Certified Data Science Specialist Course Preview

1. Data Science Course
with Visual Programming

2. TRAINER PROFILE
Copyright by Dr. Nickholas 2
Ts. Dr. Nickholas Anting Anak Guntor, PhD
nickholasanting.com
Professional Profile
• Lecturer in Computer Programming with Programming &
Civil Engineering at FKAAB, UTHM.
• Certified Data Science Analyst & Data Engineer by
Fusionex International.
• Author of Programming for Beginners with Python.
Data Science Project
• Concrete leakage detection with Fully Convolutional
Network for image processing.
• Predictive modelling for Projek Data Raya JPT.
• Sentiment analysis for user perception in market
analysis.
Training Fields
• Data Science with Visual Programming
• Machine Learning with KNIME
• Machine Learning with Python
• Programming with Python 3

3. THE WORLD OF DATA
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Geospatial World
• Evolution of digital world has generate zettabytes and yottabytes of
structured and unstructured data every day.
• Organization took advantages from this mass amount of data and turn
it into valuable information.
• These digital data come from multiple of resources.
Online Transaction Social MediaInternet of Things

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5. WHAT IS DATA SCIENCE
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Definition
The field of study that dealing with data by combining multiple domain of
knowledge, including mathematics & statistics, computer programming,
analytics skill, visualization and business communication.
Probability & Statistics Computer Programming
Visualization & Business
Intelligence
Analytics Thinking

6. ANALYTICS SPECTRUM IN DATA SCIENCE
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PRESCRIPTIVE ANALYTICS
• How to make it happen?
• What is the perfect value
of discount for product A
to increase sales and
obtain high profit?
• What happened?
• Who are customers?
• How many people buy
item A?
DESCRIPTIVE ANALYTICS DIAGNOSTIC ANALYTICS
• Why does it happen?
• What cause the sales to
drop?
PREDICTIVE ANALYTICS
• What might be happen?
• If increasing the
marketing budget, will it
increase number of
sales?

7. ADVANTAGES OF SCIENCE
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PROFIT MAXIMIZATION
Apply right strategy and approach to minimize
losses and maximize profit.
FAST & BETTER DECISION
Advance tools to analyse information faster & more
accurate.
IMPROVE SERVICES
Improve customer satisfaction & experience.
NEW INNOVATION
Facilitate new product with advance features
(Self-driving car, Grab Car, Google Map)

8. CASE STUDY 1 – AMAZON.COM
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• Apply Recommendation Based System (RBS) to predict customer needs &
behavior.
• RBS used to predicts the rating or preference a user give to an item.
• 35% of revenue generated by Recommendation Engine.

9. CASE STUDY 2 – NETFLIX
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• 80% of movies watches are
recommended by system.
• Recommendation are driven
by Machine Learning
Historical Watched Movies
You may want to watch this movie.

10. CASE STUDY 3 – GPS System
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Data
Science
• AI
• Machine Learning
Digital
Technology
• Programming
• Micro-processor

11. CASE STUDY 4 – GRAB CAR
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• Improve customer service &
satisfaction.

12. DATA SCIENCE PROFESSIONAL
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Data Scientist
• Profession who work with data in relevant
fields/domain industry.
• Data analytical expert with analytical thinking
and technical skill to solve complex problem.
• With mind of curiosity to explore what problem
need to be solved.

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Are you interested to be a part of
Data Scientist team?
It might not be easy...

14. NEEDS & DEMANDS
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• Demand of professional skill in data science in
industries keep increasing.
• Main challenge is to learn writing programming
code to work with Machine Learning.
• It may not be easy for someone who have no or
little programming knowledge and experience.

15. OUR INNOVATIVE DATA SCIENCE MODULE
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DATA SCIENCE WITH VISUAL PROGRAMMING
A complete course on data science topic including, machine learning, data analytics and visual
programming.
• NO coding require.
• Interactive and open source
KNIME Analytics Platform software
for Machine Learning.
• Advance Microsoft Power BI tools
for data analytics and visualization.

16. VISUAL VS CODING PROGRAMMING
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Conventional Coding Visual Programming

17. WHY VISUAL PROGRAMMING
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DRAG & DROP
APPROACH
NO NEED TO WRITE CODE
SCRIPTING USING
PROGRAMMING LANGUAGE.
FAST & LESS ERROR
WHILE CONSTRUCTING
THE ALGORITHM

18. KNIME ANALYTICS PLATFORM
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• Open source visual programming tools.
• Codeless approach of performing programming task.
• Used in data analytics, manipulation, visualization & reporting.

19. EXAMPLE OF MACHINE LEARNING WORKFLOW
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20. DATA SCIENCE WITH VISUAL PROGRAMMING
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By
Dr. Nickholas & Dr. Alvin
Data Science Course
with Visual Programming
COURSE OBJECTIVES
A complete course on data science topic including, machine
learning, data analytics with visual programming tools.
COURSE LEARNING OUTCOMES
• Understand the concept and workflow of the field of Data
Science.
• Apply the mathematics & statistics principle for data science.
• Performing data preparation based on real dataset.
• Creating machine learning algorithm for predictive modelling
using KNIME.
• Build interactive visualization & analytics dashboard using
Microsoft’s Power BI.

21. Summary of the Courses - Roadmap
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MATHEMATICS
& STATISTICS
Day 2
BUSINESS
INTELLIGENCE
Day 5
MACHINE
LEARNING
Day 3 & Day 4
KNIME VISUAL
PROGRAMMING
Day 1 & Day 2

22. Course Outline
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Module 1 – Introduction to Data Science
1.1 Data Science and Big Data Analytics
1.2 Discipline in the Field of Data Science
1.3 Data Analytics & Business Intelligent
1.4 Machine Learning & Artificial Intelligent
1.5 Data Science Tools.
1.6 Application of Data Science
1.7 Career Opportunities in Data Science
Module 3 – Probability & Statistics
3.1 Probability
3.1.1 Combinatory
3.1.2 Bayesian Inference
3.1.3 Distributions
3.2 Statistics
3.2.1 Descriptive Statistics
3.2.2 Inferential Statistics
3.2.3 Hypothesis Testing
Module 2 – KNIME Visual Programming
2.1 Basic KNIME Interface
2.2 Workflow & Node
2.3 Read Data File
2.4 Columns & Row Filter
2.5 Aggregation & Binning
2.6 Visualization

23. Course Outline
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Module 4 – Machine Learning with
KNIME
4.1 Introduction to Machine Learning
4.2 Data Preparation & Preprocessing
4.3 Linear Regression Model
4.4 Logistic Regression Classification
4.5 Naive Bayes Classification
4.6 K-Nearest Neighbor Classification
4.7 Decision Tree Classification
4.8 Random Forest Classification
4.9 K-Means Clustering
4.10 Practical Hands-On 1
4.11 Practical Hands-On 2
4.12 Practical Hands-On 3
Module 5 – Business Intelligence with
Microsoft Power BI
5.1 Revision on Business Intelligent & Analytics
5.2 Introduction to Power BI Desktop
5.3 Query Editor
5.4 Data & Relationship View
5.5 Visualization & Dashboard Creation
5.6 Timeseries, Aggregation & Filter
5.7 Maps and Scatteplots
5.8 Creating an Interactive Dashboard
5.9 Practical Hands-On 4

24. PROJECT 1 – Student's Placement Acceptance Rate
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Predictive model using machine learning for student's placement acceptance rate to
academic programme offers by higher learning institutions.
Programmed offered
• Programme 1
• Programme 2
• Programme 3
Current Application Process
Login Apply
WAITING FOR
RESULT
• Students don't have idea the chances of they being accepted for selected programme.
• Selection criteria just minimum requirement. No statistics of acceptance.

25. Machine Learning Approach
SAINS
SASTERA
Variables
• Aliran
• Jumlah A+
• Jumlah A-
• Jumlah A
• Jumlah Kredit
• Markah Kredit
• Lulus BM
• Laluan Khas
Aliran SPM Analytics & Prediction
BIDANG A
Bidang Teras NEC
BIDANG B
BIDANG C
MODEL_NEC_A
MODEL_NEC_B
MODEL_NEC_C
Predictive Model
Result
Program Matching
Keputusan Diterima
Peluang Diterima

26. Machine Learning Process for Deep Learning
NN
Train Data
80% (98560)
ML Algorithm
Test Data
20% (24640)
Apply Model
Performance AccuracyDATA CLEANING
AND PREPARATION

27. The ML Algorithm using RapidMiner Studio

28. Performance Vector RUN 2
Training set
24640
true
DITERIMA
true
TIDAK_DITERIMA
Class
Precision
pred. DITERIMA 9821 2764 77.32%
pred.
TIDAK_DITERIMA
2878 9577 76.89%
MODEL ACCURACY = 77.10%
• Peratusan keputusan tepat adalah
77.10%.
• 77.32% peluang pelajar dapat
diterima sekiranya di predict
diterima.
• Cadangan supaya dapat menambah
variable seperti pilihan universiti
dan subjek A dapat meningkatkan
accuracy model.

30. Page 1
Academic
Profile

31. Page 2
Carian Kursus

32. PROJECT 2 – CHURN ANALYSIS
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• Churn analysis for telecommunication company.
• Machine learning model to predict churn's probability of new customers.

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Predicted 0 Predicted 1 Class Recall F-Measure
Actual 0 551 19 96.7% 0.959
Actual 1 28 69 71.1% 0.746
Class
Precision
95.2% 78.4%
Overall Accuracy 93%
• The model has high overall accuracy at 93%.
• Model has high performance to predict Churn=0 (stay) at 95.2%. Chances of customer being stay is 95.2%
when they predicted to be stayed.
• Chances of customer being churn is at 78.4%.
• F-measure in this model show that slightly different of precision & recall at 0.213 (21.3%). This is a measure
to seek the balance between Precision & Recall.

35. PROJECT 3 – CUSTOMER SEGMENTATION
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• Identify the customer segment based on loyalty and satisfaction
level.

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Clustering Model
Cluster_0
SUPPORTERS
Cluster_1
FANS
Cluster_2
ALIENATED
Cluster_3
ROAMERS
• Cluster_1 is group of
clients that are satisfy with
the shopping experience &
loyal with the product.
• Cluster_0 is group of
customers that are not
happy with the shopping
experience but they love
the brands.
• Cluster_3 is group of
customers that satisfy with
the shopping experience
but not loyal to the product.
• Cluster_2 is group of
clients that are not loyal &
satisfy.

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StrategicAction & Communication - Prescriptive
Cluster_0
SUPPORTERS
Cluster_1
FANS
Cluster_2
ALIENATED
Cluster_3
ROAMERS
• Study on what make customer in
Cluster_0 maybe can help to
understand what probably the
cause make them not satisfy with
the shopping experience.
• To retain the customer in
Cluster_1, conduct survey on
what things can be done to
improve the service and product.
• Loyalty of customers in Cluster_3
could be improve by increasing
the loyalty program, such as
a. Membership program.
b. Discount offer.

38. SKILLS GAIN AFTER COMPLETION
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• Foundation of mathematical and statistical concepts and theory in data analytics.
• Apply four main analytics spectrum – Descriptive, Diagnostic, Predictive and
Prescriptive Analytics.
• Technical skill of using KNIME Analytics Platform for building machine learning model
for predictive analytics.
• Technical skill of using Power BI to develop business intelligence dashboard.

39. Practical High Quality Hands-On
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Accounting, Banking & Finance
• Churn prediction model
• Customer segmentation
• Credit risk assessment
Cyber Security
• Credit card fraud detection
Marketing & Sales
• Market demands analytics
• Forecasting
• Sales dashboard
Recommender System
• E-commerce recommender system
• Movie recommender system

40. Targeted Audience
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• Marketing Manager
• Sales Manager
• Financial, Accountant & Banking Professional
• Researchers & Academician
• Engineers
• IT Professional
• Recent Graduates in Bachelor & Master Degree

41. Career Opportunities
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DATA SCIENCE CERTIFICATION
• Data Scientist
• Data Science Consultant
• Machine Learning Engineer
• Machine Learning Developer
• Business Intelligent Analyst
• Business Intelligent Consultant
• Business Intelligent Developer
MACHINE LEARNING CERTIFICATION
• Machine Learning Engineer
• Machine Learning Developer
MICROSOFT POWER BI CERTIFICATION
• Business Intelligent Analyst
• Business Intelligent Consultant
• Business Intelligent Developer

42. OUR TRAINING VALUE PROPOSITION
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STRUCTURED CURRICULUM FOR
REAL-BUSINESS APPLICATION
PRACTICAL CASE-STUDY WITH
REAL PROJECT FOR SPECIFIC NICHE
INDUSTRY
BUILD AUDIENCE
PORTFOLIO THROUGH
CASE-STUDY & ASSESSMENT

43. ALA - CARTE COURSE
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MACHINE LEARNING WITH
KNIME ANALYTICS PLATFORM
Learn to create Machine Learning algorithm
with visual programming approach (No Coding
require)
3 – Days
BUSINESS INTELLIGENT WITH
POWER BI
Learn how to use Microsoft’s Power BI Desktop
for dashboard visualization & analytics.
2 – Days

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Thank you very much.
Have any question?