This document provides an introduction to data science, machine learning, and IBM tools. It defines key concepts like artificial intelligence, machine learning, deep learning, and data science. It outlines the typical data science workflow including data collection, cleaning, exploration, modeling, and deployment. It also discusses different types of data analysis and model types. Finally, it provides an overview of popular data science tools and frameworks and lists some IBM specific analytics tools.

1. Introduction to DS, ML and
IBM Tools
—
Qamar Un Nisa
-Lead Developer Advocate, IBM Pakistan
-Women in Data Science (WiDS) Ambassador
-MS in Data Science, NLP Thesis
January 29, 2021

2. Contents
2
Introduction to AI and Data 04
Data Analysis and its types 05
Data Science Flow 07
Data Science Methodology 08
AI / Data Science Use-Cases 13
Data Science Tools and Frameworks 14

3. 3
Around 2.5
quintillion bytes
of Data is
generated
everyday.

4. 4
What is it all about?
Danger
Zone
Data
Science
Artificial Intelligence
Machine Learning
Deep Learning
Data Science
Any technique which enables computers to
mimic human intelligence.
AI techniques that give computers the ability to
learn with previous data and patterns – mimic
human learning.
Makes the computation of multi-layer neural
networks feasible – mimic human brain.

5. 5
Types of Data Analysis
What Happened:
Uncovers insights
Dashboards
Descriptive
Analysis
Diagnostic
Analysis
Why it Happened:
Uncovers patterns and
dependencies
Social Media
Marketing Campaign
Predictive
Analysis
What will Happen:
Use past patterns to
predict future trends
Predicting future
sales
Prescriptive
Analysis
What to do:
Eliminate possible
future problems or
take advantage of
promising trend
Recommendation
Systems

6. 6
A Data Scientist
wear
different
types of
HATS.

7. Data Science Flow Pipeline
Process
Data
Explore (Basic Stats,
Plots, Trends, Pattern
Mining)
Design Model e.g.
Feature Engineering
Learn Model
Update/Improve Model
Verify Experiment
(A/B) Testing
Domain Knowledge Expert Knowledge
Raw
Data
1. Data Cleaning/
Processing
Model
Deployment
2. Exploratory Data
Analysis / Data
Visualization
3. Machine Learning
Model / Data Modelling

8. 8
Researching the Data - Types of Data
Name
Gender
Numerical
Discrete Continuous
Height
Number of
audiences
Age Salary
Images
Categorical

9. 9
Researching the Data - Types of Data
Name
Gender
Numerical
Discrete Continuous
Height
Number of
audiences
Age Salary
Text Data
Categorical
Nominal Ordinal
Rating
Grades

10. 10
Preparing the Data
Data Cleaning
• Bad formats: ignore or treat like missing data
• Missing Data: replace or remove row
• Useless variables: remove
• Wrong Data: dummy or rubbish data like aaa, bbb, 21ad etc.
Data Transformation
• Transform variables: data formats, column data type
• Create derived variables: Country from IP, age from ID Card
• Normalize strings: different spellings and nicknames (Male, M, Men etc.)
• Feature value rescaling: most ML algorithms need values ranging from 0-1
• Enrich: lookup and add age from profile

11. 11
Data Exploration – Data Visualization

12. 12
Applying Models on the Data – Data Modelling
Machine Learning
Supervised Learning
Classification
Clustering Regression
Unsupervised Learning Reinforcement Learning

13. 13
Ways to excel in Data Science
• Build Foundations – Cognitiveclass.ai | Coursera | DataCamp etc.
• Tools – Opensource, company specific etc.
• Kaggle – Participate in competitions, maintain Kaggle profile.
• Competitions – Local events, Webinars , Hackathons.
• Internship – Companies, Telcos, Banks, Startups working with AI.

14. 14
AI / Data Science Use-Cases
Fore more, Visit: Developer.ibm.com

15. 15
Data Science Tools – Libraries and Frameworks

16. 16
IBM Data Science & Analytics Tools
• Cloud Pak for Data (Watson Studio)
• SPSS Modeler
• Auto AI
• IBM Cognos Analytics
• Watson Machine Learning
• Watson AI OpenScale
• Other Watson Services – Chatbot, Natural Language Processing etc
Create free IBM Cloud account: http://ibm.biz/intro-ds

17. Thank you
17
Qamar Un Nisa
Lead Developer Advocate, IBM Pakistan
Data&AI Focal, IBM MEA
Email: qamar.n@ibm.com
LinkedIn: linkedin.com/in/qamarnisa/
Survey: https://ibm.biz/BdfXaR

18. Upcoming Events
• Workshope 2:
6 February 2021 – Introduction to Machine Learning and Different Models
Link: https://ibm.webex.com/ibm/j.php?MTID=mb8227fc22282ed57c4856d45d860b3db
• Workshop 3:
13 February 2021 - Automated Model Building using Watson AutoAI: Build and Deploy top-
performing models in minutes
Link: https://ibm.webex.com/ibm/j.php?MTID=m8f70391f2e893c4f8eb6d6e3f356a641

19. 19