In this first course of our Applied Data Science online course series, you'll learn about the mindset shift of going from small to big data, basic definitions and concepts, and an overview of the data science workflow.

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Applied Data Science Online Course
1st Class: Learning the Basics,
concepts, & your first ML model

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● September 20th at 12PM ET: Learning the Basics, concepts, & your first ML model
● September 27th at 12PM ET: The data science workflow, building a predictive model flow
● October 4th at 12PM ET: Getting dirty; data preparation and feature creation
● October 11th at 12PM ET: Understanding your model - and communicating about it
Curriculum
Go from Small to Big Data in 4 weeks

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> Intro (that’s now)
> Background: going from small to big data
> Machine Learning definitions & basic concepts
> The data science workflow
> Questions
> Hands-on exercise: Titanic Prediction data
The plan for today
Learning the Basics, concepts, & your first ML model

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Going from Small to Big Data

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Local processing
● Limited Power (100k lines)
● Downloading and opening csv or xls on a local place
● Not distributed
Database processing
● Can process billions of lines
● File are not stored and process in the same space
than co-worker
● Distributed analysis
Local processing vs. Database processing
Going from Small to Big Data

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The basic element you’re working on when you’re modifying data in Excel is the cell.
When you’re working with data from a database, your basic element is a column.
Whether you’re cleaning your data or enriching it with new variables, you’ll be creating new columns in new datasets,
never changing one line of a file at a time.
Cell-to-cell Modifications vs. Mass Actions
Going from Small to Big Data
VS.

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Potential pain points for analysts to transition
Going from Small to Big Data
Interacting
with database
How to connect to
Amazon Web
Service? Hadoop?
How to extract and
transform the data
1 2 4
Collaboration
with other
profile
How to benefit and
interact with the
works of a Data
Scientist or Data
Engineer?
3
Working with
Big Data
Extract and
Transform my data
on very large files
Advanced
Analytics
How to create
Machine Learning
Models without
coding skills? And to
handle Geography,
Time series…?

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Concepts and Definitions

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Data Science: An interdisciplinary field that uses scientific
methods, processes, algorithms and systems to extract
knowledge and insights from data in various forms, both
structured and unstructured (wikipedia)
What are we talking about?
Definitions

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Machine Learning: A field of study focused on constructed systems that learn from large amount of data to make
predictions or find relations.
What are we talking about?
Definitions

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Different types of Machine Learning
Definitions
Supervised Unsupervised
Data is labeled, algorithm predicts an
output from the input data
Data isn’t labeled, algorithm learns the
inherent structure of the data and makes a
prediction
Examples:
• Predicting the genre of a song based on a
label
Examples:
• Predicting the genre of a song without a label

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Different types of Machine Learning
Definitions
Prediction Clustering
Goal: Create a model that can predict a
target variable
Goal: Separate data into clusters based on
similarity (no specific target)
Examples:
• Predict the sales price of an apartment
• Forecast the winner of an election
• Diagnose a disease
Examples:
• Find groups of similar apartments
• Segment voters into demographic groups
• Group diseases based on symptoms

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Different types of Prediction
Definition
If target is (continuous) then regression
If target is (discrete) then classification
Ex: predicting price of airline tickets
Ex: predicting fraud

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Different types of Prediction
Definition

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Different types of Machine Learning
Examples
Predicting mortgage defaults
Forecasting lifetime spending of customer
Grouping songs into genres
Predicting amount of snowfall
Segmenting website visitors
Recommending movies to Netflix users
Detecting unusual financial transactions
Prediction Clustering
(classification)
(regression)
(regression)
(classification)
(regression)

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What’s in a dataset
Definitions
Feature
Observation
● Types of data

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Train, test, validate
If performance on test set starts to decline, think about retraining your model
Training set
Used to create your model
Validation set
Used to measure performance
Testing set
Used to check model performance
after deployment

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Train, test, validate
Random or Time based
Training set Validation set Test set
70%
20%
10%

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The Data Science Workflow

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7 steps of a data projects
The Data Science Workflow

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Advanced version of the workflow
The Data Science Workflow
Data Acquisition &
Understanding
Data Preparation Model Creation
Evaluation Deployment
Dataset 1
Scored
dataset
Scored
dataset
Iteration 1
Iteration 2
Iteration n
Dataset 2
Dataset n
Business
Understanding

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Qu s o s?

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Hands-on

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Kaggle Titanic Challenge
Titanic Use Case
Predicting who survived the tragedy

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