1.
Ayodele Odubela
How to Start Doing
Data Science

2.
About Me
● Data Scientist @ CometML
● MS in Data Science from Regis University
● Teaching Explainable ML
● Author of Getting Started in Data Science
● Currently writing Uncovering Bias in
Machine Learning

3.
Skills
01

4.
What is Data Science?
Data science is an inter-disciplinary field that uses (somewhat) scientific methods, processes,
algorithms and systems to extract knowledge and insights from many structural and
unstructured data.

5.
Coding
SQL
Python
R
Understanding and Creating New Metrics
Deciding which methods work in your industry
Business Sense
Math
Statistics
Probability
Linear Algebra

6.
What Data Projects Include
Identify a Problem
Asses the org’s incentives
Gather & clean data
Data documentation
Exploratory Analysis
Inferential Statistics
Data Storytelling
Harm identification and mitigation
Creating ML Models
Building User Recourse Frameworks

7.
Relevant Roles
Data Scientist
Skills: Advanced SQL, Intermediate Python/R, Intermediate Statistics
Machine Learning Engineer
Skills: Advanced Python, Tensorflow/PyTorch/Keras, Intermediate Linear Algebra, Calc, &
Statistics
Research Scientist
Skills: Advanced Math, Science Communications

8.
Tasks
02

9.
Data Wrangling
Any language can be used to get data from databases and API’s

10.
Data Cleaning
Dealing with Missing Values
Combining Sparse Categorical Columns

11.
Data Transformations
Power Transforms and scaling/normalization
We do this to make modeling structured data easier

12.
Functional Programming
Applying and composing functions to make code more concise and reusable

13.
Experimental Design
Understanding and making the consistent experimental choices
Hypothesis Testing
A/B Testing

14.
Concepts
03

15.
Goals
● Predict future events given past data
● Find anomalies in our datasets
● Make recommendations based on someone’s interests

16.
Methods
1. Clean data so its in a format we can model
2. Understand data distributions to inform model selection
3. Perform Exploratory Data Analysis to grasp data
4. Choose modeling techniques that help us solve problems
5. Measure how well our models perform and optimize then
6. Iterate!

17.
Exploratory What?
In statistics, exploratory data analysis is an approach to analyzing data sets to summarize their
main characteristics, often with visual methods. A statistical model can be used or not, but
primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis
testing task.

18.
Regression
Is just a fancy word for predicting numbers

19.
Classification
attempts to tell different things apart

20.
Clustering
tries to identify groups of similar things based on how far apart they are

21.
Reinforcement Learning
autonomous agents learn from their environment and make new decisions based on if they
were rewarded or punished

22.
Hands-On Experience
04

23.
Practice Consistently

24.
Finding Data
Kaggle
UCI Data Repository
Data.World
Government & Local Open Data
Web Scraping
Public APIs

25.
Cleaning & Manipulating Data
Grasp the basic techniques
Build intuition for when to use certain methods
Understand pros and cons of each
Tools:
Excel
Python & R
SQL

26.
Getting Practice
Github
Medium Tutorials
Hackathons
MooCs

27.
To get a formal education or
not?

28.
Market Yourself
05
Even while you’re still learning

29.
Communicate Your Value
How have you impacted past businesses?
How would your relevant projects help a company?
Do you know how to quantify your value?

30.
Github
Showing off code projects
Connecting with other developers
Collaborating and proving technical skills

31.
Blog / Personal Website
Share Expertise
Show off Portfolio
Provide insight into your thought process

32.
Thank You!
25% off Getting Started in Data Science
Code: VBROWNBAG
@DataSciBae
ayodeleodubela.com