00:04: hey everyone
00:05: nick dear burtis here teaching you
00:07: financial modeling
00:08: today i'm here to talk about the
00:10: syllabus and expectations
00:12: for the course so let's go ahead and go
00:15: over to the syllabus
00:17: so um the first thing to note on here
00:22: is the textbook uh so the textbook
00:25: here i'm recommending uh simon baninga's
00:29: financial modeling book
00:31: now an important thing to note about
00:33: this book is it is excel
00:34: only i have not yet been able to find a
00:38: good
00:39: python financial modeling book maybe
00:42: i'll
00:42: ultimately end up writing that book
00:45: but for now this is kind of the best
00:48: that we have
00:48: for the course now i
00:52: certainly this textbook is not required
00:53: i would generally only recommend it
00:55: if you're someone who learns well just
00:58: from reading
00:59: a textbook and you also don't have a ton
01:03: of excel experience
01:04: or you're not you know super familiar
01:07: with the finance
01:09: because the book is helpful for the
01:11: excel side of things and just
01:13: generally how to go about building the
01:15: model
01:16: but it's not really going to help you at
01:18: all as far as the
01:20: the python side beyond you know knowing
01:22: generally
01:24: what goes into the model um
01:27: so certainly don't feel the need to
01:29: purchase that unless you fall into that
01:30: category
01:32: um as far as your computer you know the
01:35: main requirement for the class is is
01:36: having a computer that can run
01:38: microsoft excel either mac or windows is
01:42: fine
01:44: though you know i'm a i'm a windows and
01:46: linux user
01:47: so i haven't used mac very much and so i
01:50: won't be able to help as much
01:52: on any kind of operating specific
01:55: operating system specific
01:56: issues that you may have uh using a mac
02:00: but certainly it works for all the
02:02: course material just fine
02:04: and many students have gone through the
02:05: course using a mac before
02:09: so uh you know the general idea of this
02:13: course
02:13: is that we are covering generally
02:17: how to build a financial model
02:20: not necessarily you know here's the 10
02:23: different
02:24: types of models let's learn all the
02:26: steps to complete each of those models
02:28: it's more about a general process
02:31: and the skills required to build any
02:34: financial model
02:36: so my goal is to give you the these
02:38: skills and patterns
02:40: uh and ideas so that you go on the job
02:43: and you know they ask you to build
02:46: a model for a situation where
02:50: it's not just you know your standard
02:52: like fill in some template is no it's a
02:54: custom model that you have to tailor to
02:56: this situation
02:57: you'll be ready to go and do that so
02:59: that's the idea here is to
03:02: give more real world kind of experience
03:05: and skills
03:06: uh to be able to do this all on your own
03:09: out in the workforce
03:12: and the two tools that we're going to
03:14: focus on
03:15: to uh implement these models
03:18: are excel and python
03:22: and i want to note that this class is
03:25: definitely hard i have heard
03:29: time and time again from people that
03:32: it's the hardest course they took at
03:35: university of florida
03:37: but i've also heard just as many people
03:40: say
03:40: that it's the most rewarding class that
03:42: they took that they got the most out of
03:44: it they got the most
03:45: tangible skills and results out of it
03:49: so you know don't come into this course
03:52: just you know saying i need an elective
03:54: i just need to fill the slot
03:56: uh only come into this course if you're
03:58: truly interested in financial modeling
04:00: and you're willing to
04:01: work hard to be able to
04:04: uh implement all of the projects and
04:06: learn all the concepts and skills
04:09: because definitely if you're not someone
04:11: that's coming in with
04:13: programming experience which i assume
04:14: that most people don't
04:16: but if you aren't you have your work cut
04:18: out for you certainly
04:21: so it's not an easy course don't take it
04:24: lightly
04:24: i'm here to help you along the way i
04:26: want to give you all the support
04:28: that i can so that you can learn this
04:29: stuff but it is not an easy course by
04:32: any means
04:35: and coming to the prerequisites for the
04:38: course
04:39: so minimum business finance
04:43: but even further i would recommend that
04:46: you have taken
04:48: the debt and money markets course the
04:49: equity and capital markets course
04:52: kind of the core finance courses
04:55: because we really are focusing on how to
04:58: build the models
04:59: we really don't have time in the course
05:02: to really cover the finance concepts so
05:04: you should be coming in
05:06: with strong knowledge in the concepts so
05:09: that we can focus
05:10: just on the actual model and if you are
05:14: missing some of these skills
05:16: and knowledge uh then i can recommend
05:18: you some resources where you can fill
05:20: the gaps
05:21: but generally i would recommend that you
05:23: have these three courses
05:24: even though the minimum requirement is
05:26: only business finance
05:29: so coming to technical skills certainly
05:33: you're going to need
05:34: algebra which is true for mostly any
05:36: finance class just to be able to
05:38: you know manipulate equations uh that
05:40: are within the models
05:44: but further than that you need to have
05:47: some excel
05:47: experience coming in i'm assuming that
05:51: uh the typical student coming in is at
05:54: an intermediate level
05:55: with excel and has zero python
05:59: experience so that's kind of the average
06:01: i'm trying to teach to
06:03: so if you're someone coming in without
06:05: any excel experience
06:07: or with a small amount of excel
06:10: experience
06:12: i listed a few categories of skills that
06:14: you should make sure that
06:16: you understand and can apply so just go
06:19: ahead and take a look at that list
06:21: on the syllabus and make sure that you
06:24: can
06:24: uh you know do all these things in excel
06:27: and you feel comfortable with them
06:29: uh and if not i have some resources at
06:32: the end of the syllabus that you can
06:33: look through and
06:35: fill in your gaps and knowledge
06:38: on these categories and you know you can
06:41: feel
06:42: free to ask me any questions along the
06:44: way
06:46: if you're someone that has programming
06:48: or python experience
06:49: that's wonderful you're going to have
06:50: definitely less of a learning curve in
06:52: the beginning of the course
06:53: but those are definitely not a
06:55: requirement i'm assuming
06:57: no experience with programming or python
07:02: so uh the class we have three parts of
07:05: the class
07:06: the lecture videos like you're watching
07:08: right now
07:09: uh but then we're also going to have two
07:11: more interactive components
07:13: of the class uh so they're going to be
07:15: lecture review sessions
07:17: and interactive lab sessions so the
07:19: lecture
07:20: review sessions are about uh giving you
07:23: an opportunity
07:24: to ask questions from the lecture
07:27: you know and you know as a group that
07:30: way
07:30: um you can see everyone else's questions
07:34: as well
07:36: and that way we still kind of get the
07:38: interactive lecture experience
07:39: even though the lectures are recorded so
07:42: those
07:43: uh lecture review sessions will also be
07:46: recorded um so we'll have um
07:49: posted uh after the sessions then we'll
07:52: have
07:52: everyone's questions and the answers to
07:54: those questions
07:56: uh so that you can um
07:59: go through those and make sure that you
08:00: understand everything
08:02: and then the interactive lab sessions
08:05: that's about
08:06: giving you a time for
08:09: uh everyone to be on a call and for
08:11: people to be working
08:13: on the problems for the class so whether
08:16: that's the
08:16: weekly uh assignments or if you
08:20: get through all those then you can work
08:22: towards the projects as well
08:25: but it's just giving you an opportunity
08:26: where you know you can
08:29: discuss things with your classmates and
08:32: you can
08:32: ask me questions as well so i'm making
08:36: my time available so that i'm a resource
08:39: for you as you go to complete the
08:42: problems
08:43: certainly it helps a lot especially in
08:47: programming if you're stuck on something
08:50: just to have someone knowledgeable
08:53: to just point you in the right direction
08:55: it can really save you a lot of time so
08:57: these lab sessions
08:58: are very valuable and you should
09:00: definitely take advantage of those
09:05: as far as the projects in the course
09:09: that's the main grade in the course is
09:11: the projects
09:13: and so it's going to be uh
09:16: you know relatively larger projects
09:20: you definitely want to allow yourself
09:21: plenty of time to complete them
09:24: um you know this is definitely where
09:27: all the difficulty in the course comes
09:29: in um
09:30: is being able to learn enough of the
09:33: skills to be able to complete these
09:34: projects
09:36: so the projects are fairly open-ended
09:39: they just give you
09:40: a certain problem that you need to
09:42: create a model for
09:43: uh and then it's you know up to you how
09:46: you want to actually go
09:47: and create that model um so it's not
09:50: just going to be like
09:52: here do here's how to do abc okay now in
09:56: the project do a b
09:57: and c with different numbers no that's
09:58: not it at all it's it's going to be
10:00: a completely different model you're
10:03: you're learning a general pattern that
10:05: you can then apply
10:06: to any new uh situation
10:10: and as far as um
10:15: who you should be working with on the
10:16: project so ultimately
10:18: the projects are individual everyone is
10:21: going to submit their own project
10:24: and you should not be copying each
10:25: other's work that means don't copy
10:28: workbooks don't copy code and don't you
10:30: know just sit there
10:32: and do the exact same things next to
10:34: each other
10:35: now i do want to encourage you that you
10:38: can
10:39: still uh you know discuss things you can
10:42: talk through the problems with your
10:44: classmates i don't want you to have to
10:46: go this completely alone
10:49: so definitely don't feel like i'm saying
10:52: you know you can't
10:53: talk about the projects at all you can
10:55: definitely talk about them
10:56: and kind of discuss your approaches and
10:59: challenges
11:00: and how you've solved you know certain
11:02: parts of it
11:04: just don't you know give someone the
11:06: guide to completely go through the thing
11:09: the idea is that everyone should be
11:12: completing their own projects so that
11:13: everyone
11:14: is getting the full learning experience
11:20: and so you you know a lot of this course
11:22: is going to be
11:23: asking me questions uh you know i'm here
11:26: as a resource
11:28: for you uh to make sure that
11:31: um you know whenever you have a doubt uh
11:34: i can come in and make everything clear
11:38: so the lecture review sessions those are
11:40: those group uh video calls
11:42: where you'll be able to ask questions
11:45: and you know have the whole class
11:47: participate in that
11:50: and it'll be more you know general uh
11:52: generalized
11:53: uh concepts and and techniques at that
11:56: point
11:57: lab sessions are more about the problems
11:59: you're working on a problem and you have
12:01: a specific
12:02: issue trying to complete that problem uh
12:05: then i can help you out
12:07: you know either one-on-one or you know
12:09: one or
12:10: me with you know just a small group of
12:12: you
12:14: and then you can email me and i'll email
12:17: you back
12:18: or we can make an appointment for a
12:21: video call if necessary if we need a
12:24: longer format
12:25: to discuss something in more detail or
12:28: you're not
12:29: able to attend some of the other
12:31: sessions
12:33: and i just want to say before you ask
12:35: questions make sure that you've watched
12:37: any of the relevant
12:38: lectures uh because if your question is
12:41: just
12:41: directly something that was covered in
12:43: the lecture i'm just you know gonna say
12:45: go watch that lecture video it covers
12:47: this
12:47: uh well
12:53: as far as uh late policy
12:56: um you can submit the projects
12:59: late uh but it's you know ten percent
13:02: off
13:03: per day uh for each day that it's late
13:07: um and that 10 kicks in
13:11: um you know as soon as you miss the
13:13: deadline so
13:14: um the deadlines are are by midnight so
13:18: it's due wednesday uh
13:20: by midnight then if you turn it in
13:22: thursday morning until the 1am
13:24: you would be losing 10 um
13:28: which i think you know is a pretty
13:30: generous policy
13:31: 10 per day because in the work world you
13:35: often will not be given any latitude
13:37: uh if you just don't hit your deadline
13:39: you could get fired so
13:41: i think this is quite the accommodative
13:46: policy
13:48: um so what should you do to be
13:51: successful here um i definitely
13:55: you know unless you're someone who's
13:56: coming in with programming experience or
13:59: python experience
14:01: definitely you should take some time to
14:03: do
14:04: some outside learning on python
14:07: you know as i said i am assuming
14:09: everyone is coming in with no experience
14:12: but this is not a programming class this
14:15: is not a python
14:16: class it's a financial modeling class
14:18: and so we can only spend so much time
14:21: on the basics of programming in python
14:25: and so we'll go kind of quickly through
14:26: those and to be able to get a better
14:29: treatment of them so that you really
14:31: understand
14:32: what's going on which will make it that
14:34: much easier to
14:35: learn throughout the course uh you
14:37: should definitely
14:38: use some outside resources and i've put
14:40: those resources at the end of the
14:43: syllabus
14:45: so definitely in the past students who
14:47: utilized those outside resources
14:50: just had so much of an easier time in
14:52: the course because they weren't
14:54: getting tripped up on just the basics of
14:57: writing python code
14:59: they kind of had that down and so they
15:01: were able to focus
15:02: on applying it to financial modeling
15:04: rather than just learning the basics
15:08: definitely you need to watch all the
15:10: lectures um and
15:12: the lectures kind of build on each other
15:14: if you just try and
15:15: jump in on the last few before a project
15:18: is due you're going to be totally lost
15:19: because
15:20: it just keeps building and building so
15:23: definitely
15:24: go through in order
15:28: and i would definitely encourage you to
15:30: participate
15:32: in the interactive lab sessions um
15:35: you know that's that's huge to be able
15:36: to uh
15:38: you know work on the problem get stuck
15:41: and then be able to get
15:42: uh quick feedback from me that just
15:44: makes your cycle so much faster and
15:46: learning
15:47: uh so you're not sitting there uh for
15:49: hours struggling over something
15:51: uh which would be simple for me to
15:54: answer
15:55: uh very quickly so um those
15:58: lab sessions are very valuable and
16:00: students who have utilized those
16:03: to a greater degree have done better in
16:05: the course in the past
16:08: i will also be giving some practice
16:10: problems so i would definitely recommend
16:12: completing those and start the projects
16:16: very early as soon as you can because
16:19: there is a lot of work
16:20: involved in the projects
16:24: and definitely if there's any point in
16:26: the course where you start to feel
16:28: lost you start to feel behind
16:31: you need to come and talk to me you need
16:33: to email me
16:35: because you cannot afford to get behind
16:38: in this course
16:39: as i said the material just keeps
16:41: building
16:42: we keep using what we've learned
16:44: previously to go
16:45: and do the next thing so if you get lost
16:49: you're only going to get more lost as
16:51: time goes on so as soon as you have that
16:53: feeling
16:54: you need to talk to me i can you know
16:57: give you suggestions
16:58: to rectify that so definitely
17:02: do that rather than getting lost
17:05: and then getting behind and then digging
17:07: yourself into a hole that is very
17:09: difficult to get
17:10: out of
17:15: so the grading in the class 80
17:18: is uh these projects and 20
17:21: is the lab exercises so you'll also be
17:24: you know turning those lab exercises in
17:27: weekly and those are mostly for uh
17:30: sort of a completion grade um though
17:33: you know if i look at it and think that
17:35: you didn't give
17:37: a full effort on it then uh
17:40: you're going to lose points on the lab
17:43: exercise
17:44: but otherwise it doesn't necessarily
17:45: have to be completely correct
17:47: as long as you completed it and gave a
17:50: good faith
17:50: effort um in the lab exercise
17:54: then um you'll get credit for that
17:57: but the projects you know are completely
17:59: uh graded
18:01: on the basis of you know how accurate
18:04: they are
18:05: uh how readable they are and how well
18:08: formatted
18:09: uh they are
18:12: you can see the grading scale here so
18:14: take a look at that
18:15: for any details on that as well as other
18:19: grading policies you can review the
18:20: syllabus
18:22: um and so the main topics that we're
18:25: covering
18:26: um so the first is kind of the intro
18:28: section and then we're going to
18:30: focus mainly on corporate valuation in
18:32: this course
18:34: uh so in the intro section um after an
18:37: overview we're going to go into
18:39: learning the basic technical skills with
18:41: excel and python
18:44: and the first model that we're going to
18:45: cover is a time value of money model
18:48: uh you know looking at saving for
18:51: retirement so that should be you know
18:53: very tangible
18:54: for everybody and i think it's an
18:56: excellent place to start where we can
18:59: focus on the model and you know the
19:00: finance part of it
19:02: should come easily for everybody since
19:04: it's a basic
19:06: finance concept then we'll go into some
19:10: uh statistics tools that we need for
19:13: modeling
19:14: um and we'll learn about uh
19:17: monte carlo methods um are another
19:20: general technique
19:21: in financial modeling and as far as
19:24: where we're going to apply
19:25: all these tools and skills and and
19:28: patterns that we learn in the first part
19:29: of the course
19:31: uh we're going to look at a capital
19:33: budgeting
19:34: situation and then after that we're
19:37: ultimately going to work towards
19:40: uh the full discounted cash flow
19:42: valuation
19:43: of a stock um so that
19:46: is everything uh getting the weighted
19:49: average
19:50: cost of capital estimating the free cash
19:52: flows
19:54: and putting that all together into one
19:56: model
19:58: that will ultimately be the final
19:59: project in the course
20:01: is producing this discounted cash flow
20:04: valuation
20:07: so that's the course and then i've also
20:11: included here
20:12: on the syllabus resources that you can
20:14: use
20:15: to again supplement or
20:19: get started with your python and excel
20:22: skills
20:22: so i have excel as well as python
20:26: resources here for you so again unless
20:29: you are already coming in
20:31: with programming experience python
20:34: experience and strong excel experience
20:36: i would highly recommend that you take a
20:38: look at these resources
20:40: to be able to uh get an outside
20:43: perspective and get the basics
20:45: of these tools uh so that you can focus
20:49: more on the financial modeling part of
20:51: the course
20:54: so that is the syllabus next time we
20:57: will come back and talk about
21:00: what is a financial modeling and kind of
21:02: the introduction to the actual
21:04: course content so thanks for listening
21:07: and see you next time

00:03: hey everyone
00:03: nick dear brotis here teaching you
00:05: financial modeling
00:07: today i'm here to talk a little bit more
00:08: about what we're focusing on in this
00:10: class
00:11: and what is a financial model
00:15: so this class is
00:18: a skills and technique based
00:21: course so it's not let's go learn the
00:25: laundry list of financial models
00:28: it's more let's learn a general process
00:31: and the skills necessary
00:33: to be able to build financial models so
00:35: you can go out
00:36: and build whatever model you choose
00:41: um so
00:44: generally we're going to look at you
00:47: know
00:47: fairly simple uh models
00:52: but that'll be just the base model and
00:55: then we're going to extend that model
00:57: in a number of various ways like
01:00: sensitivity analysis scenario analysis
01:03: monte carlo simulation
01:05: these kind of things that are going to
01:07: give you a
01:08: much greater understanding of the
01:10: problem
01:11: than just doing the basic model
01:14: for the problem
01:17: so techniques and skills over
01:21: concrete models and
01:24: focus on extensions
01:27: to the models and being able to uh
01:31: really uh you know whatever problem you
01:33: happen to be modeling
01:35: really get a good understanding of
01:38: everything that's going on
01:40: by being able to model kind of the
01:42: variance of your result
01:44: as well as the original expected result
01:50: so what is a financial model
01:54: what is a model in general so
01:57: we can think of a model as something
01:59: actually
02:00: very simple it's just a process
02:03: which converts inputs to outputs so
02:08: you know that's a very general
02:10: definition but
02:11: you know you can have a model for nearly
02:14: anything
02:14: so we need a general definition
02:18: um so when you view it in this kind of
02:21: context
02:22: that a model just takes some inputs and
02:25: converts them
02:26: to outputs that really
02:31: gives us a concrete uh
02:34: way of thinking about the model um
02:38: rather than you know just thinking about
02:42: you know we're doing evaluation of a
02:44: company we're estimating the weighted
02:45: average cost of capital all these
02:47: particulars
02:48: ultimately it's just to convert your
02:51: inputs
02:51: to outputs
02:55: so we can make this a little more
02:57: concrete by looking
02:58: at a couple examples of models
03:02: so here first we can think about a
03:06: retirement problem um so this
03:09: is a problem where um
03:12: you know someone is saving for
03:15: retirement they're earning
03:16: uh wages over time and they're saving a
03:19: portion of that
03:21: and they want to save that and invest
03:23: that
03:24: money so that they can then retire
03:27: at the end of life so
03:31: figuring out how much uh this person
03:35: actually needs to save to be able to hit
03:38: their
03:38: retirement goals um
03:41: is you know the problem that we're
03:44: thinking about modeling here
03:46: so when we um you know think about the
03:51: real world problem that we are dealing
03:54: with
03:55: um we can also kind of split that
03:58: real world process
04:01: into the same kind of structure
04:04: so we're going to have inputs into the
04:06: real world process and we're going to
04:08: have outputs
04:11: and our model is ultimately going to try
04:13: and represent
04:14: that middle piece the process so
04:18: um in the real situation for the
04:20: retirement problem
04:21: uh someone is earning wages from their
04:25: job
04:26: and then they're saving a portion of
04:28: those wages so those
04:30: are our inputs uh you know how much are
04:32: they bringing in
04:33: um in cash and then how much of that are
04:36: they actually putting into
04:38: savings and then the the process
04:42: which converts the inputs to the outputs
04:45: for the retirement
04:46: real problem is the investment process
04:48: right
04:49: uh you know buying the investments um
04:52: and then holding them
04:53: and earning um you know dividends and
04:56: capital gains
04:56: on those investments ultimately selling
04:59: those investments at a later point
05:01: uh that's the real world process and
05:04: then the output or result from that
05:06: process
05:07: is that this person you know has a
05:09: certain amount of cash
05:11: from those investments and so they can
05:13: retire as a result
05:16: so when we think about modeling this um
05:19: so our model is going to take the place
05:22: of the real world process
05:24: so our model is basically trying to
05:26: capture uh
05:27: this investment process right uh you
05:30: know whatever
05:31: how much money is coming in investing
05:34: that in whatever
05:35: uh securities earning a certain return
05:38: on those
05:39: uh you know selling those at a later
05:41: date
05:42: um all these kind of things could go
05:45: into
05:45: the model and then the inputs to the
05:48: model
05:49: would be our you know tangible things
05:52: that we can measure
05:53: which capture uh you know the inputs to
05:56: the real world problems so
05:58: certainly the the cash flows they're
06:00: getting from their wages
06:02: we can measure that we can think about
06:05: their
06:06: how much they're saving in terms of a
06:07: savings rate of what you know what
06:09: percentage
06:10: of their wages are they saving in each
06:13: year
06:14: um and as an input into the investment
06:16: process we can think about the interest
06:18: rates
06:18: that this person is going to earn on
06:21: their investments
06:23: and then you know coming out as a result
06:25: of the model
06:26: we have the future value of the cash
06:29: flows which again
06:30: represents you know how much cash this
06:32: person is going to have
06:34: when they retire or you know if we have
06:37: a specific
06:39: amount of desired cash uh then we could
06:42: figure out how long would it take for
06:44: this person
06:45: to get enough cash to retire
06:49: so you know the model is all about uh
06:52: you know approximating some
06:54: real world process and here is a
06:56: relatively simple
06:58: real world process but the same thing
07:00: can be applied to
07:02: more complicated uh situations
07:05: so definitely more complicated is
07:08: thinking about how to value
07:09: a company especially like a large you
07:12: know multinational corporation with all
07:14: these different business segments
07:17: certainly it is complex to come up with
07:21: a
07:22: relatively accurate valuation for that
07:24: company
07:27: so the real world situation that we're
07:30: modeling
07:30: is actually the business operations
07:34: of this uh you know whatever company
07:36: that we're valuing
07:38: so if we think about uh say microsoft
07:41: uh you know microsoft is primarily in
07:44: the business of
07:45: selling software right um and so
07:50: uh you know the inputs to uh
07:53: the real world situation here is that
07:55: microsoft is creating that
07:57: software and then the process uh
08:00: is they sell that software and the
08:02: output is that
08:04: they get uh the cash proceeds um
08:07: from selling that and that leads to
08:11: the stock reaching a certain price as
08:15: a result so then if we're going to think
08:19: about
08:19: building a model for evaluation of
08:21: microsoft
08:23: uh what would we think about so you know
08:25: again
08:26: for the inputs on the model it has to be
08:29: what we can actually
08:30: tangibly measure um or at least
08:33: uh you know guess at so these
08:36: you know have to be numeric values for
08:40: the most part
08:41: um and so you know microsoft creating
08:44: software is
08:45: is quite a big thing um we would have to
08:48: come up with more tangible things that
08:50: go into creating that software
08:54: so you know how much are they having to
08:56: pay in salaries
08:57: uh for the software developers to
08:59: produce that how much
09:01: uh are they putting into marketing to
09:03: sell the product
09:04: um you know how much did they pay to
09:07: finance the company to get the capital
09:09: to
09:10: to create this software in the first
09:12: place
09:13: uh you know how much is uh the
09:16: the sales growing um all these kinds of
09:19: things
09:20: uh would go into the inputs of this
09:22: valuation model
09:25: and then the model um would be capturing
09:28: um you know the business um
09:31: so it can be you know from a couple
09:34: different perspectives you can have more
09:36: of an operational
09:37: model where you try to actually dig into
09:39: all their operations and come up with
09:41: cash flows and everything or it can be
09:43: just you know based on the
09:44: top line uh financials it's a more
09:47: aggregated view
09:49: but ultimately the model is trying to
09:51: capture uh the business process
09:54: that uh microsoft goes through to
09:57: generate value
09:58: uh for its shareholders and then the
10:01: outputs from our model are going to be
10:02: the stock price and
10:04: and returns on that stock
10:07: so regardless of the complexity of the
10:10: underlying
10:11: problem that you're dealing with you can
10:13: ultimately boil it down to
10:16: their inputs their outputs and there's
10:17: some process and then when we go to
10:19: model that we think about what tangibly
10:21: can we measure
10:22: in those inputs and outputs and how do
10:25: we approximate this process
10:27: uh with a model that's the general
10:31: idea so that
10:34: concludes thinking about what is
10:37: a model and basically what we are
10:40: talking in this class
10:41: so next time we'll come back to talk
10:43: about the tools and skills
10:46: that we're going to cover as part of
10:48: this course
10:50: so thanks for listening and see you next
10:55: time

00:03: hey everyone
00:04: nick dear british here teaching you
00:06: financial modeling
00:07: and today i'm here to talk to you about
00:10: what are the tools and skills that we're
00:12: using in this class
00:13: you know python and excel why do
00:16: i choose them for the course and why do
00:19: i think that they're the
00:20: most appropriate tools and skills to
00:23: learn for financial modeling
00:26: so let's go ahead and jump in
00:29: so um
00:33: you know a lot of people you know do
00:35: financial modeling
00:36: primarily just in excel but i've chosen
00:39: to
00:40: uh use python in this class so why
00:44: are we going after python as
00:47: a tool to use so
00:51: if you want to do anything more advanced
00:54: in financial modeling you have to go to
00:57: programming there's no way
01:00: to be able to implement some of the
01:02: intricacies
01:04: of the detailed situations
01:07: of the models that we're building
01:09: without
01:10: going to programming so
01:13: you know it becomes excel alone
01:17: is not going to be enough you have to go
01:19: to some kind of programming
01:21: now excel does have its built-in
01:24: language vba so that's another option
01:28: but we'll talk about why i believe
01:31: python is a better choice to learn
01:33: as a language for your financial
01:36: modeling
01:37: so why why python out of programming
01:41: languages
01:42: so i think this this comic here
01:46: illustrates things uh well about kind of
01:49: the general
01:51: feeling about python in the programming
01:53: community
01:55: so you can pause the video to read that
01:58: but the main idea here is just that uh
02:02: it's it's pretty easy to pick up python
02:04: it doesn't require
02:06: a lot of structure a lot of knowledge
02:10: of you know computer science concepts or
02:12: anything like that to be able to
02:15: complete some simple tasks you just need
02:17: very little code to do that very little
02:19: structure
02:21: so that makes it uh quite a bit easier
02:25: for people to get started with
02:26: python than with other programming
02:29: languages
02:32: and this comic also you know kind of
02:34: highlights the flexibility and power
02:36: of python that there's this rich
02:39: ecosystem
02:40: of open source packages that you can use
02:44: which kind of supercharge the
02:46: capabilities
02:47: of python so it's not like excel where
02:50: you know microsoft
02:51: develops excel so it's whatever features
02:53: microsoft
02:55: is able to build into excel are the ones
02:57: that you can use
02:58: uh you know there are plugins but you
03:00: know there's not a whole ton of them
03:03: whereas python at its core is an open
03:06: source
03:06: language built by the community um
03:10: and then there's also a community of
03:13: uh packages around python which extend
03:16: the capabilities of
03:18: python and today there are over 250 000
03:21: different packages that you can add to
03:23: python uh
03:25: just free and open source out there
03:27: waiting for you to use
03:28: uh and so there's a ton of stuff that
03:31: you can do the flexibility
03:33: is enormous with python the flexibility
03:36: and the capabilities
03:40: now why for finance specifically
03:45: you know again um i kind of hit on it
03:48: already but out of the
03:49: mainstream programming languages that
03:51: are out there it's definitely the
03:53: easiest to learn
03:54: because of that uh lack of structure
03:58: um needed and a lot of cases
04:01: um and it's already uh you know growing
04:04: very quickly in the finance
04:06: industry um it's definitely uh becoming
04:09: the
04:10: the top uh programming language in the
04:13: financial industry
04:14: and definitely for financial modelers
04:17: so you won't be alone when you learn
04:20: python
04:22: and you know i mentioned these 250 000
04:25: packages that are out there that make
04:27: your life
04:28: so much easier to do all sorts of
04:29: different things uh
04:31: you know learning python is going to be
04:33: useful for you
04:34: you know for many things outside of
04:37: this course and financial modeling
04:41: so you know some of the other categories
04:43: where python definitely excels
04:45: are you know data science doing all
04:47: kinds of analysis
04:49: and visualization of data
04:52: algorithmic training you can build your
04:56: own
04:56: little trading bots which execute uh
04:59: strategies for you
05:01: uh scripting um so you know completing
05:04: some kind of automation kind of tasks of
05:06: just making your life easier
05:08: um devices
05:12: you know like internet of things iot
05:14: devices
05:15: python is on those as well
05:20: web development certainly python is used
05:23: extensively
05:25: in the web um you know some
05:28: uh very popular um sites are built on it
05:32: you know such as you know instagram is a
05:34: good example
05:35: uh built totally on python
05:39: web scraping is about you know
05:42: collecting
05:42: data from websites in an automated
05:45: fashion
05:46: so you know a website may have 3000
05:48: different pages that you want to collect
05:49: data from
05:50: you can automate all of that with python
05:55: and actually even these uh slides
05:58: that you're viewing right now i
06:00: generated with
06:01: python and all of the um
06:06: like documents and everything in the
06:08: course i'm generating all of those with
06:10: python um and this is a very
06:14: uh small list compared to the
06:15: capabilities uh these were just some of
06:17: the categories that i wanted to
06:19: highlight
06:20: but the high level is you know you learn
06:21: python and you're able to go
06:24: in all these different directions should
06:26: you want to go to them
06:28: um versus you know we'll talk more about
06:31: vba but that
06:32: kind of constrains the amount of choices
06:34: that you have
06:38: another great thing about python is it
06:40: is the fastest
06:41: growing programming language and
06:43: depending on the ranking
06:46: you know it's between one and three on
06:49: all of the rankings
06:50: um and within the next couple of years
06:53: it's gonna be number one
06:54: on all the rankings because it is
06:55: growing so much faster
06:57: than the other top programming languages
07:01: so um you're not alone
07:04: with adopting python so many people are
07:07: doing it around the world
07:08: and it will be the world's number one
07:11: programming language
07:12: very soon by all measures
07:16: so um some more advantages of python
07:20: i mentioned it already but it's
07:21: completely open source
07:23: that means it's totally free uh that
07:26: means that
07:28: even the code that creates python that
07:31: creates the language as well as creates
07:34: uh
07:34: all these these packages this extra
07:36: functionality that we're gonna add to
07:38: python
07:39: all that source code is just out there
07:41: and able for you to use so if you really
07:45: like a project a package that's out
07:47: there
07:48: but you really wish it had some other
07:50: feature you know you can potentially
07:52: just take that code and you can add that
07:54: feature
07:55: yourself because that's all completely
07:57: free and open
07:58: and you can contribute that back to the
08:00: library and maybe they'll add it as a
08:02: feature for everybody so
08:04: you know it's a whole community of
08:06: people that come together
08:07: uh kind of in their free time to develop
08:11: these tools that everybody in the world
08:13: can use
08:16: and one of these biggest focuses of
08:19: python language is to make it as
08:22: readable as
08:23: possible so it's almost
08:26: like just reading english uh there's not
08:29: a whole lot of special symbols or crazy
08:32: uh characters or anything in there um
08:36: it uses uh white space rather than um
08:40: you know curly braces and things like
08:42: that and so
08:44: it's it looks very clean it's it's easy
08:46: even if you're not
08:48: a python programmer to kind of read it
08:50: and understand what's going on
08:53: so the readability is huge for beginners
08:56: as well as
08:57: uh you know uh just you know saving time
09:00: over time being able to
09:01: look at someone's code and just
09:03: understand it fairly easily
09:06: um and you know going along with the
09:09: flexibility
09:10: of python so it's uh
09:14: there are a lot of features in python
09:17: um and so it can grow to the point where
09:21: you know a lot of experts do use python
09:24: as their primary language
09:26: because it has all the kind of features
09:29: that they would want
09:30: you know being programming experts but
09:32: you're not required to use
09:34: all of those features you can use as
09:36: little as you want to use
09:38: so you know it's easy for beginners to
09:41: pick it up you don't have to have
09:43: all this structure that is important
09:46: for you know building large scale
09:48: applications you can just write a couple
09:50: lines of code and get something done
09:53: but it does allow you to also use all
09:55: that structure if you want to go
09:57: and build something big so
10:01: you know you can kind of start small
10:02: with it and over time you can add more
10:04: and more things
10:06: which improve your process
10:11: and another nice
10:15: feature of python this is by way of one
10:18: of these packages that we can add to
10:20: extend the functionality
10:22: so there's a deep integration of python
10:25: with excel
10:27: we can run python code and make excel do
10:30: things
10:31: and we can run python code from within
10:33: excel
10:35: so you know the applications of that
10:38: will become much more clear as we get
10:39: into the course
10:42: but you know basically you can use
10:45: python instead of vba you don't even
10:47: have to worry about vba you can do all
10:49: the same things
10:50: as vba um
10:53: but it's also python you can use it for
10:55: other things after you've learned it
11:00: so what's the drawback of
11:03: using python um well here i think is the
11:07: biggest
11:08: drawback uh and this is not unique to
11:10: python it is
11:12: uh just the nature of going to
11:14: programming over just using basic
11:16: excel that it's inherently
11:20: uh text-based and
11:23: um inherently there's no
11:26: like interface to using it right like
11:29: you type
11:30: some text and you might get some text as
11:34: the output
11:36: so you know you just look here you stare
11:39: at the terminal
11:39: that's probably intimidating for a lot
11:42: of people
11:43: um but this is kind of where
11:46: python lives as well as you know any
11:49: other
11:49: programming language now we are going to
11:52: talk about
11:53: uh things that have built been built on
11:55: top of python that are going to make it
11:56: nicer to use
11:58: we're not actually really going to be
11:59: working in a terminal
12:01: primarily we're going to use uh what's
12:04: called
12:04: jupiter as a project which gives a nice
12:08: interface to python
12:11: but still at its core you know it's just
12:13: a text thing
12:15: um and so you know that can be
12:18: a bit of a shell shock for people that
12:20: are used to just using
12:22: excel you know you just hit okay i gotta
12:25: type text now to do things
12:27: what is going on
12:32: so no graphical interfaces is basically
12:36: what i'm getting at there now i say by
12:38: default because there are
12:40: uh interfaces that you can add and use
12:42: and we're going to use jupiter
12:45: but by default you know you're just
12:46: working with text
12:49: another disadvantage is you know for the
12:51: most part
12:52: people know excel uh you know you can
12:55: kind of expect
12:56: that pretty much anyone you're working
12:59: with in a business environment is gonna
13:00: have
13:01: a basic working knowledge of excel at a
13:03: minimum
13:04: uh but that is not there for python yet
13:08: i think we're going in that direction i
13:10: think more and more people
13:11: are going to uh know how to program in
13:14: python
13:15: over time uh but as of now you know
13:18: still it's a minority of people who
13:20: actually know how to use python
13:23: um so that can make it a little more
13:24: difficult to work with other people
13:26: if you've uh you know programmed your
13:28: model in python
13:29: and then your boss says hey you two need
13:31: to work together
13:32: uh but this other person doesn't know
13:34: any python that's going to be
13:37: difficult for them to come and work on
13:39: your model
13:42: and you know somewhat going along with
13:45: well
13:46: you know you've just got text to work
13:48: with it can be a little bit harder
13:50: to get started on a project uh you know
13:52: it's definitely more open-ended
13:55: you can just sit there staring at the
13:58: blank
13:58: editor like what what do i type now
14:01: where do i go first
14:03: um but you know that just comes with
14:06: experience the more that you work with
14:08: it the more that you're gonna know
14:10: uh what you should do so definitely
14:13: by the end of this course uh you'll not
14:16: be feeling that pain so much and you'll
14:18: have a good idea
14:19: how to get started on any given project
14:23: so why not use vba i mean that's already
14:27: built in
14:28: to excel uh you know we're already
14:31: talking about how everybody uses excel
14:33: so you know why not just use the thing
14:36: that's already there
14:37: uh to do our programming right um
14:40: so the biggest thing i think here is
14:42: highlighted by this picture
14:44: or maybe the second biggest thing is
14:46: that uh
14:48: it's kind of hard to read it's not it
14:50: doesn't have nearly as much of a focus
14:53: on readability as python does
14:56: um you know just looking at this code is
15:00: kind of hard to understand
15:02: what's going on
15:05: um so that readability you know it's
15:08: going to cost
15:08: you time in terms of you know if you
15:11: have to work on someone else's code
15:13: you've got to go and understand
15:14: all this stuff um and it's even going to
15:17: be difficult for yourself when you come
15:19: back to an old project and you think
15:20: what is going on here now i have to
15:23: understand my code again it's just more
15:26: difficult to read
15:29: but you know just as important maybe
15:31: more important is that
15:33: you know vba you can automate all sorts
15:35: of stuff in microsoft office so it's not
15:37: just excel you can do word you can do
15:39: bower
15:40: powerpoint but it's microsoft office
15:44: only right you're not going to go
15:47: and build instagram with vba you're not
15:50: going to go and do actual data science
15:52: and analysis
15:53: and all these things with vba unless you
15:56: you know really want to torture yourself
15:58: [Music]
15:59: and so by learning python
16:02: which is even easier to learn than vba
16:05: then
16:06: you have so many more capabilities than
16:08: if you had just learned vba
16:09: instead um
16:13: and you know we mentioned a little bit
16:15: earlier that python can basically be a
16:17: vba
16:18: replacement in excel um so python can do
16:21: everything that vba can
16:23: it even you know has uh a package which
16:27: you know gives all the functionality of
16:28: vba
16:29: but it can do so much more and vva
16:32: can just do vba so why spend time
16:36: focusing on you know just a subset of
16:39: what python can do and spend even longer
16:41: to learn that subset as vba when you
16:44: could just learn python
16:48: instead
16:50: and then you know why is this not just
16:53: 100 python course why are we using excel
16:56: as well um so
16:59: excel is everywhere everyone knows how
17:02: to use excel as i said
17:04: and you know so much of the data that
17:06: you would work with
17:07: and any organization is stored in excel
17:10: spreadsheets so it's kind of hard to get
17:13: away
17:14: from excel and it's ubiquitous
17:18: um so and then there are some you know
17:22: advantages
17:22: um just on the nature of the tool um
17:26: you know it's a very visual experience
17:28: using excel right you do something and
17:30: you
17:30: see the result of that immediately
17:35: so that can make it quicker to
17:38: kind of learn how to use it and do some
17:41: simple things
17:43: but you know when you want to go and do
17:45: something complicated
17:47: then uh you start to run into
17:49: difficulties and excel
17:53: and you know looking at a particular
17:58: value in excel is really easy you just
18:00: click on the cell
18:01: right um and you know you can trace back
18:05: the
18:05: the dependencies of that cell um and
18:08: everything
18:10: so it's pretty easy to look at and
18:12: figure out what's going on in one
18:13: particular
18:14: area of an excel spreadsheet
18:21: but there are certainly disadvantages
18:25: with excel so
18:28: you know those who have built
18:30: complicated things in excel
18:31: before probably uh you know
18:35: this this picture probably dredges up
18:37: some some feelings for you
18:39: um that you know as you add more and
18:42: more
18:42: uh worksheets that are all linked
18:44: together it all just
18:46: starts to become complicated and
18:47: unwieldy uh you might have multiple
18:49: workbooks that are referencing each
18:51: other
18:52: um it's not clear you know
18:55: where everything starts where everything
18:56: ends everything is just kind of linked
18:58: together in various ways
19:00: and so it's really hard to understand
19:03: you know i have a particular number here
19:06: what is the whole process that got me
19:08: there was it you know reference from
19:10: another calculation which referenced the
19:12: data from another workbook
19:13: um which you know so it can be
19:17: difficult you know at a high level view
19:20: to understand
19:21: what's going on in a complicated excel
19:24: model um
19:27: and part of uh why that's the case
19:30: is because you know the the code
19:33: the formulas that you're putting into
19:35: the cells
19:37: and the view the kind of the the what
19:40: you're seeing the output
19:41: of the cells uh it's all mixed together
19:44: right you see
19:45: just the output normally you only see
19:47: the code or formula
19:48: if you click into a cell so that
19:52: the the code is inherently hidden and
19:54: you
19:55: you kind of have to go digging to
19:56: understand
19:58: where a particular value came from
20:03: but it becomes even worse as soon as you
20:05: start
20:06: layering in vba uh because
20:09: it's not that bad to you know look at a
20:12: cell
20:13: look at what cells it's referencing and
20:15: kind of just follow back
20:18: until you get to the the raw inputs and
20:20: then you can understand the whole
20:21: process
20:22: that produce this output uh but as soon
20:25: as you layer on vba
20:26: that's just this whole other separate
20:28: thing just going on in the background
20:30: and you're only going to know that vba
20:33: is going on at all if you
20:34: you know go into the vba editor and then
20:37: you start to read that code
20:39: um and then look at the code and
20:41: understand where it's being applied
20:42: in relation to all the cell formulas and
20:45: everything
20:46: um and so it's kind of this invisible
20:48: background thing that can do things that
20:50: you're not expecting
20:52: um and so having both the cell formulas
20:55: and vba
20:57: going on in the background can make it
20:59: very confusing to understand
21:01: the exact process that goes from inputs
21:03: to outputs
21:06: um it's also quite easy to
21:09: make a certain type of mistake in excel
21:13: you know maybe because you know so much
21:15: of excel uh
21:16: you know you're gonna like drag to auto
21:18: complete things
21:19: to do the same thing for every row or
21:21: every column
21:22: um and then you know you accidentally
21:25: just type something in one of those
21:27: cells
21:28: and it overwrites what you had there um
21:31: and it can be really easy to miss that
21:33: especially if you have thousands of
21:34: cells
21:35: going on there's just one of them which
21:37: is different
21:38: um and so those kinds of mistakes can be
21:41: very very difficult to track down
21:46: whereas it would be harder to make that
21:48: kind of mistake
21:49: in a programming language as things are
21:51: generally
21:53: more generally applied
21:57: um and certain tasks uh which
22:00: um are really simple to do in python
22:03: um become very complex
22:06: and excel and we'll definitely look at
22:09: examples of those
22:10: in the course and that is you know the
22:12: primary uh
22:13: reason is that of going to a programming
22:16: language is excel is just not
22:18: going to be able to accomplish
22:20: everything that we want to do so you got
22:21: to go to a programming language
22:24: vba or some other language and we're
22:26: choosing python
22:28: for all of the reasons mentioned
22:32: so that's uh the tools and skills that
22:36: we are covering in this course and why
22:38: we're focusing on these rather than
22:41: some of the other options that are out
22:43: there
22:45: so next time we will look at how to get
22:48: python installed
22:49: on your machine and that will conclude
22:51: our introduction
22:52: segment of the course so thanks for
22:55: listening
22:56: and see you next time

00:03: hey everyone
00:04: nick dear burtis here teaching you
00:06: financial modeling today we're going to
00:08: talk about
00:08: installing python on your computer
00:13: so should be a quick video
00:16: just looking at the install process for
00:19: python
00:21: and so there's a lot of different ways
00:23: to actually install python
00:26: being that it's open source but the way
00:29: that i'm
00:30: recommending for the course is to use
00:32: anaconda
00:33: so anaconda is a package distribution
00:36: that comes along
00:38: with python and it gives you
00:42: already pre-included already downloaded
00:44: and set up
00:45: uh most of the additional packages
00:49: that you would want to add to python
00:52: to be able to do financial modeling and
00:54: general data analytics
00:57: so it's a distribution focused on
00:59: scientific computing and includes
01:01: plenty of packages which help out with
01:04: those
01:04: objectives so you just
01:08: go to the anaconda website here
01:13: and that would bring you to this kind of
01:15: page
01:17: and take you to this page
01:22: and so you just click download
01:26: and then it'll bring you to the where
01:28: you can download the installer
01:30: so make sure that you pick the one for
01:32: your appropriate system
01:33: uh python38 here um
01:37: and i already went ahead and downloaded
01:40: this so we wouldn't have to wait
01:42: um so then you can go to run the
01:45: installer
01:47: and you know this is gonna look slightly
01:48: different on a mac but
01:50: same basic process here so you just go
01:54: through the installer
01:55: and you know most the stuff you can just
01:57: go through the defaults are going to be
01:59: fine
02:00: um you know i already
02:04: have it installed so i had to pick a
02:07: different path there but you won't have
02:08: to do that
02:11: but the one thing that you do have to do
02:15: which
02:16: [Music]
02:18: is not there by default is you do have
02:20: to check this option here add anaconda 3
02:23: to my path
02:25: environment variable and you know the
02:28: installer
02:29: generally does not recommend doing this
02:31: but i promise you it's not going to
02:33: break anything
02:34: as far as the course it's going to be
02:36: fine um
02:38: and it is necessary for later on in the
02:41: course when we're going to do the
02:42: integration between python and excel
02:45: so make sure that you have this checked
02:47: as you go through
02:49: otherwise you're probably going to need
02:52: to actually
02:53: reinstall python later on in the course
02:55: once we need it
02:57: and that's you know just an annoying
02:59: extra step to go through
03:00: whereas you check this option here then
03:03: you
03:04: just install python once you're going to
03:05: have it for as long as you have that
03:07: computer
03:08: and you can use it for the course and
03:10: for anything else that you want to do
03:13: so then you know with both these boxes
03:15: checked you would just go and hit
03:17: install uh that's that's where i'm gonna
03:20: finish here on this because i already
03:22: have it installed on my machine
03:25: but then you know it would just you know
03:26: do the regular kind of progress bar
03:28: across the screen and then when that
03:29: finishes
03:30: you'll have python installed so how can
03:34: you
03:34: check to make sure that you have python
03:37: installed
03:39: so on windows
03:42: so you can go into the windows menu
03:46: and type cmd and you're going to see
03:48: command prompt
03:49: come up there so if you click
03:53: command prompt then you should be able
03:55: to type python
03:57: in the command prompt and then that
04:00: should get you to this kind of screen
04:02: here where it says python and the
04:05: version it should say 3.8 point
04:07: something for you
04:10: and it's going to have these three lines
04:12: or three um
04:14: greater than signs um here and you can
04:17: type things
04:18: on this prompt so if you get to this
04:22: um then the install
04:25: has worked correctly if you're on a mac
04:28: uh
04:28: same exact process only instead of
04:31: searching cmd
04:32: you're going to search for terminal in
04:34: your app search on
04:36: mac so it's just instead of being called
04:38: command prompt it's called terminal
04:40: but then once you get into it you're
04:42: again just going to type python
04:44: hit enter and then it should take you to
04:46: this kind of output here where it shows
04:48: the python version
04:49: as well as this prompt that you see here
04:53: so if you can do that then you have
04:55: python installed properly
04:57: and next time we will look
05:01: at how we can actually start applying
05:04: some of this
05:05: um we will start to get into
05:08: the basics of excel and python
05:14: so that concludes how to install python
05:17: so thanks for listening and see you next
05:22: time