1
00:00:07,300 --> 00:00:10,800
- Welcome to Python
Fundamentals Part 1 of 5.

2
00:00:10,800 --> 00:00:13,240
My name is Paul Deitel and
I'll be your instructor

3
00:00:13,240 --> 00:00:15,230
for these LiveLessons videos.

4
00:00:15,230 --> 00:00:17,940
I'm the CEO of Deitel & Associates, Inc.,

5
00:00:17,940 --> 00:00:21,450
an MIT grad with 38 years
of experience in computing,

6
00:00:21,450 --> 00:00:23,690
and also one of the
world's most experienced

7
00:00:23,690 --> 00:00:25,480
programming languages trainers,

8
00:00:25,480 --> 00:00:29,173
having been teaching
programming training since 1992.

9
00:00:30,320 --> 00:00:31,790
Throughout these LiveLessons videos,

10
00:00:31,790 --> 00:00:33,810
you're going to be working hands-on

11
00:00:33,810 --> 00:00:36,290
with the Python programming language.

12
00:00:36,290 --> 00:00:37,430
This is now one of world's

13
00:00:37,430 --> 00:00:39,550
most popular programming languages

14
00:00:39,550 --> 00:00:42,163
and it's also the fastest
growing among them.

15
00:00:43,320 --> 00:00:45,210
Professionals often discover quickly

16
00:00:45,210 --> 00:00:48,400
that they really like Python
for a number of reasons.

17
00:00:48,400 --> 00:00:50,530
It's a general purpose
programming language

18
00:00:50,530 --> 00:00:53,030
so you can use it for
lots of different tasks.

19
00:00:53,030 --> 00:00:56,170
It's concise and extremely expressive

20
00:00:56,170 --> 00:01:00,170
so you can really write a
lot of complex applications

21
00:01:00,170 --> 00:01:03,270
with relatively minimal amounts of code.

22
00:01:03,270 --> 00:01:06,440
It's very readable so it's easy to learn.

23
00:01:06,440 --> 00:01:09,820
It's interactive because
there's an interactive mode

24
00:01:09,820 --> 00:01:12,510
in the IPython interpreter
that we'll be using

25
00:01:12,510 --> 00:01:13,940
so you can go right down

26
00:01:13,940 --> 00:01:16,630
to the individual
programming snippet level

27
00:01:16,630 --> 00:01:19,050
and see exactly what that statement

28
00:01:19,050 --> 00:01:22,490
or snippet is going to do immediately.

29
00:01:22,490 --> 00:01:24,830
There's a massive open source community

30
00:01:24,830 --> 00:01:26,280
surrounding Python as well,

31
00:01:26,280 --> 00:01:28,930
developing incredibly powerful libraries

32
00:01:28,930 --> 00:01:30,550
that you're going to take advantage of

33
00:01:30,550 --> 00:01:32,760
over the five parts of these LiveLessons.

34
00:01:32,760 --> 00:01:35,470
And the job market, in
particular for Python,

35
00:01:35,470 --> 00:01:37,420
is extremely strong right now,

36
00:01:37,420 --> 00:01:39,710
especially for anybody

37
00:01:39,710 --> 00:01:42,880
who has any kind of data science skills

38
00:01:42,880 --> 00:01:45,313
and artificial intelligence
skills as well.

39
00:01:46,360 --> 00:01:49,570
Part one of these Python
Fundamentals LiveLessons videos

40
00:01:49,570 --> 00:01:52,030
is based on the Before You Begin section

41
00:01:52,030 --> 00:01:53,630
and chapters one through four

42
00:01:53,630 --> 00:01:56,317
of our recently published Python books,

43
00:01:56,317 --> 00:01:59,497
"Intro to Python for Computer
Science and Data Science:

44
00:01:59,497 --> 00:02:02,880
"Learning to Program with
AI, Big Data and the Cloud,"

45
00:02:02,880 --> 00:02:06,107
that's our textbook, and
also our professional book,

46
00:02:06,107 --> 00:02:07,937
"Python for Programmers."

47
00:02:09,070 --> 00:02:10,100
Many people find that,

48
00:02:10,100 --> 00:02:12,020
as they work their way through the videos,

49
00:02:12,020 --> 00:02:15,160
it's helpful to have a
copy of the book at hand.

50
00:02:15,160 --> 00:02:17,670
However, this is not required.

51
00:02:17,670 --> 00:02:19,960
There is additional
information in the books

52
00:02:19,960 --> 00:02:22,780
that I don't cover as
part of these videos.

53
00:02:22,780 --> 00:02:25,600
If you're interested in getting
a copy of one of the books,

54
00:02:25,600 --> 00:02:28,980
you can find them in various
print and electronic formats

55
00:02:28,980 --> 00:02:31,123
at the sites that are listed for you.

56
00:02:32,720 --> 00:02:34,090
If you're a college instructor,

57
00:02:34,090 --> 00:02:35,760
you'll probably want to take a look

58
00:02:35,760 --> 00:02:39,000
at "Intro to Python for Computer
Science and Data Science,"

59
00:02:39,000 --> 00:02:42,630
which is our college
textbook version of the book.

60
00:02:42,630 --> 00:02:45,880
This one is in full color, it's 880 pages,

61
00:02:45,880 --> 00:02:49,210
240 pages more than
"Python for Programmers,"

62
00:02:49,210 --> 00:02:52,950
and in particular, it
contains a ton of exercises,

63
00:02:52,950 --> 00:02:57,950
557 self-check exercises
as well as 471 additional

64
00:02:58,390 --> 00:03:01,760
end of chapter exercises and projects.

65
00:03:01,760 --> 00:03:03,120
Now many professionals find

66
00:03:03,120 --> 00:03:05,350
that they like to work
with the textbook version

67
00:03:05,350 --> 00:03:08,373
specifically because of the exercises.

68
00:03:09,980 --> 00:03:11,897
If you're interested
in learning more about

69
00:03:11,897 --> 00:03:15,070
"Intro to Python for Computer
Science and Data Science,"

70
00:03:15,070 --> 00:03:18,100
please take a look at the links
that I have provided below.

71
00:03:18,100 --> 00:03:20,740
The first one is for an
architectural diagram

72
00:03:20,740 --> 00:03:24,180
in which we show you the
four-part modular structure

73
00:03:24,180 --> 00:03:26,200
of this book and that really applies

74
00:03:26,200 --> 00:03:29,497
both to the college textbook
and our professional book,

75
00:03:29,497 --> 00:03:31,850
"Python for Programmers," as well.

76
00:03:31,850 --> 00:03:35,560
The for programmers book
has one fewer chapter,

77
00:03:35,560 --> 00:03:38,650
but the overall architecture
of the two books is the same.

78
00:03:38,650 --> 00:03:40,410
And also, in the for programmers book,

79
00:03:40,410 --> 00:03:43,810
we have less of the lower
end pedagogical material

80
00:03:43,810 --> 00:03:45,520
that's really geared to people

81
00:03:45,520 --> 00:03:47,920
who are novices in programming.

82
00:03:47,920 --> 00:03:50,240
The second link is for
the full table of contents

83
00:03:50,240 --> 00:03:52,250
where you can see everything
that we're going to cover

84
00:03:52,250 --> 00:03:53,170
throughout the book.

85
00:03:53,170 --> 00:03:55,260
And the third one is the book's preface

86
00:03:55,260 --> 00:03:57,570
where you can learn
more about our approach

87
00:03:57,570 --> 00:04:01,120
to teaching Python, data
science, artificial intelligence,

88
00:04:01,120 --> 00:04:03,073
and various big data topics.

89
00:04:04,340 --> 00:04:06,230
I'd also like to recommend
that you take a look

90
00:04:06,230 --> 00:04:08,320
at these two additional links.

91
00:04:08,320 --> 00:04:11,940
The first one is for the full
book cover, and in particular,

92
00:04:11,940 --> 00:04:14,450
if you look at the back cover copy,

93
00:04:14,450 --> 00:04:17,010
you'll see a nice summary of everything

94
00:04:17,010 --> 00:04:19,560
that we're going to do throughout
these LiveLessons videos.

95
00:04:19,560 --> 00:04:21,890
And I'd also recommend
that you take a moment

96
00:04:21,890 --> 00:04:24,210
to read through the technical

97
00:04:24,210 --> 00:04:26,970
and academic reviewer testimonials

98
00:04:26,970 --> 00:04:29,700
to really get a sense of all
the things that they liked

99
00:04:29,700 --> 00:04:33,590
about how we presented Python
and the various data science,

100
00:04:33,590 --> 00:04:35,653
AI, and big data topics.

101
00:04:37,790 --> 00:04:40,490
Next, let's talk about
the modular architecture

102
00:04:40,490 --> 00:04:44,430
of these videos, which also
applies to the books as well.

103
00:04:44,430 --> 00:04:46,390
In lessons one through 10 of these videos,

104
00:04:46,390 --> 00:04:50,160
we'll be focused on Python
programming fundamentals,

105
00:04:50,160 --> 00:04:52,560
and throughout those lessons we have,

106
00:04:52,560 --> 00:04:53,800
at the end of each one,

107
00:04:53,800 --> 00:04:56,430
an introduction to data science

108
00:04:56,430 --> 00:04:58,820
in which we're going to introduce things

109
00:04:58,820 --> 00:05:03,270
like artificial intelligence,
basic descriptive statistics,

110
00:05:03,270 --> 00:05:06,490
measures of central
tendency and dispersion,

111
00:05:06,490 --> 00:05:10,920
simulation techniques,
working with CSV files.

112
00:05:10,920 --> 00:05:13,180
We're going to introduce
a really powerful library

113
00:05:13,180 --> 00:05:15,560
called pandas which you'll use extensively

114
00:05:15,560 --> 00:05:18,420
throughout the rest of the
lessons once we introduce it.

115
00:05:18,420 --> 00:05:21,330
And we're also going to start
to take a look at the concepts

116
00:05:21,330 --> 00:05:24,780
of time series and
simple linear regression.

117
00:05:24,780 --> 00:05:26,430
All of these topics are meant

118
00:05:26,430 --> 00:05:29,290
to help support the case study lessons

119
00:05:29,290 --> 00:05:34,290
that we'll be looking at
in lessons 11 through 16.

120
00:05:34,410 --> 00:05:37,300
As you work through the
high-end case study lessons

121
00:05:37,300 --> 00:05:38,780
in parts four and five

122
00:05:38,780 --> 00:05:41,730
of these Python Fundamentals
LiveLessons videos,

123
00:05:41,730 --> 00:05:43,710
one of the things I'll be doing with you

124
00:05:43,710 --> 00:05:47,690
is introducing a lot of
real-world data sets.

125
00:05:47,690 --> 00:05:50,680
Many of the libraries I'll
present to you come bundled

126
00:05:50,680 --> 00:05:53,050
with data sets that you can use to learn

127
00:05:53,050 --> 00:05:54,790
about working with those libraries

128
00:05:54,790 --> 00:05:57,570
and learn various AI, data science,

129
00:05:57,570 --> 00:05:59,740
and big data topics as well.

130
00:05:59,740 --> 00:06:01,300
And in addition you're going to find

131
00:06:01,300 --> 00:06:04,980
that you can acquire data sets
from a wide range of sources.

132
00:06:04,980 --> 00:06:08,460
There are all sorts of data
set repositories out there,

133
00:06:08,460 --> 00:06:11,410
various governments
provide big data data sets

134
00:06:11,410 --> 00:06:14,830
that you can take advantage of
including the US government,

135
00:06:14,830 --> 00:06:18,480
and you'll also find that
there's many additional data sets

136
00:06:18,480 --> 00:06:20,510
bundled with these libraries.

137
00:06:20,510 --> 00:06:23,930
And some of the libraries
also give you convenient ways

138
00:06:23,930 --> 00:06:27,970
to load data sets from those
massive data set repositories

139
00:06:27,970 --> 00:06:29,800
that are available to you.

140
00:06:29,800 --> 00:06:31,620
Now, after you've worked your way

141
00:06:31,620 --> 00:06:33,330
through lessons one through five

142
00:06:33,330 --> 00:06:36,110
and some parts of lessons six and seven,

143
00:06:36,110 --> 00:06:37,860
you'll actually be able to go ahead

144
00:06:37,860 --> 00:06:40,440
and attack the really cool concepts

145
00:06:40,440 --> 00:06:42,300
that we're going to be presenting to you

146
00:06:42,300 --> 00:06:45,230
in lessons 11 through 16.

147
00:06:45,230 --> 00:06:47,390
But I do encourage you not to skip

148
00:06:47,390 --> 00:06:49,430
any of the lessons along the way.

149
00:06:49,430 --> 00:06:51,640
There are lots of interesting topics

150
00:06:51,640 --> 00:06:54,400
in lessons eight, nine, and 10,

151
00:06:54,400 --> 00:06:56,980
including all of Python's object-oriented

152
00:06:56,980 --> 00:06:58,863
programming concepts as well.

153
00:07:00,640 --> 00:07:02,140
As we developed the content

154
00:07:02,140 --> 00:07:04,800
on which these LiveLessons
videos are based,

155
00:07:04,800 --> 00:07:07,110
we kept three principles in mind.

156
00:07:07,110 --> 00:07:10,760
Keep it simple, keep it
small, and keep it topical.

157
00:07:10,760 --> 00:07:12,340
With respect to keep it simple,

158
00:07:12,340 --> 00:07:14,140
one of the things we really wanted to do

159
00:07:14,140 --> 00:07:16,150
was make it super easy for people

160
00:07:16,150 --> 00:07:19,040
to get into the Python
programming language.

161
00:07:19,040 --> 00:07:21,450
So we really strived for simplicity

162
00:07:21,450 --> 00:07:23,620
and clarity in our writing

163
00:07:23,620 --> 00:07:26,420
and in the way that we
presented the material.

164
00:07:26,420 --> 00:07:29,760
We also preferred the simpler libraries

165
00:07:29,760 --> 00:07:31,470
that are available to us.

166
00:07:31,470 --> 00:07:33,220
So one of the things
that you're going to get

167
00:07:33,220 --> 00:07:35,960
out of the five parts of
these LiveLessons videos

168
00:07:35,960 --> 00:07:40,020
is an introduction to many
different open source libraries,

169
00:07:40,020 --> 00:07:42,640
some of which are bundled with Python,

170
00:07:42,640 --> 00:07:44,560
some of which are simply available

171
00:07:44,560 --> 00:07:46,660
via the open source community.

172
00:07:46,660 --> 00:07:48,470
And, whenever we had a choice

173
00:07:48,470 --> 00:07:50,520
between a couple of different libraries

174
00:07:50,520 --> 00:07:52,130
that could do the same thing,

175
00:07:52,130 --> 00:07:55,433
we always went with the one
that was simpler to use.

176
00:07:56,980 --> 00:07:58,670
With respect to keep it small,

177
00:07:58,670 --> 00:08:01,250
you're going to find
that the vast majority

178
00:08:01,250 --> 00:08:05,770
of the book's 538 code
examples are extremely small.

179
00:08:05,770 --> 00:08:09,520
Sometimes just one line of
code or a few lines of code.

180
00:08:09,520 --> 00:08:11,180
However, when you're programming,

181
00:08:11,180 --> 00:08:14,380
you often are going to be
building larger applications,

182
00:08:14,380 --> 00:08:17,260
so we also include 40 larger scripts

183
00:08:17,260 --> 00:08:19,423
and in-depth case studies as well.

184
00:08:20,970 --> 00:08:22,850
With respect to keep it topical,

185
00:08:22,850 --> 00:08:25,120
we did one and a half years of research

186
00:08:25,120 --> 00:08:27,780
in preparation for
writing our Python books

187
00:08:27,780 --> 00:08:30,150
and recording these Python videos as well.

188
00:08:30,150 --> 00:08:32,700
And normally, when we get
ready to write any book,

189
00:08:32,700 --> 00:08:35,020
we'll create what we
call a resource center,

190
00:08:35,020 --> 00:08:36,020
and we'll have anywhere

191
00:08:36,020 --> 00:08:39,970
from 25 to 50 different
key research pieces

192
00:08:39,970 --> 00:08:43,930
on which we will base our
writings and the examples

193
00:08:43,930 --> 00:08:47,630
that we eventually use in
these video recordings as well.

194
00:08:47,630 --> 00:08:49,070
Now, for the Python content,

195
00:08:49,070 --> 00:08:53,050
we actually had 1500
different resource centers,

196
00:08:53,050 --> 00:08:57,580
each of which averaged around
10 different research pieces.

197
00:08:57,580 --> 00:09:00,290
So about 15,000 different pieces,

198
00:09:00,290 --> 00:09:02,900
whether they were books,
or articles, or videos,

199
00:09:02,900 --> 00:09:07,200
or blog posts went into our
research for this material.

200
00:09:07,200 --> 00:09:10,420
And what that enabled us to
do was really take the pulse

201
00:09:10,420 --> 00:09:12,130
of all the different communities

202
00:09:12,130 --> 00:09:15,320
that we were trying to target
with the content in this book,

203
00:09:15,320 --> 00:09:17,720
whether it be the computer
science community,

204
00:09:17,720 --> 00:09:21,200
the data science community, the
Python community in general,

205
00:09:21,200 --> 00:09:24,520
or the AI big data and
cloud communities as well.

206
00:09:24,520 --> 00:09:27,450
And that also enabled us
to create some really cool

207
00:09:27,450 --> 00:09:30,250
and interesting examples that
I'll be presenting to you

208
00:09:30,250 --> 00:09:31,283
in these videos.

209
00:09:33,190 --> 00:09:36,620
The best way to use these
videos is to watch a little

210
00:09:36,620 --> 00:09:39,140
and then do a little
and one of the reasons

211
00:09:39,140 --> 00:09:42,010
that it works so well
to take that approach

212
00:09:42,010 --> 00:09:45,480
is the IPython interpreter
that you'll see me using

213
00:09:45,480 --> 00:09:47,470
throughout my presentation.

214
00:09:47,470 --> 00:09:50,870
The IPython interpreter
gives you immediate feedback,

215
00:09:50,870 --> 00:09:53,900
enabling you to write anything
from a simple expression

216
00:09:53,900 --> 00:09:56,460
like seven plus three and see the result

217
00:09:56,460 --> 00:09:59,770
up through large chunks of code as well,

218
00:09:59,770 --> 00:10:03,040
and you can immediately
execute those expressions,

219
00:10:03,040 --> 00:10:06,520
statements, and chunks of
code and see the results.

220
00:10:06,520 --> 00:10:07,610
If you get an error,

221
00:10:07,610 --> 00:10:10,670
you can see the code and the
error right next to each other

222
00:10:10,670 --> 00:10:12,550
so you can go back and fix that problem

223
00:10:12,550 --> 00:10:14,900
and try again instantaneously.

224
00:10:14,900 --> 00:10:17,090
And, if it's a correct piece of code,

225
00:10:17,090 --> 00:10:19,290
you can see the results immediately,

226
00:10:19,290 --> 00:10:21,670
which makes it a really great way

227
00:10:21,670 --> 00:10:24,320
to learn the language,
explore the language,

228
00:10:24,320 --> 00:10:27,730
and also to explore the
many different libraries

229
00:10:27,730 --> 00:10:31,163
that you have access to via
the open source community.

230
00:10:32,360 --> 00:10:34,000
Throughout lessons one through 10,

231
00:10:34,000 --> 00:10:37,100
my key focus will be on
presenting rich coverage

232
00:10:37,100 --> 00:10:39,800
of Python programming fundamentals.

233
00:10:39,800 --> 00:10:42,380
Now, not only will I be
showing you the syntax

234
00:10:42,380 --> 00:10:44,270
of the language but we'll also be looking

235
00:10:44,270 --> 00:10:46,710
at three different programming idioms.

236
00:10:46,710 --> 00:10:49,690
We'll look at procedural
style programming in Python,

237
00:10:49,690 --> 00:10:51,540
object-oriented programming,

238
00:10:51,540 --> 00:10:54,380
and functional-style programming as well.

239
00:10:54,380 --> 00:10:56,210
And, throughout all of the examples,

240
00:10:56,210 --> 00:10:58,920
we'll be focusing on best practices

241
00:10:58,920 --> 00:11:00,823
and current Python idiom.

242
00:11:02,280 --> 00:11:04,320
During our one and a
half years of research

243
00:11:04,320 --> 00:11:05,940
leading into this project,

244
00:11:05,940 --> 00:11:09,260
one of the things that we
found is that the vast majority

245
00:11:09,260 --> 00:11:11,720
of programming that people do in Python

246
00:11:11,720 --> 00:11:15,330
is what we like to refer to
as object-based programming.

247
00:11:15,330 --> 00:11:16,920
You import a library,

248
00:11:16,920 --> 00:11:19,710
create an object of one of
the types in that library,

249
00:11:19,710 --> 00:11:22,030
and then interact with that object.

250
00:11:22,030 --> 00:11:24,810
And one of the reasons
that you're able to do that

251
00:11:24,810 --> 00:11:28,830
is the literally thousands
of different Python libraries

252
00:11:28,830 --> 00:11:30,630
that are available to you.

253
00:11:30,630 --> 00:11:33,190
Now the Python standard library itself

254
00:11:33,190 --> 00:11:36,710
has a tremendous number
of what are called modules

255
00:11:36,710 --> 00:11:40,340
that you can import into
your applications and use.

256
00:11:40,340 --> 00:11:42,630
And the table that you see on the screen

257
00:11:42,630 --> 00:11:45,460
is just a small subset of those libraries.

258
00:11:45,460 --> 00:11:48,300
And in particular ones
that we'll be focusing on

259
00:11:48,300 --> 00:11:50,343
throughout these LiveLessons videos.

260
00:11:51,900 --> 00:11:53,570
In addition we'll be focusing

261
00:11:53,570 --> 00:11:56,950
on lots of other open source libraries,

262
00:11:56,950 --> 00:11:58,940
many of which are actually installed

263
00:11:58,940 --> 00:12:01,180
with the Anaconda Python distribution

264
00:12:01,180 --> 00:12:04,780
that I'll be using as I
present Python to you.

265
00:12:04,780 --> 00:12:06,050
On the screen now you see

266
00:12:06,050 --> 00:12:10,060
some of the key artificial
intelligence, data science,

267
00:12:10,060 --> 00:12:12,280
and big data-related libraries

268
00:12:12,280 --> 00:12:14,863
that we'll be presenting
throughout these videos.

269
00:12:16,760 --> 00:12:19,717
During the 1850s Gold
Rush, there was a saying,

270
00:12:19,717 --> 00:12:22,300
"There is gold in them thar hills."

271
00:12:22,300 --> 00:12:26,477
Well in 2019 and '20 and
beyond, we can say instead,

272
00:12:26,477 --> 00:12:29,370
"There's gold in them thar data."

273
00:12:29,370 --> 00:12:31,930
Almost all of the
cutting edge applications

274
00:12:31,930 --> 00:12:33,660
that are being developed nowadays

275
00:12:33,660 --> 00:12:35,950
are data-driven applications

276
00:12:35,950 --> 00:12:39,640
and specifically big
data-driven applications.

277
00:12:39,640 --> 00:12:42,420
So another saying that you
may have heard previously

278
00:12:42,420 --> 00:12:45,210
is that "A picture is
worth a thousand words."

279
00:12:45,210 --> 00:12:48,050
Well, when you're talking about big data,

280
00:12:48,050 --> 00:12:50,810
a visualization, which is just a diagram,

281
00:12:50,810 --> 00:12:53,240
is something that could be worth billions

282
00:12:53,240 --> 00:12:57,483
or even trillions of data
items in a big data source.

283
00:12:58,410 --> 00:13:01,700
So, with that in mind, we're
going to be presenting scores

284
00:13:01,700 --> 00:13:06,060
of static, dynamic, and
interactive visualizations

285
00:13:06,060 --> 00:13:08,040
throughout these LiveLessons videos.

286
00:13:08,040 --> 00:13:11,840
And, as you're going to see,
with tiny amounts of code,

287
00:13:11,840 --> 00:13:13,890
you can create some really cool

288
00:13:13,890 --> 00:13:16,290
and interesting visualizations as well.

289
00:13:16,290 --> 00:13:18,250
And that's going to be really critical

290
00:13:18,250 --> 00:13:20,010
as you start making your way

291
00:13:20,010 --> 00:13:23,340
into big data applications development.

292
00:13:23,340 --> 00:13:27,260
The vast majority of data
that we receive nowadays

293
00:13:27,260 --> 00:13:30,190
is so large that no human being

294
00:13:30,190 --> 00:13:32,290
can look at all of that data.

295
00:13:32,290 --> 00:13:34,620
So visualizations become critical

296
00:13:34,620 --> 00:13:37,273
in helping people understand
what they're looking at.

297
00:13:38,700 --> 00:13:41,210
As we get into some of
the higher-end lessons,

298
00:13:41,210 --> 00:13:43,620
one of the things that
we're going to start doing

299
00:13:43,620 --> 00:13:46,070
is talking about the different steps

300
00:13:46,070 --> 00:13:48,330
in a data science study.

301
00:13:48,330 --> 00:13:51,530
So we're going to show
you how to obtain data

302
00:13:51,530 --> 00:13:53,540
and prepare it for analysis

303
00:13:53,540 --> 00:13:56,750
and one of the things that
we'll do after we obtain data

304
00:13:56,750 --> 00:13:58,690
is get to know that data.

305
00:13:58,690 --> 00:13:59,850
And, in that case,

306
00:13:59,850 --> 00:14:02,580
visualization is often a key aspect

307
00:14:02,580 --> 00:14:04,390
of getting to know your data.

308
00:14:04,390 --> 00:14:07,260
We'll also talk about how to analyze data

309
00:14:07,260 --> 00:14:09,430
using many different techniques.

310
00:14:09,430 --> 00:14:11,620
And, to do that, we're
going to be building things

311
00:14:11,620 --> 00:14:14,980
like machine learning models
and deep learning models.

312
00:14:14,980 --> 00:14:17,270
And one of the things you
will want to be able to do

313
00:14:17,270 --> 00:14:19,860
is to tune those models as well.

314
00:14:19,860 --> 00:14:21,450
And then finally we'll want

315
00:14:21,450 --> 00:14:24,890
to communicate the results of
our studies, which once again,

316
00:14:24,890 --> 00:14:28,173
is often going to involve visualizations.

317
00:14:29,560 --> 00:14:32,220
In parts four and five of
these LiveLessons videos,

318
00:14:32,220 --> 00:14:33,260
you're going to be working

319
00:14:33,260 --> 00:14:36,020
with many different predefined models

320
00:14:36,020 --> 00:14:38,680
that somebody else
creates and trains for you

321
00:14:38,680 --> 00:14:40,380
and then makes available to you

322
00:14:40,380 --> 00:14:43,290
through the various libraries
that we'll be importing

323
00:14:43,290 --> 00:14:45,540
and using in our Python applications.

324
00:14:45,540 --> 00:14:48,840
And you're also going to learn
how to create your own models

325
00:14:48,840 --> 00:14:53,010
and train them based on data
sets that are typically bundled

326
00:14:53,010 --> 00:14:55,350
with the libraries that
we'll be introducing,

327
00:14:55,350 --> 00:14:59,380
although you can also download
and use other data sets

328
00:14:59,380 --> 00:15:01,780
to train your models as well.

329
00:15:01,780 --> 00:15:04,030
Now throughout those two
lessons we're going to look

330
00:15:04,030 --> 00:15:06,940
at all sorts of cool
and interesting topics

331
00:15:06,940 --> 00:15:09,650
starting with natural language processing,

332
00:15:09,650 --> 00:15:12,210
then we'll move on to data mining Twitter,

333
00:15:12,210 --> 00:15:15,950
which is one of the most
popular big data sources

334
00:15:15,950 --> 00:15:18,050
that are out there and also one

335
00:15:18,050 --> 00:15:21,830
for which natural language
processing is commonly used

336
00:15:21,830 --> 00:15:25,010
to perform a technique
called sentiment analysis,

337
00:15:25,010 --> 00:15:27,670
where you look at the text of tweets

338
00:15:27,670 --> 00:15:30,440
and you basically determine
if they're positive

339
00:15:30,440 --> 00:15:32,740
or negative about a given topic.

340
00:15:32,740 --> 00:15:34,590
And that could be used to do things

341
00:15:34,590 --> 00:15:38,120
like try to predict election
results or try to predict

342
00:15:38,120 --> 00:15:41,390
how well a movie is going
to do at the box office.

343
00:15:41,390 --> 00:15:44,970
We'll move on to IBM Watson
and Cognitive Computing

344
00:15:44,970 --> 00:15:47,930
where we'll build a cool
interlanguage translator

345
00:15:47,930 --> 00:15:49,840
using their web services.

346
00:15:49,840 --> 00:15:52,770
We'll also take a look at
building your own models

347
00:15:52,770 --> 00:15:55,860
in the machine learning
and deep learning lessons.

348
00:15:55,860 --> 00:16:00,170
And then finally we'll finish
off with our big data lesson

349
00:16:00,170 --> 00:16:04,310
where we'll cover topics like
Apache Hadoop, Apache Spark,

350
00:16:04,310 --> 00:16:08,053
NoSQL databases, and
the Internet of Things.

351
00:16:09,890 --> 00:16:12,100
Now that we've talked about
all of the different things

352
00:16:12,100 --> 00:16:14,270
that you'll see throughout
parts one through five

353
00:16:14,270 --> 00:16:17,370
of these Python Fundamentals
LiveLessons videos,

354
00:16:17,370 --> 00:16:20,100
let's talk specifically
about what you'll do here

355
00:16:20,100 --> 00:16:21,520
in part one.

356
00:16:21,520 --> 00:16:23,990
We'll start out with a
Before You Begin lesson

357
00:16:23,990 --> 00:16:25,530
in which I'm going to talk about

358
00:16:25,530 --> 00:16:28,790
installing the Anaconda
Python distribution,

359
00:16:28,790 --> 00:16:31,530
getting the code examples
that we provide for you

360
00:16:31,530 --> 00:16:33,320
to execute on your own.

361
00:16:33,320 --> 00:16:35,100
And those code examples, by the way,

362
00:16:35,100 --> 00:16:36,800
are provided in two forms.

363
00:16:36,800 --> 00:16:39,540
You'll find them in source code files

364
00:16:39,540 --> 00:16:42,400
which end with a .py extension

365
00:16:42,400 --> 00:16:43,940
and you'll be able to use those

366
00:16:43,940 --> 00:16:46,560
with the interactive IPython interpreter

367
00:16:46,560 --> 00:16:50,470
or to execute them as what
we call scripts in Python.

368
00:16:50,470 --> 00:16:53,960
But we also provide what are
known as Jupyter notebooks,

369
00:16:53,960 --> 00:16:58,870
which are interactive web-based
notebooks that contain text,

370
00:16:58,870 --> 00:17:02,470
they can also contain
images, audio, video,

371
00:17:02,470 --> 00:17:05,010
and live code that you can execute

372
00:17:05,010 --> 00:17:07,030
directly in your web browser.

373
00:17:07,030 --> 00:17:10,250
And you may find that those
are actually a really nice way

374
00:17:10,250 --> 00:17:12,390
for you to interact with Python

375
00:17:12,390 --> 00:17:16,230
and learn about the Python
programming language as well.

376
00:17:16,230 --> 00:17:19,780
I'll also talk about the two
key Python package managers

377
00:17:19,780 --> 00:17:23,060
that you'll use for installing
additional libraries

378
00:17:23,060 --> 00:17:25,550
throughout these LiveLessons videos.

379
00:17:25,550 --> 00:17:27,340
As we move on to lesson one,

380
00:17:27,340 --> 00:17:29,930
we'll focus on three test drives

381
00:17:29,930 --> 00:17:32,100
where we'll show you three different ways

382
00:17:32,100 --> 00:17:35,000
to execute the Python
code that we provide.

383
00:17:35,000 --> 00:17:38,520
One of which is going to be
the IPython Interactive Mode

384
00:17:38,520 --> 00:17:42,610
where you enter either
expressions or statements

385
00:17:42,610 --> 00:17:45,290
and interactively execute them.

386
00:17:45,290 --> 00:17:47,910
Another is going to be to use IPython

387
00:17:47,910 --> 00:17:50,370
to execute scripts in their entirety.

388
00:17:50,370 --> 00:17:52,850
So those are full-blown applications

389
00:17:52,850 --> 00:17:54,500
that we'll develop in Python.

390
00:17:54,500 --> 00:17:57,200
And then, finally, we'll
do our introduction

391
00:17:57,200 --> 00:17:59,510
to the Jupyter notebooks environment

392
00:17:59,510 --> 00:18:02,500
and show you how to get the
environment up and running,

393
00:18:02,500 --> 00:18:05,150
how to load notebooks into Jupyter,

394
00:18:05,150 --> 00:18:07,460
and then how to execute code

395
00:18:07,460 --> 00:18:09,563
within a Jupyter notebook as well.

396
00:18:11,220 --> 00:18:12,870
In lesson two, we're going to start

397
00:18:12,870 --> 00:18:16,000
getting into Python
programming fundamentals.

398
00:18:16,000 --> 00:18:18,030
We'll look at basic statements,

399
00:18:18,030 --> 00:18:20,270
show you how to create
variables in Python.

400
00:18:20,270 --> 00:18:21,130
And if you're coming

401
00:18:21,130 --> 00:18:23,870
from a strongly typed
programming language,

402
00:18:23,870 --> 00:18:26,030
one of the things that
you'll see about Python

403
00:18:26,030 --> 00:18:29,860
is that it's dynamically typed
so you don't have to state

404
00:18:29,860 --> 00:18:32,500
in advance that a given variable

405
00:18:32,500 --> 00:18:35,520
is going to hold an integer,
or a floating point number,

406
00:18:35,520 --> 00:18:36,490
or a string.

407
00:18:36,490 --> 00:18:39,000
It figures all of that out for you

408
00:18:39,000 --> 00:18:41,500
and it's always based on
whatever that variable

409
00:18:41,500 --> 00:18:44,520
happens to be referring
to at a given time.

410
00:18:44,520 --> 00:18:46,680
We'll look at some basic input and output

411
00:18:46,680 --> 00:18:48,140
as part of lesson two.

412
00:18:48,140 --> 00:18:51,520
We'll also start to show you
the various different forms

413
00:18:51,520 --> 00:18:53,420
of strings in Python.

414
00:18:53,420 --> 00:18:54,450
We'll show you, in fact,

415
00:18:54,450 --> 00:18:56,530
three different forms in lesson two,

416
00:18:56,530 --> 00:18:58,290
and a couple of additional ones

417
00:18:58,290 --> 00:19:00,600
in subsequent lessons as well.

418
00:19:00,600 --> 00:19:03,070
And finally, at the end of lesson two,

419
00:19:03,070 --> 00:19:04,980
we're going to start
talking about the fact

420
00:19:04,980 --> 00:19:07,690
that everything in Python is an object

421
00:19:07,690 --> 00:19:10,980
and we'll actually demonstrate
some of that dynamic typing

422
00:19:10,980 --> 00:19:12,673
I was talking about as well.

423
00:19:14,140 --> 00:19:14,973
In lesson three,

424
00:19:14,973 --> 00:19:17,750
we'll be focusing on
Python's control statements,

425
00:19:17,750 --> 00:19:20,460
and this is where you're
starting to get a sense

426
00:19:20,460 --> 00:19:23,610
of how small the Python
language really is.

427
00:19:23,610 --> 00:19:27,100
It has fewer overall
keywords and statements

428
00:19:27,100 --> 00:19:29,420
than most of the other
programming languages

429
00:19:29,420 --> 00:19:32,880
that you're probably
coming from into Python.

430
00:19:32,880 --> 00:19:34,600
So we'll talk about the if statement

431
00:19:34,600 --> 00:19:36,400
and it's three different forms here.

432
00:19:36,400 --> 00:19:39,260
We'll introduce the only
two looping structures

433
00:19:39,260 --> 00:19:42,110
that Python has the for
loop and the while loop.

434
00:19:42,110 --> 00:19:43,530
And, as you'll see,

435
00:19:43,530 --> 00:19:46,990
Python does not have a
counter controlled for loop.

436
00:19:46,990 --> 00:19:48,550
The one that we'll show you

437
00:19:48,550 --> 00:19:51,740
is for iterating over a sequence.

438
00:19:51,740 --> 00:19:53,870
And as part of that we're going to start

439
00:19:53,870 --> 00:19:57,260
to introduce one of the key
data structures in Python,

440
00:19:57,260 --> 00:19:58,750
which is known as a list.

441
00:19:58,750 --> 00:20:01,770
And that's similar to
the concept of an array

442
00:20:01,770 --> 00:20:04,300
in other programming languages.

443
00:20:04,300 --> 00:20:05,750
In this lesson we'll also talk

444
00:20:05,750 --> 00:20:08,150
about the augmented assignment operators

445
00:20:08,150 --> 00:20:11,113
and introduce the Boolean
operators as well.

446
00:20:12,740 --> 00:20:14,940
In lesson four, we're going
to start talking about

447
00:20:14,940 --> 00:20:19,610
how to create your own custom
Python function definitions.

448
00:20:19,610 --> 00:20:21,760
Now, one of the things I always try to do

449
00:20:21,760 --> 00:20:24,190
in my LiveLessons videos is compare

450
00:20:24,190 --> 00:20:26,600
and contrast the current
language I'm talking about,

451
00:20:26,600 --> 00:20:27,960
in this case Python,

452
00:20:27,960 --> 00:20:29,860
with the other programming languages

453
00:20:29,860 --> 00:20:31,450
that you may be familiar with.

454
00:20:31,450 --> 00:20:32,470
And, in particular,

455
00:20:32,470 --> 00:20:35,620
Python is a C-based programming language.

456
00:20:35,620 --> 00:20:38,670
So you'll find that I
will often compare Python

457
00:20:38,670 --> 00:20:43,670
to languages like C, C++,
Java, and C# as well.

458
00:20:43,830 --> 00:20:45,560
And one of the reasons I bring that up

459
00:20:45,560 --> 00:20:48,250
is because the way you define functions

460
00:20:48,250 --> 00:20:51,230
in Python is somewhat different

461
00:20:51,230 --> 00:20:53,480
from those other programming languages.

462
00:20:53,480 --> 00:20:55,810
And that's a combination of the fact

463
00:20:55,810 --> 00:20:57,950
that Python uses different keywords

464
00:20:57,950 --> 00:20:59,740
and also the fact that Python

465
00:20:59,740 --> 00:21:02,090
is a dynamically typed language,

466
00:21:02,090 --> 00:21:06,170
whereas those other languages
are statically typed.

467
00:21:06,170 --> 00:21:07,210
Now, in this lesson,

468
00:21:07,210 --> 00:21:09,720
we're also going to be
talking a little bit more

469
00:21:09,720 --> 00:21:11,960
about how to import libraries

470
00:21:11,960 --> 00:21:15,220
so that you can use them
in your applications.

471
00:21:15,220 --> 00:21:16,610
We're going to start to get

472
00:21:16,610 --> 00:21:19,220
into some basic simulation techniques

473
00:21:19,220 --> 00:21:21,120
with random number generation.

474
00:21:21,120 --> 00:21:23,160
We'll talk a little bit about scope

475
00:21:23,160 --> 00:21:24,940
in the context of functions.

476
00:21:24,940 --> 00:21:27,510
And we'll also show you some syntax things

477
00:21:27,510 --> 00:21:32,510
like default parameter values
and keyword arguments as well.

478
00:21:32,870 --> 00:21:34,880
Towards the end of this lesson,

479
00:21:34,880 --> 00:21:38,690
we're also going to get into
tuples for the first time,

480
00:21:38,690 --> 00:21:42,380
which are another key
data structure in Python.

481
00:21:42,380 --> 00:21:44,260
And tuples basically allow you

482
00:21:44,260 --> 00:21:47,120
to aggregate a bunch of related items

483
00:21:47,120 --> 00:21:49,400
which may or may not be of the same type.

484
00:21:49,400 --> 00:21:52,890
And in this lesson, we'll use
that capability to demonstrate

485
00:21:52,890 --> 00:21:57,023
how to return more than one
value at a time from a function.

486
00:21:58,950 --> 00:22:00,030
Now throughout part one

487
00:22:00,030 --> 00:22:02,740
of these Python Fundamentals
LiveLessons videos,

488
00:22:02,740 --> 00:22:05,180
a couple of key things
that you're going to see

489
00:22:05,180 --> 00:22:07,840
are a little bit of
functional style programming

490
00:22:07,840 --> 00:22:12,180
and the first several of our
Intro to Data Science sections.

491
00:22:12,180 --> 00:22:14,340
With regard to functional
style programming,

492
00:22:14,340 --> 00:22:16,030
what you're typically going to be doing

493
00:22:16,030 --> 00:22:18,630
is saying what you want to accomplish

494
00:22:18,630 --> 00:22:21,450
not how to accomplish a given task.

495
00:22:21,450 --> 00:22:24,350
And one of the reasons
that we're able to do that

496
00:22:24,350 --> 00:22:25,940
is via libraries.

497
00:22:25,940 --> 00:22:28,910
The Python standard library is extensive.

498
00:22:28,910 --> 00:22:31,020
There are a ton of built-in functions

499
00:22:31,020 --> 00:22:32,850
and lots of additional libraries

500
00:22:32,850 --> 00:22:35,200
that you can import and use as well.

501
00:22:35,200 --> 00:22:37,330
And they take this kind of everything

502
00:22:37,330 --> 00:22:39,540
but the kitchen sink approach where,

503
00:22:39,540 --> 00:22:41,920
if there is a common programming task,

504
00:22:41,920 --> 00:22:44,670
chances are there's already a library

505
00:22:44,670 --> 00:22:47,080
that can help you perform that task.

506
00:22:47,080 --> 00:22:50,050
So for instance if I
want to calculate the sum

507
00:22:50,050 --> 00:22:53,400
of a list of values, there's
a built-in sum function,

508
00:22:53,400 --> 00:22:56,650
which we'll introduce in part one,

509
00:22:56,650 --> 00:23:00,900
that is able to receive a list
and give you back that total.

510
00:23:00,900 --> 00:23:04,240
You don't have to figure out
how to calculate that total,

511
00:23:04,240 --> 00:23:07,200
the sum function already
knows how to do that for you.

512
00:23:07,200 --> 00:23:09,920
And we're going to see
lots of different instances

513
00:23:09,920 --> 00:23:13,510
like that as we introduce
you to various aspects

514
00:23:13,510 --> 00:23:15,310
of the Python standard library

515
00:23:15,310 --> 00:23:18,680
and other third-party libraries as well.

516
00:23:18,680 --> 00:23:21,890
Now with respect to the Intro
to Data Science sections,

517
00:23:21,890 --> 00:23:25,990
our goal is to get you
thinking about data science

518
00:23:25,990 --> 00:23:28,380
and various topics related to it

519
00:23:28,380 --> 00:23:31,210
in preparation for the
high-end case studies

520
00:23:31,210 --> 00:23:33,440
that you'll be looking at in parts four

521
00:23:33,440 --> 00:23:36,060
and five of these LiveLessons videos.

522
00:23:36,060 --> 00:23:40,240
So in lesson one through
four in this part,

523
00:23:40,240 --> 00:23:43,060
we're going to start to
introduce some key things

524
00:23:43,060 --> 00:23:45,460
like basic descriptive statistics

525
00:23:45,460 --> 00:23:49,120
and also start talking about
getting to know your data

526
00:23:49,120 --> 00:23:51,150
which is going to be a key aspect

527
00:23:51,150 --> 00:23:53,823
of any data science study you perform.