Play with Python: An intro to Data Science

Play with Python: An intro to Data Science Ignacio Larrú Instituto de Empresa

Who am I? Passionate about Technology From Iphone apps to algorithmic programming I love innovative technology Former Entrepreneur: Founded several companies from online memorabilia e-commerce to structural civil engineering calculations. Investment Banker: I advise Spanish companies in M&A and IPO processes Venture Capital & Bootcamp: CFO, investment director at K Fund + Academic Director of IE Data Science Bootcamp 2

Big Data and Data Science Big Data technologies Data Science

Why is Data Science so difficult?

Overview of the Data Science process Validation!!!! Framing the Problem Solving the Problem Action!!!

Problem recognition Business comes first, think on what moves the needle Focus specific on decisions that will be made as a result of the analysis Helps everyone realize the reason for the analysis Makes identifying key stakeholders easier No decision. No analytics? Plan your objective for your problem: Investigation Exploration A/B Testing Survey Prediction Past performance ( reporting) Scope of the problem should be expansive but by the end of the problem framing you should have a clear statement of the problem

Exploratory Data Analysis Use descriptive statistics (median, mode, variance, frequency tables, correlations lines, etc ) to understand the important characteristics of a dataset Identify trends and outliers

Overview of Data processing algorithms (i) 1. Classification -> for each individual in a population, which of a set of clasess this individual belongs to. Among all the customers of ACME, which are likely to respond to a given offer? 2. Regression -> Estimate or predict, for each individual, the numerical value of some variable for that individual How much will a customer use the service?

Overview of Data processing algorithms (ii) 3. Similarity matching -> identify similar indivduals based on data known about them Other customers also bought 4. Clustering -> Group individuals in a population together by their similarity but not driven by nay specific purpose Do our customers form natural groups or segments? 5. Co occurence ->Find associations between entitites based on transactions involving them What items are commonly purchased together?

Can data visualizations hurt your analysis?

Lying with graphs Source: Hbr.com

Python Data Science Stack

Hello World! print( hello World ) 17

Python is interpreted 18

Programming Python 19

Comments in Python # for a single line comment for a multiple line comment 20

Variables in Python 21

Variables in Python Don t need to have a pre-defined type, they get the type from the value they are pointing at Four main types: String holds text based values name = Ignacio int Integer numbers name = 10 float floating decimal numbers name = 10.4 Boolean (True or False) More variable types (lists, tuples, dictionaries to be reviewed during the course) 22

Variables in Python A variable has a name (identifier) a type a scope and a value A valid identifier is a non-empty sequence of characters with: The start character can be the underscore "_" or a capital or lower case letter The letters following the start character can be anything which is permitted as a start character plus the digits Identifiers are case-sensitive! Python keywords are not allowed as identifier names! 23

Python vs. other languages Python Statically typed languages Variable type determined at runtime Variable bound to one object and the object has only one type Varible can change type by changing the type of the object bound to the variable Bound to a type at compile type Bound to an object at runtime Need to declare the variable before using it 24

Python is a dynamically typed language 25

Python is a strongly typed language 26

With every great power Guidelines Use descriptive names (x vs. sales_amount) Be consistent (user_name or username?) Follow the traditions of the language Usually in Python variable names start with a lowercase letter and avoid starting with an underscore Keep the length in check no user_total_sales_month_report 27

Mathematical operators Source:http://www.emcu.it/ 28

Mathematical operators 29

Converting values float(x) - returns a floating-point value by converting x int(x) str(x) - returns an integer value by converting x - returns a string value by converting x bool(x) returns a boolean value 30

If else - elif 31

If else - elif 32

Logical operators 33

While Loop 34

While and if 35

For Loops 36

Range() Function range([start], stop[, step]) 37

Break, Continue and pass with else Break -> End loop Continue -> End operation Pass -> Null statement used as placeholder Else at the end of loops: For -> ended normally the loop ( no break) While -> The loop condition is false 38

Break, Continue and pass with else 39

Python simple data structures 40

Sequences 41

Strings are sequences 42

Using len() and in len() function will return the length of a sequence The in operator checks if an element is a member of a sequence If the element is a member the condition is true else it is false 43

Using len() and in 44

Programming exercise 45

Slicing Sequences 46

Programming exercise 47

Lists Mutables sequences 48

Lists Adding new items append adds at the end of the list Insert(index,value) allows you to insert at a given index 49

Lists Remove remove(value) del function 50

Lists Remove items 51

Sort() vs. Sorted() 52

Tuples Inmutable secuences that can contain elements of different types that can be mutable If the contents not need to change used tuples vs. lists Faster than lists 53

Tuples are inmutable but not its elements 54

Sets Non duplicative unordered collections 55

Sets Operations 56

Sets Math Operations 57

Sets Math Operations 58

Dictionaries 59

Dictionaries 60

Dictionaries - operations update(d) to join dictionaries (or {**x,**y}) copy() to creatr a shallow copy get( key ) returns None if element doesn t exist 61

Dictionaries - operations 62

Crossfit coding 63

Session Wrap-up 64