Summary
Summary of Data Science Skills Python DataCamp modules (325235-M-3)
- Course
- Institution
This document includes all modules of the DataCamp modules for Data Science Skills.
[Show more]
1 review
By: rubenkessels • 1 year ago
Seller
Follow
jesmen12
Reviews received
Content preview
Summary data science skillsInhoud
Course 1: Introduction ............................................................................................................................. 3
1.1 Python basics ................................................................................................................................. 3
1.2 Python lists .................................................................................................................................... 3
1.3 Functions and packages................................................................................................................. 5
1.4 Numpy (Numeric Python).............................................................................................................. 6
Course 2: Intermediate python ............................................................................................................... 8
2.1 Matplotlib ...................................................................................................................................... 8
2.2 Dictionaries & pandas.................................................................................................................... 9
2.3 Logic, Control Flow and Filtering ................................................................................................. 13
2.4 Loops ........................................................................................................................................... 15
2.5 Case study: hacker statistics ........................................................................................................ 17
2.5 Summary...................................................................................................................................... 19
Course 3: DataFrames............................................................................................................................ 20
3.1 Transforming DataFrames............................................................................................................ 20
3.2 Aggregating DataFrames; Summary statistics ............................................................................. 21
3.3 Slicing and Indexing DataFrames ................................................................................................. 23
3.4 Creating and Visualizing DataFrames .......................................................................................... 25
Course 4: Supply Chain Analytics in Python .......................................................................................... 28
4.1 Basics of supply chain optimization and PuLP ............................................................................. 28
4.2 Modeling in PuLP ......................................................................................................................... 29
4.3 Solve and evaluate model ........................................................................................................... 32
4.4 Sensitivity and simulation testing of model ................................................................................ 34
Course 5: Cleaning Data in Python ........................................................................................................ 38
5.1 Common data problems .............................................................................................................. 38
5.2 Text and categorical data problems ............................................................................................. 41
5.3 Advanced data problems ............................................................................................................. 43
5.4 Record linkage ............................................................................................................................. 46
Course 6: Cluster analysis ...................................................................................................................... 49
6.1 Introduction to clustering ............................................................................................................ 49
6.2 Hierarchical Clustering ................................................................................................................. 53
6.3 K-Means clustering ...................................................................................................................... 56
6.4 Clustering in the real world ......................................................................................................... 59
,Course 7: Machine Learning with scikit-learn (model testing) .............................................................. 63
7.1 Classification ................................................................................................................................ 63
7.2 Regression ................................................................................................................................... 66
7.3 Fine-tuning your model ............................................................................................................... 68
7.4 Preprocessing and pipelines ........................................................................................................ 70
Course 8: Linear classifiers .................................................................................................................... 73
8.1 Applying logistic regression and SVM .......................................................................................... 73
8.2 Loss functions .............................................................................................................................. 75
8.3 Logistic regression ....................................................................................................................... 77
8.4 Support Vector Machines (SVMs in detail) .................................................................................. 80
,Course 1: Introduction
1.1 Python basics
iPython shell = interactive
Python script > text files > use print to generate output
Use a # to add comments in a python script
Calculator
Variables and types
• Variables: named piece of memory that can store a value.
- Syntax: name = value
Usage:
- Compute an expression's result,
- Store that result into a variable,
- And use that variable later in the program.
• Types: Type(‘variable’)
- Float Decimal number
- Integer Whole number
- Strings Text ‘’’’
- Booleans True/False
> Different behaviour using operators for different types of floats.
> When working with different types -> Convert if necessary before using operators.
1.2 Python lists
Lists; store multiple values
• Lists: Lists are used for storing small amounts of one-dimensional data containing different types.
- But, can’t use directly with arithmetical (matrix) operators (+, -, *, /, ...).
- If you need efficient arrays with arithmetic and better multidimensional tools.
• Sublists: One list can contain more sublists
, Subsetting lists (access information in a list; indexes)
• Element: The number in a list. 1.68 is the fourth element
• Index: The index of an element in the list, it starts at 0. 1.68 has index 3
> To select an element using indexing: Fam[3] gives ‘1.68’
> Negative indexes Fam[-1] gives ‘1.89’
• Slicing: Select multiple elements in a list and creating a new list
Example: fam [3:5] returns [1.68, ‘mom’] (element 3 and 4)
> [Start ; End] -> Start is included, End is excluded!
> [:4] returns indexes 0, 1, 2 and 3 (elements 1, 2, 3, 4)
> [5:] returns indexes 5, 6, 7 (elements 6, 7, 8)
Subsetting lists of lists
x = [["a", "b", "c"],
["d", "e", "f"],
["g", "h", "i"]]
X[rows][columns]
x[2][0] Returns: ‘g’ (sublist 2 , index 0)
x[2][:2] Returns: [‘g’, ‘h’] (sublist 2 , index 0 and 1)
Manipulation Lists (update lists for commands)
• Changing the elements in a list (e.g. change, add, remove elements)
1. Change: Fam [7] = 1.86 Changes the height of dad
2. Change slice: Fam [0:2] = [“Lisa”, 1.74] Changes the 0 and 1 index
3. Adding/extend: Fam + [“me”, 1.79] Adds ‘me’ and 1.79 to the list
4. Remove: del(fam[2]) Removes “emma from the list”
> Watch out because the indexes of the list have now changes!
How lists work
> x and y are the referred to the same list. > Solution: create y as a new list.
The benefits of buying summaries with Stuvia:
Guaranteed quality through customer reviews
Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.
Quick and easy check-out
You can quickly pay through credit card or Stuvia-credit for the summaries. There is no membership needed.
Focus on what matters
Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!
Frequently asked questions
What do I get when I buy this document?
You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.
Satisfaction guarantee: how does it work?
Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.
Who am I buying these notes from?
Stuvia is a marketplace, so you are not buying this document from us, but from seller jesmen12. Stuvia facilitates payment to the seller.
Will I be stuck with a subscription?
No, you only buy these notes for CA$11.30. You're not tied to anything after your purchase.
Can Stuvia be trusted?
4.6 stars on Google & Trustpilot (+1000 reviews)
75323 documents were sold in the last 30 days
Founded in 2010, the go-to place to buy study notes for 14 years now