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https://dicecamp.com/
V
“Machine Learning For Career Growth:
Navigating your Path in Data science”
(March 30th, 2024)
Trainers
Ahsan Ijaz (Data Scientist)
Presentation Outline
 Introduction
 Applications of Data Science
 Understanding Big Data
 Understanding Different Terminologies-Differences & Similarities
 Types of Data Analytics
 Data Analytics Process
 Tools Selection
The Growing Data
You know what, 2.5 Quintilian bytes of data has been created on a
daily basis for the past few years which makes for approximately 90%
of data in the world.
Image Source: giphy.com
Consider the example of healthcare domain, according to a study, the data
generated by every human body is 2 terabytes per day. This data includes
activities of brain, the stress level, heart rate, the sugar level, and many more.
Image
Source:
IntelliPaat
Image
Source:
IntelliPaat
Major Data
Sources
Increased
Social Media
Usage
Growing E-
commerce
Technological
Advancement in
Every Domain
Sensors and
Embedded Systems
in Everyday Life
Why this Growth?
It’s All About Data Now
Image
Source:
Google
Cloud
Data science defines the process of
cleaning, transforming, and
modeling data to discover useful
information for business decision-
making.
Data is everywhere but it’s not the
data that’s important it’s what you
do with the data that matters and
that’s where data science comes in.
Numerous Applications
Area:
Marketing
Area: Business Insight Area: Cyber Security
Area:
Healthcare
Area:
Recommender Sys.
Area: Government
Image
Source:
https://builtin.com/big-data/big-data-
examples-applications
Real-World Applications
• AMAZON: ADS BASED ON WHAT YOU BUY
• GENERAL ELECTRIC: MORE EFFICIENT
, ECO-FRIENDLY AIRPLANES
• FOURKITES: PRECISE DELIVERY ETAs
Real-World Applications-Contd.
• PALANTIR TECHNOLOGIES: PREDICTING ROADSIDE BOMB COORDINATES
• SKUPOS: A CONVENIENCE FOR CONVENIENCE STORES
• PROPELLOR HEALTH: SMART ASTHMA INHALERS
What do you think makes up the healthcare data?
Image
Source:
Healthcare
Analytics
Health Care Domain
Healthcare
data
is
complicated
as
it
comprises
of
many
thing.
Nature of Healthcare Data
Types of Healthcare Data
1. Wellness Data
• Fitness Tracker
• Digital Pill Boxes
• Personal
Medical Devices
• Pervasive Monitoring
Tools/Home Monitoring
2. Precision Medicine
• Disease Links
• Nutri-genomics
3. Electric Medical Records
EHR contains patient
information from all providers
involved in a patient's care
An EMR is a digital version
of a paper chart that
contains all of a patient's
medical history from one
practice.
4. Administrative Data
• Financial Data
• Logistics Data
• Quality Assessment
Data
5. Diagnostics Data
• Waveforms & Signals
• In and Out Laboratory
Data
• Microbiology Data
6. Demographic Data
• Intrinsic Data:
–Age
–Sex
–Height
–Weight (to some extent)
–Ethnicity
• Extrinsic Data:
–Address
–Martial status
–Religion
–Employment
–Insurance plan
7. Clinical Data
Image Source: Healthcare Analytics
• Procedures • Clinical Notes
8. Medication Data
Image Source: Healthcare Analytics
• Dosage and Dosage Unit
• Form and Form Unit
• Route (Intravenous/Oral)
Image Source: Healthcare Analytics
• Diagnosis through mobile
technology
• Treatment through
telemetry
• Monitoring through
Wearable devices
It has synchronous vs.
asynchronous forms!
9. mHealth Data
Real-World Data-Challenges
Reality
is
very
complicated!
Too many interconnected systems
working together.
Reality
is
very
complicated!
Too many interfaces working
together.
Real-World Data-Challenges (Contd.)
Healthcare Data is “Big”!
Concept of Big Data
Why it is Big Data?
Because healthcare data hits all 4V’s!
Some Terminologies
Data mining focuses on
identifying hidden pattern from
data.
Big data is about processing of
large voluminous amount of
growing data.
Deep learning involves adding
multiple layers to achieve
machine learning accuracy.
Inspired from the brain model!
Machine learning deals with
specific models to train the
machine.
AI focuses on giving
intelligence to the machine
for performing different tasks
Data Warehousing?
Data Analytics?
Business Intelligence?
Data science focuses on
understanding business problem
and converting into an analytical
solution.
The Relationship!
Image Source: https://www.linkedin.com/pulse/how-make-simple-explain-ai-ml-dl-together-data-science-vollmer
Data Science Vs. Data Analytics
Data Science aims to improve product development
processes, decision-making processes, trend analysis
processes, and forecasting processes through taking
advantage of the various fields.
Data analytics is a discipline that collects and studies
information and allows for decision making on the
basis of the analysis of large volumes of data. Data
analysis is a more specific area than data science.
Image
Source:
https://svitla.com/blog/data-science-
vs-data-analytics
Math
&
Stats
Computer
&
IT
IT Guys Non IT Guys
Business
Knowledge
Everyone is on same page in Start -- Data Science Domain
Data Science Learning Track
GOAL
Understanding
Algorithms/Math/Stats
Business Understanding
Domain Knowledge
Learn a Tool/Language
For Data Manipulation
3
Steps
Process
Data Science Learning Track
Both
Have
To Learn
New Things
Both have to put Equal Effort
Math
&
Stats
IT Guy
Non IT Guy
Business
Knowledge
Math
&
Stats
IT
Knowledge
Question Acquire Ingest/ETL Wrangling Visualize
Exploratory
Data Analysis
(EDA)
Choose Build/Train Deploy
Modelling
Test
Validate
Business Owner Data Engineer/Data Scientist Data Analyst/Data Scientist
Data Scientist/Machine Learning Engineer DevOps/Data Scientist Business Owner/
Data Scientist
Business
Owner/
Data Scientist/
Data Analyst
Data Science Learning Track
Comparison of
Data Science
Skills
Data
Analyst
Machine
Learning
Engineer
Data
Engineer
DevOps
Engineer
Data
Scientist
Programming Tools
Data Visualization and
Communication
Data Intuition
Statistics
Data Wrangling
Machine Learning
Software Engineering
Multivariable Calculus
and Linear Algebra
Very
Important
Somewhat Important
Not that Important
Tools Selection
https://dicecamp.com/

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Machine Learning For Career Growth..pptx

  • 2. V “Machine Learning For Career Growth: Navigating your Path in Data science” (March 30th, 2024) Trainers Ahsan Ijaz (Data Scientist)
  • 3. Presentation Outline  Introduction  Applications of Data Science  Understanding Big Data  Understanding Different Terminologies-Differences & Similarities  Types of Data Analytics  Data Analytics Process  Tools Selection
  • 4. The Growing Data You know what, 2.5 Quintilian bytes of data has been created on a daily basis for the past few years which makes for approximately 90% of data in the world. Image Source: giphy.com Consider the example of healthcare domain, according to a study, the data generated by every human body is 2 terabytes per day. This data includes activities of brain, the stress level, heart rate, the sugar level, and many more. Image Source: IntelliPaat Image Source: IntelliPaat
  • 5. Major Data Sources Increased Social Media Usage Growing E- commerce Technological Advancement in Every Domain Sensors and Embedded Systems in Everyday Life Why this Growth?
  • 6. It’s All About Data Now Image Source: Google Cloud Data science defines the process of cleaning, transforming, and modeling data to discover useful information for business decision- making. Data is everywhere but it’s not the data that’s important it’s what you do with the data that matters and that’s where data science comes in.
  • 7. Numerous Applications Area: Marketing Area: Business Insight Area: Cyber Security Area: Healthcare Area: Recommender Sys. Area: Government Image Source: https://builtin.com/big-data/big-data- examples-applications
  • 8. Real-World Applications • AMAZON: ADS BASED ON WHAT YOU BUY • GENERAL ELECTRIC: MORE EFFICIENT , ECO-FRIENDLY AIRPLANES • FOURKITES: PRECISE DELIVERY ETAs
  • 9. Real-World Applications-Contd. • PALANTIR TECHNOLOGIES: PREDICTING ROADSIDE BOMB COORDINATES • SKUPOS: A CONVENIENCE FOR CONVENIENCE STORES • PROPELLOR HEALTH: SMART ASTHMA INHALERS
  • 10. What do you think makes up the healthcare data? Image Source: Healthcare Analytics Health Care Domain
  • 13. 1. Wellness Data • Fitness Tracker • Digital Pill Boxes • Personal Medical Devices • Pervasive Monitoring Tools/Home Monitoring
  • 14. 2. Precision Medicine • Disease Links • Nutri-genomics
  • 15. 3. Electric Medical Records EHR contains patient information from all providers involved in a patient's care An EMR is a digital version of a paper chart that contains all of a patient's medical history from one practice.
  • 16. 4. Administrative Data • Financial Data • Logistics Data • Quality Assessment Data
  • 17. 5. Diagnostics Data • Waveforms & Signals • In and Out Laboratory Data • Microbiology Data
  • 18. 6. Demographic Data • Intrinsic Data: –Age –Sex –Height –Weight (to some extent) –Ethnicity • Extrinsic Data: –Address –Martial status –Religion –Employment –Insurance plan
  • 19. 7. Clinical Data Image Source: Healthcare Analytics • Procedures • Clinical Notes
  • 20. 8. Medication Data Image Source: Healthcare Analytics • Dosage and Dosage Unit • Form and Form Unit • Route (Intravenous/Oral)
  • 21. Image Source: Healthcare Analytics • Diagnosis through mobile technology • Treatment through telemetry • Monitoring through Wearable devices It has synchronous vs. asynchronous forms! 9. mHealth Data
  • 22. Real-World Data-Challenges Reality is very complicated! Too many interconnected systems working together.
  • 23. Reality is very complicated! Too many interfaces working together. Real-World Data-Challenges (Contd.)
  • 24. Healthcare Data is “Big”!
  • 26. Why it is Big Data? Because healthcare data hits all 4V’s!
  • 27. Some Terminologies Data mining focuses on identifying hidden pattern from data. Big data is about processing of large voluminous amount of growing data. Deep learning involves adding multiple layers to achieve machine learning accuracy. Inspired from the brain model! Machine learning deals with specific models to train the machine. AI focuses on giving intelligence to the machine for performing different tasks Data Warehousing? Data Analytics? Business Intelligence? Data science focuses on understanding business problem and converting into an analytical solution.
  • 28. The Relationship! Image Source: https://www.linkedin.com/pulse/how-make-simple-explain-ai-ml-dl-together-data-science-vollmer
  • 29. Data Science Vs. Data Analytics Data Science aims to improve product development processes, decision-making processes, trend analysis processes, and forecasting processes through taking advantage of the various fields. Data analytics is a discipline that collects and studies information and allows for decision making on the basis of the analysis of large volumes of data. Data analysis is a more specific area than data science. Image Source: https://svitla.com/blog/data-science- vs-data-analytics
  • 30. Math & Stats Computer & IT IT Guys Non IT Guys Business Knowledge Everyone is on same page in Start -- Data Science Domain
  • 31. Data Science Learning Track GOAL Understanding Algorithms/Math/Stats Business Understanding Domain Knowledge Learn a Tool/Language For Data Manipulation 3 Steps Process
  • 32. Data Science Learning Track Both Have To Learn New Things Both have to put Equal Effort Math & Stats IT Guy Non IT Guy Business Knowledge Math & Stats IT Knowledge
  • 33. Question Acquire Ingest/ETL Wrangling Visualize Exploratory Data Analysis (EDA) Choose Build/Train Deploy Modelling Test Validate Business Owner Data Engineer/Data Scientist Data Analyst/Data Scientist Data Scientist/Machine Learning Engineer DevOps/Data Scientist Business Owner/ Data Scientist Business Owner/ Data Scientist/ Data Analyst Data Science Learning Track
  • 34. Comparison of Data Science Skills Data Analyst Machine Learning Engineer Data Engineer DevOps Engineer Data Scientist Programming Tools Data Visualization and Communication Data Intuition Statistics Data Wrangling Machine Learning Software Engineering Multivariable Calculus and Linear Algebra Very Important Somewhat Important Not that Important