1
00:00:00,000 --> 00:00:05,561
Welcome to this course introduction on
Coursera on Computational Methods for Data

2
00:00:05,561 --> 00:00:11,123
Analysis. My name is Nathan Kutz. I am a
professor and chair of applied mathematics

3
00:00:11,123 --> 00:00:16,753
at the University of Washington. I'd like
to kind of briefly introduce the ideas of

4
00:00:16,753 --> 00:00:22,315
what I want to cover in this course so
that you would have a better idea of, of

5
00:00:22,315 --> 00:00:27,673
how to think about this course. Data, of
course, has become ubiquitous in all the

6
00:00:27,673 --> 00:00:33,184
sciences, as well as things that are not
necessarily considered sciences, such as

7
00:00:33,184 --> 00:00:38,516
computer science where you're thinking
about data mining applications. But in

8
00:00:38,516 --> 00:00:43,917
either case, data is driving revolutions
in how we think about managing and

9
00:00:43,917 --> 00:00:49,873
analyzing both complex dynamical systems
as well as things that are purely data

10
00:00:49,873 --> 00:00:55,689
driven, such as market place factors as
well as thinking about mining for data

11
00:00:55,689 --> 00:01:01,437
bases for important information. This
course has a very specific aim which is to

12
00:01:01,437 --> 00:01:10,700
integrate many aspects of data analysis in
a very broad sense. In the set we're going

13
00:01:10,345 --> 00:01:14,787
to start off with, in fact with signal
processing and dynamics of signal

14
00:01:14,787 --> 00:01:19,526
processing. We're gonna look at thinking
about statistical methods, bringing them

15
00:01:19,526 --> 00:01:24,028
into these ideas of how to think about
time and frequency analysis, and building

16
00:01:24,028 --> 00:01:28,530
this forward towards understanding things
like dimensionality reduction, and

17
00:01:28,530 --> 00:01:32,854
principal component analysis. Also
included in this is going to be some kind

18
00:01:32,854 --> 00:01:36,763
of idea of thinking about machine
learning. How do you think about

19
00:01:36,763 --> 00:01:41,442
integrating dynamics? How do you integrate
your data collection into teaching a

20
00:01:41,442 --> 00:01:46,090
computer to see bigger trends in your
data? And in constructing appropriate

21
00:01:46,090 --> 00:01:51,210
models that might be able to help you
predict future states of your system of

22
00:01:51,210 --> 00:01:56,593
interest. This course is an upper division
undergraduate, beginning graduate course,

23
00:01:56,593 --> 00:02:01,129
so it's fairly high-level. It's expected
that you'd have an amazing intro,

24
00:02:01,129 --> 00:02:05,523
background in linear algebra. Well,
amazing may be a little oversh, pushing

25
00:02:05,523 --> 00:02:10,820
but you should have a very good background
in linear algebra. You should have a solid

26
00:02:10,820 --> 00:02:15,636
background in some computing techniques.
here, the course wi ll be mostly based

27
00:02:15,636 --> 00:02:19,850
upon Matlab. It's a very fun course,
there's lots of great and relevant

28
00:02:19,850 --> 00:02:24,907
examples. it will also make you work very
hard. so have no illusions about this

29
00:02:24,907 --> 00:02:29,906
being easy. It is a very difficult course,
but at the end, you'll learn some of those

30
00:02:29,906 --> 00:02:34,664
cutting edge techniques being used today
in both thinking about data mining

31
00:02:34,664 --> 00:02:39,806
applications, but also how you apply this
to applications where you might have an

32
00:02:39,806 --> 00:02:45,303
underlying physical system of interest and
you want to use the data from that system

33
00:02:45,303 --> 00:02:50,930
to help you be able to say something very
concrete about your system of interest by

34
00:02:50,930 --> 00:02:55,911
using data from that system directly in
meaningful ways. I hope you enjoy the

35
00:02:55,911 --> 00:02:57,140
course, and welcome.