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Hi and welcome!
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We at 365 Data Science specialize in data science trainings.
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We post videos weekly, so you can master indispensable skills for free!
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Alright, let’s get started!
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All right!
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What do you need to know about programming if you are just getting started?
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We all have to deal with certain tasks in our daily lives.
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Many we can solve on our own, while others, especially the ones that are more complicated,
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can be solved with the help of a computer.
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Assume you have defined a problem that must be solved and you know the steps that must
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be taken to solve it.
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Even if you could structure your logic perfectly and type a brilliant solution in English,
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the computer will not understand it, as it understands 1s and 0s only.
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No other symbols.
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Similar to a light switch – it recognizes two phases – on and off.
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To communicate a real-life problem to the computer, you need to create a specific type
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of text, called a source code or a human readable code, that software can read and then process
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to the computer in 0s and 1s.
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A program is a sequence of instructions that designate how to execute a computation.
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Therefore, the formal definition of programming is the following:
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Taking a task and writing it down in a programming language that the computer can understand
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and execute.
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You need not be a geek or a computer scientist to program.
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Actually, the subject of computer science is not the study of programming; these are
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different things, and this can confuse beginners.
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Computer science is about understanding what computers can do.
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Programming, instead, is the activity of telling computers to do something for us.
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Think about the world we live in today.
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There are more than a thousand programming languages out there, and each language is
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designed for carrying out specific tasks.
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So, depending on the sphere to which your problem applies, only some languages can be
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of good use.
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For instance, PHP is good for web programming but is not suitable for programming devices.
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C plus plus can definitely help you with the latter, while Python and R are some of the
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favorite tools of data scientists and people from the finance industry.
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When you meet an experienced programmer, don’t think he can programme in all languages out
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there.
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Instead, it is likely he can work with one or may be a few languages, but he has mastered
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them well.
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Right.
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But how does somebody become good at programming?
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First, programming requires problem-solving skills and involves abstract thinking.
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You are supposed to understand your task perfectly, and then break it down into a sequence of
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instructions (or smaller computational steps) that the machine can execute.
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For example, John is asked by his boss to do the following: create a program that adds
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10 to any number his boss inputs with the keyboard.
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The correct reasoning would be: if x is the unknown provided, we need an output of x +
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10.
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After you have created these steps, with the help of a programming language, you will type
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in beautifully organized lines of code.
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So, the second crucial thing to develop is mechanistic thinking.
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Unfortunately, computers can only execute what you ask them to do, and they won’t
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understand what you imply by the instructions you have provided.
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They will simply compute the code, without interpreting your output.
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Fortunately, we can do that, though.
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Humans can understand and interpret code instructions and adjust it whenever necessary.
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And this is why a solid knowledge about the syntax of a programming language and the ability
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to understand computer code is of paramount importance – it will positively affect your
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thinking process, allowing you to break down your problem into parts the computer can execute.
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In the example, we provided above, John must think of the following subtasks: first, he
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must define a function that takes x as an argument and then returns as an output a new
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variable equal to x + 10.
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This is how this problem can be solved.
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Regardless of the problem you are facing or the programming language you are using, your
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coding style is crucial!
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Remember that.
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Having three lines of code is straightforward to understand.
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However, in practice, you will likely work with hundreds of lines of code that must be
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sent to other people.
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If your work is difficult to read, unnecessarily complicated, full of variables’ names, conveying
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no meaning, it will be poorly received by other programmers.
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Therefore, throughout this course, we will pay attention to the best practices that will
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help you organize your code!
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Programming challenges are great as they develop your mechanistic thinking and problem-solving
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abilities.
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This involves formulating problems, breaking them down into meaningful steps, and communicating
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these steps to the computer in an organized way.
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Although it may be new to some of you, Python has been on the programming stage for over
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two decades.
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There are two main reasons you should learn Python.
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First, it has several technical advantages compared to other programming languages.
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And second, its practical application covers several industries.
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It is a powerful computational tool when we have to solve complicated tasks in the fields
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of finance, econometrics, economics, data science, and machine learning.
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Therefore, it is a perfect stepping stone for somebody who learns how to code and is
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determined to pursue a career as a data scientist.
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Here’s a slightly more technical description of Python.
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It is an open-source, general-purpose high-level programming language.
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Let’s break this definition into several pieces and try to understand each of these
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attributes.
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Open-source software (OSS): Open-source means it is free.
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Python has a large and active scientific community with access to the software’s source code
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and contributes to its continuous development and upgrading, depending on users’ needs.
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This is the main reason Python is cross-platform – it is available for all major operating
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systems: Windows, Mac, and Linux.
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The benefit of it is Python can be quickly applied anywhere.
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Domain-specific languages, like MATLAB and SAS, also used for solving financial and econometric
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tasks, are paid.
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This plays a role in a language’s popularity.
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General-purpose: Yes, we will dig deeper in one of Python’s
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specific applications – analysis of financial data.
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However, you should know there is a broad set of fields where it could be applied.
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For instance, Python can be used for web programming through the Django framework.
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Although this is beyond the scope of this course, you should be aware the wide scope
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of application and the interoperability with other programming languages could be an explanation
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why some large organizations have chosen Python as their main programming language.
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High-level: This is slightly more technical.
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Broadly speaking, computers can run programs written in low-level languages only, also
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called machine languages.
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So, a program written in a high-level language must be first interpreted into a low-level
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language before it can be executed.
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This process takes time.
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There is specialized software and applications that will do this interpretation for you.
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Nevertheless, the advantages of using a high-level language are huge!
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It is difficult to code and understand low-level programming languages.
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They are too technical.
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High-level languages employ syntax a lot closer to human logic, which makes the language easier
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to learn and implement.
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It allows the programmer to focus on the task at hand, instead of trying to figure out unreadable
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lines of code.
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To summarize the technical advantages that make Python a powerful programming language,
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often preferred over other programming languages, we can say the following:
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- It is free and constantly updated; - It can be used in multiple domains;
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- It does not require too much time to process calculations and has an intuitive syntax that
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allows for complex quantitative computations.
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What we’ve said so far demonstrates Python’s enormous practical applicability.
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It is one of the most popular programming languages in several fields.
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One of them is the world of finance.
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Just consider, today, banks and financial institutions spend more on technology than
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any other industry!
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Thousands of developers work in financial institutions to maintain existing software
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and build new programs.
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There is a growing demand for people who have solid knowledge about the world of finance
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and Python programming.
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It is clear we are living in the era of Big Data.
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People in different disciplines - economics, finance, computer science, marketing, and
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many more can retrieve huge amounts of data.
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We can talk about Big Data when we have millions of observations.
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In such situations, the computational capabilities of traditional data processing applications,
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like Microsoft Excel, become insufficient.
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We need a more powerful tool to tackle Big Data in more or less the same way, regardless
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of the field of application.
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Python is perfect for these situations, as it gives us flexibility.
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To conclude, Python’s popularity lies on two main pillars.
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One is that it is an easy-to-learn programming language designed to be highly readable, with
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a syntax quite clear and intuitive.
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And the second reason is its user-friendliness does not take away from its strength.
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Python can execute a variety of complex computations and is one of the most powerful programming
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languages preferred by specialists.
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For this course, you must install both Python and Jupyter on your computer.
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If you have them, you can still complete this lecture, because we will say a few interesting
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things about Jupyter.
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So, why isn’t there just one software application, called “Python”, you can install on your
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computer that is automatically being updated and that runs everything smoothly?
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I am sorry to tell you, but it’s not the case.
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We have to deal with reality.
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First, Python is a programming language.
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It can allow you to communicate with the computer.
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To do that, you’ll need the help of a specific software or an application.
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Namely, the Jupyter Notebook App, which is more often called Jupyter, can help us do
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that.
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It is a server-client application that allows you to edit your code through a web browser.
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Consider the following graph.
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All units represent different software.
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On one side, you have several language kernels.
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These are programs designed to read and execute code in a specific programming language, like
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Python, R, or Julia.
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The Jupyter installation always comes with an installed Python kernel, and the other
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kernels can be installed additionally.
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On the other side, you have various types of interfaces, where you can write code.
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They represent the clients.
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An example of such a client is the web browser.
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The Jupyter server provides the environment where a client is matched with a corresponding
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languages kernel.
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In our case, we will focus on Python, and a web browser as a client, or as an interactive
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shell.
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Your work will be stored on a notebook document, and since we will be strictly using the Python
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language, it will be called “IPython Notebook” file, with the file format “dot ipynb”.
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Having said all that, we can explain why Jupyter is used in so many large corporations, like
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Google, Microsoft, and IBM.
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For its design, it is well-suited to demonstrations of programming concepts and training.
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First, in large corporations, solving a particular task could require coding in a few languages,
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say Python, R, Julia, or PHP.
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Instead of installing different interfaces for each language kernel you need, Jupyter
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allows you to use the same structure of the notebook type of file.
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Simply, each notebook you create will connect to the language kernel you request.
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Consider also, this file can be easily stored locally or on a remote server.
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Therefore, Jupyter facilitates the communication between teams in a corporation tremendously.
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Second, Jupyter is not a text editor that opens a new window every time you execute
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a different part of your code, as is the case with some other software applications.
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In the same file, you can have pure text that can communicate a message to the reader, computer
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code like Python, and output containing rich text, like equations, figures, graphs, pictures,
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and others.
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This simplifies the process of the work flow immensely, and Jupyter Notebook is increasingly
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preferred over other software packages.
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That’s why we’ll use it too The next step would be to install Anaconda
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- a software package that contains both the Python programming language and the Jupyter
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Notebook App.
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There are various ways to install Python on your computer.
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But especially for new users, it is highly recommended to opt for Anaconda.
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It will install, not only Python, but also the Jupyter Notebook App and many scientific
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computing and data science packages.
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Let’s open www.continuum.io and click on the “Download Anaconda” button in the
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home page.
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You have to pick one of the three operating systems – Windows, Mac, or Linux.
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I will show you how to install Anaconda on Windows, but the procedure is identical if
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you are going to use the Mac or Linux version.
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Now, you must choose the best among the four provided options.
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Do you need a 2.7 or a 3.6 version?
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And then there is a 32-bit or a 64-bit version, depending on the Windows you have installed.
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I know it sounds strange to maintain not one but two versions of a single program.
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For this course, the differences between Python 2.7 and 3.6 will be almost insignificant.
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To prove this is the case, we have attached both Python 2 and Python 3 notebook files
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to all videos throughout the course where providing lecture code was relevant.
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So, which version should you install?
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Given that we have recorded the entire course on Python 2, install this version if you want
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to use notebook files containing the same code as the one displayed in the videos.
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If, instead, you are fine with dealing with just a couple of small syntactical changes,
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or there’s some other reason why you prefer to work with Python 3, then install that version,
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and later use the notebook files stored in the “Python 3” folder in the resources
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sections of the lectures.
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For example, if you access the additional material to the lecture about “Variables”,
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and you click on the folder called “Python 2”, you will see notebook files that will
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run on Python 2 directly.
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In the same manner, if you go back and select the “Python 3” folder, you will only encounter
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files that are suitable for working with Python 3.
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Remember that we have kept this principle throughout.
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It is valid also for the second, financial part of the course, which can be taken if
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you have any of the Python versions.
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So, it’s up to you which one to install now.
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Having said that, let’s go back to the installation process.
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When you choose between a 32 or 64-bit version, you can simply check your operating system.
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Newer computers are surely running with a 64-bit processors, but if you would like to
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verify this before you begin, you could open your control panel menu from the start window.
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Then, select the “System” icon and check the information referring to the “System
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Type”.
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In my case, it is 64-bit, so when I go back to the Anaconda website, I will select the
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64-bit 2.7 Python version.
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Then, we must find a directory where we want to install the distribution and press “Save”.
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While waiting to complete the download, you can decide whether to leave your e-mail to
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Continuum.
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This is not a necessary step, so you could also select “No Thanks” and you are good
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to go.
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When the download has finished, please double-click on the file to run the application.
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What follows is nothing different from the standard windows installer.
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Agree and press “Next” until you have the chance to specify a destination folder.
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You could do this after selecting the “Browse” button.
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When satisfied with the indicated directory, click “Next” one more time.
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We suggest you tick both advanced options.
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The first option will automate more complicated processes, while the second will register
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Anaconda as a default Python on your computer if you have not installed some other package.
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Finally, click “Install” and proceed until you see the window where you can finalize
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the installation.
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We will not use “Anaconda Cloud” just yet, so I’ll untick the box.
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What you just installed is the whole Anaconda Distribution – the Python language, a text
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editor, many applications, and packages.
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You also have the Jupyter Notebook App.
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You can open the start menu and select the respective icon from there.
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A new window will pop up.
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It will take a few seconds for the App to load.
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Once this is done, your web browser will open a new tab with the Jupyter Dashboard.
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Ok, so in this lesson, we’ll do a quick tour of the Jupyter dashboard.
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As soon as you load the notebook, the Jupyter dashboard opens.
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Each file and directory has a check box next to it.
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By ticking and unticking an item, you could manipulate the respective object – that
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means you can duplicate or shutdown a running file.
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In addition, you can rename and delete folders.
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The selection menu allows you to select all the files in the console of the same type
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by expanding this button.
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For example, you could mark all the folders or all the
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running files.
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In addition, you can check this little box that will directly select all the items on
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the page.
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The logic regarding the directories management is the same as the one of an operation system
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– files can be grouped into folders, and folders can contain other folders.
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From the “Upload” button in the top-right corner, you can upload a notebook into the
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directory you are in.
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The standard explorer box opens and when you select a file, you can click on “Open”,
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and it will immediately appear in your directory.
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Finally, you can expand the “New” button.
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From the list that falls, you will most likely need to create a new text file, a new folder,
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or a new notebook file.
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A notebook file can contain code in any of the languages in your “Notebooks” section.
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When you create a new Python notebook file, it will be recorded in the IPython Notebook
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format, dot ipynb for brief.
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Don’t fear this other new name, IPython – think of it as the predecessor of Jupyter.
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Consider the IPython Notebook format as a legacy file format…
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All right, let’s carry out a few operations.
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I will rename this folder to “Exercises”.
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Ok.
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Done.
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Now, I will create a new IPython Notebook file by expanding the “New” button and
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selecting one of the two Python formats I have here.
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Let’s use the default Python format.
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The browser immediately opens a new tab for me.
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This is the interactive shell we mentioned earlier.
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Here, you will write your code and see its output.
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Ok.
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Great.
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Now that we know more about the dashboard, we are ready to examine the shell and see
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how we can code in Jupyter.
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The field you see here is called a cell.
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You can access a cell by pressing “Enter”.
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Once you’ve done that, you’ll be able to see the cursor, so you can start typing
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code.
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The grey box is called input field.
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Now, you are in “Edit mode”.
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The green cell border and the little pencil in the top right corner indicate that as well.
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To close “Edit mode”, you have to press the “Escape” button.
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As I press “Escape”, I’ll go back to “Command mode”, which will allow me to
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edit the notebook as a whole.
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This is why the cursor and the pencil disappeared.
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The cell border turned back to grey, and its left margin is blue.
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Now, I’ll press “Enter” again.
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I will put a short code that says x is a list composed of four numbers, 1, 2, 3, and 4.
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After that, I can ask the computer to print this list for me by typing “x”.
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I can execute these commands in two ways.
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The first one is to hold Ctrl and then press Enter.
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By doing this, the machine will execute the code in the cell, and I will “stay” there,
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meaning I will not have created or selected another cell.
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Observe that an output field with the same number as the input field appeared.
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Input and Output fields of the same number are grouped together.
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The output represents the machine’s response to your commands provided in the corresponding
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input and the respective field cannot be modified.
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The second option allows for a more fluid code writing.
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To execute the same code, hold “Shift” and then press “Enter”.
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The previous two commands are being executed and then a new cell where you can write code
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is created.
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If you use “Shift” and “Enter”, you can continue typing code easily.
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It is amusing that you can cut, copy, and paste cells.
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You can use the buttons on the main menu.
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Since we are keen supporters of ergonomics, we suggest you get used to the keyboard shortcuts.
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When coding, you will mainly have to use the keyboard, so it’s worth memorizing different
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keyboard combinations that would allow you to work faster.
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Let’s see how we can apply shortcuts in
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practice.
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I will cut this cell by selecting it, and then pressing the “X” key.
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Remember you can always use the arrow keys to navigate along your notebook file.
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So, I will move up and then directly press the “V” key to paste it.
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Voilà!
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My cell was moved up here, below the cell I had selected before I pressed the “V”
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key.
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Now, I will press “C” to copy the same cell.
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I will use the arrow keys again to go down this time, and at the end, I will press “V”
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again.
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Apparently, this cell was copied and pasted below.
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The other two buttons in the menu cannot be substituted by keyboard shortcut combinations.
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They allow you to move a selected cell up or down, just as I am doing right now.
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Observe that the corresponding output field moves along with its input cell.
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Lovely!
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Instead of carrying out a command with Shift and Enter, you might prefer clicking on this
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button with the mouse – the result will be the same.
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What is interesting is that, when you work with more complex code that requires tougher
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calculations from the computer, while running the code, a little star will appear over here,
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before the square brackets.
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Sometimes, this process might take too long to complete, so to stop it or break it, this
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classical “stop” symbol can do that for you.
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Three other shortcuts could accelerate your coding a lot.
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Suppose I have selected this cell.
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After I press the “A” key, a new empty cell will be inserted above.
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If I select the same cell and press “B”, another cell will be created just below.
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Imagine the last step was a mistake and I need to delete that empty cell.
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In this case, it has already been selected, so I can press the “D” key twice, and
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it will readily disappear.
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Wonderful!
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All the cells we saw so far were code cells.
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Let’s see what is a markdown cell.
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It is a cell that contains strictly documentation - text not executed as a code.
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It will contain some message you would like to leave to the reader of the file.
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To convert a selected cell into a markdown cell, you should either expand this list and
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opt for “markdown”, or simply press the “M” button.
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Press “Enter” to access the cell and type some text.
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When I run this cell, the output will be a simple statement.
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Now, if you want to turn a markdown cell back to a code cell, select it and opt for “Code”
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from this drop-down menu or press the “Y” key.
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To conclude, there are two advantages of using Jupyter.
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First, when your code becomes longer, markdown cells turn out to be useful, as they allow
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you to leave comments and explain the solution you’ve created.
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This is why practitioners love to use them.
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The other benefit of using this App is you can select and execute whichever cell you
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want to – you need not run all the previous cells to run a particular cell!
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This allows for solving a problem in pieces and saves a lot of computation time.
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This was a long but indispensable lesson.
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We went through everything you need to know before you start coding.
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Thank you for watching!
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If you found this video interesting and want to gain an edge in your career, make sure
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to like, comment, and subscribe!
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And don’t forget to check out some of our other videos for another quick win in the
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data science skills department!
