if you're trying to get information out of

webpages and you've got a gazillion web pages

that's unstructured data that would require a little bit more effort to get

information out of it. Let me explain regression in the simplest possible

terms. If you have ever taken a cab ride a taxi ride you understand regression.

Here's how it works. The moment you sit in a cab ride in a cab you see that

there's a fixed amount there it's is $2.50 you rather the cab moves or you

get off this is what you owe to the driver the moment you step into a cab

that's a constant you have to pay that amount if you have stepped into a cab.

Then as it starts moving for every meter or hundred meters the fare increases by

certain amount so there's a there's a fraction there's a relationship between

distance and the amount you would pay above and beyond that constant. And if

you're not moving and you're stuck in traffic then every additional minute you

have to pay more so as the minutes increase your fare increases as the

distance increases your fare increases and while all this is happening you've

already paid a base fare which is the constant this is what regression is

regression tells you what the base fare is and what is the relationship between

time and the fare you have paid and the distance you have traveled and the fear

you've paid because in the absence of knowing those relationships and just

knowing how much people traveled for and how much they paid regression allows you

to compute that constant that you didn't know it was 2.50 and it would compute the

relationship between the fare and and the distance and the fare and the time.

That is regression.

you

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