The slides were used in a trial session for a student aiming to learn python to do Data science projects . The session video can be watched from the link below https://youtu.be/CwCe1pKOVI8 I have over 20 years of experience in both teaching & in completing computer science projects with certificates from Stanford, Alberta, Pennsylvania, California Irvine universities. I teach the following subjects: 1) IGCSE A-level 9618 / AS-Level 2) AP Computer Science exam A 3) Python (basics, automating staff, Data Analysis, AI & Flask) 4) Java (using Duke University syllabus) 5) Descriptive statistics using SQL 6) PHP, SQL, MYSQL & Codeigniter framework (using University of Michigan syllabus) 7) Android Apps development using Java 8) C / C++ (using University of Colorado syllabus) Check Trial Classes: 1) A-Level Trial Class : https://youtu.be/v3k7A0nNb9Q 2) AS level trial Class : https://youtu.be/wj14KpfbaPo 3) 0478 IGCSE class : https://youtu.be/sG7PrqagAes 4) AI & Data Science class: https://youtu.be/CwCe1pKOVI8 https://elmalla.info/blog/68-tutor-profile-slide-share You can get your trial Class now by booking : https://calendly.com/ahmed-elmalla/30min And you can contact me on https://wa.me/0060167074241 by Python & Computer science tutor in Malaysia

1. Data Science & AI
Ahmed Elmalla
https://elmallla.info

2. Students Brain
Building brian chunks / Physical Exercise
Creativity vs emotional stability
Focus mode vs Diffuse mode
Working Memory Vs Permanent memory

3. Data Science
● “…a field that deals with unstructured, structured data, and semi-structured
data. It involves practices like data cleansing, data preparation, data analysis,
and much more.
● Data science is the combination of statistics, mathematics, programming, and
problem-solving; capturing data in ingenious ways; the ability to look at things
differently; and the activity of cleansing, preparing, and aligning data.”

4. Data Science Road Map
● Descriptive Statistics
● Probability
● Python (OOP + Pandas + Numpy + Scipy)
● Data Cleaning: One of the MOST important skills that you need to master to
become a good data scientist, you need to practice on many datasets to
master it.
● Data Visualization (using Matplotlib and seaborn )
● Dashboards (Tableau)
● SQL and DB (SQL for Data Analysis)
● Time Series Analysis

5. Descriptive Statistics
It is used to summarize the main features of a data set, such as its central tendency, variability,
and distribution. Descriptive statistics can be used to describe a data set in a way that is easy
to understand and interpret. Some of the most common descriptive statistics include:
● Mean: The mean is the average of all the values in a data set.
● Median: The median is the middle value in a data set when all the values are arranged in
order from least to greatest.
● Mode: The mode is the most frequent value in a data set.
● Range: The range is the difference between the largest and smallest values in a data set.
● Variance: The variance measures how spread out the values in a data set are.
● Standard deviation: The standard deviation is a measure of how much variation there is
from the mean in a data set.

6. Probability
● Model uncertainty: Data is often noisy and incomplete, which means that there is
uncertainty about the true values of the data. Probability can be used to model this
uncertainty and to make inferences about the data.
● Make predictions: Probability can be used to make predictions about future events, given
the observed data. For example, a data scientist might use probability to predict the
likelihood of a customer clicking on an ad, or the likelihood of a patient recovering from a
disease.
● Detect anomalies: Probability can be used to detect anomalies in data. An anomaly is an
observation that is significantly different from the rest of the data. Probability can be used
to identify anomalies by calculating the likelihood of an observation occurring.
● Cluster data: Probability can be used to cluster data into groups of similar observations.
This can be useful for identifying patterns in the data and for making predictions about

7. Python Libraries (Pandas ,Numpy , Scipy )
Pandas is a Python library that provides high-performance, easy-to-use data structures and data
analysis tools. It is widely used by data scientists for data manipulation, analysis, and
visualization.
NumPy is a Python library that provides a high-performance multidimensional array object, along
with a suite of functions for working with arrays. It is used in a wide variety of domains, including
scientific computing, data science, and machine learning.
SciPy is a scientific computation library that uses NumPy underneath. SciPy stands for Scientific
Python. It provides more utility functions for optimization, stats and signal processing.

8. Data Cleaning
● Identifying and correcting errors: This includes fixing typos, correcting incorrect values,
and removing duplicate data.
● Imputing missing values: This involves filling in missing data with either estimated values
or by removing the rows or columns with missing data.
● Dealing with outliers: Outliers are data points that are significantly different from the rest
of the data. They can be caused by errors or by unusual circumstances. Outliers can be
removed, or they can be treated as separate categories.
● Data formatting: This involves converting the data into a format that is easy to use and
analyze. This can involve changing the data type, or converting it into a different file
format.
● Data normalization: This involves scaling the data so that all of the values are within a
similar range. This can make it easier to compare the data and to identify trends.

9. Data Visualization

10. Data Visualization Libraries
● Matplotlib and Seaborn are two popular Python libraries for data visualization. Matplotlib is a
general-purpose library that can be used to create a variety of charts and graphs. Seaborn is built on
top of Matplotlib and provides a number of high-level functions for creating statistical plots.
● Creating bar charts: Bar charts are used to show the distribution of data. They can be used to show
the number of observations in each category, or the average value of a variable in each category.
● Creating line charts: Line charts are used to show the trend of data over time. They can be used to
show how a variable changes over time, or how two variables are related to each other.
● Creating scatter plots: Scatter plots are used to show the relationship between two variables. They
can be used to show how two variables are correlated, or to identify outliers.
● Creating histograms: Histograms are used to show the distribution of data. They can be used to show
the number of observations in each range of values, or the probability of a value occurring.
● Creating box plots: Box plots are used to show the distribution of data. They can be used to show the
median, quartiles, and outliers of a data set.

11. Dashboards - Tableau
A dashboard is a visual representation of data that helps users to monitor and analyze
information. Dashboards are typically used in business settings to track performance, identify
trends, and make decisions.
Tableau is a business intelligence (BI) tool that allows
users to create interactive dashboards.
Tableau is a popular choice for dashboard creation
because it is easy to use and has a wide range of
features.

12. SQL & DB
It helps data scientists access, manage, and analyze large data sets to derive
insights that can drive better business decisions.
For professionals looking to build a career in data science, learning SQL is a
must-have skill

13. Time Series Analysis
● Time series analysis is a statistical technique that is used to analyze data that is
collected over time. It is used to identify patterns and trends in the data, and to
make predictions about future values.
● Forecasting: Time series analysis can be used to forecast future values of a
variable. This can be useful for making business decisions, such as setting prices
or planning production.
● Identifying trends: Time series analysis can be used to identify trends in the data.
This can be useful for understanding the behavior of the variable and for making
predictions about future values.

14. Storing Student score in a 2D Array

15. Java Vs Python

16. Java

17. Counting Students with score > 4

18. Data Structure in Python

19. Data Structure Examples

20. Coding alone not enough
Analysis, Design, Coding, Testing, Maintenance
Methodologies like waterfall, Agile

21. Show Case 1 - Healthcare Mobile App
Mobile app that can connect to
a medical device.
Read patients oxygen level,
glucose level and ECG heart
waves.
Send it in real time to clinicians
and patients guardians

22. Project 1 Images
Patient can allow
certain Clinicians &
patient guardians to
monitor his glucose
levels, oxygen levels,
ECG waves, blood
pressure and more.

23. Showcase Videos
How guardians / Clinicians can monitor patients
https://youtu.be/CxRpQkLE9uA
Live Monitoring for patients: https://youtu.be/uz3wkkcGdHA
Patient Offline showcase (without connecting to Checkme) :
https://youtu.be/wmGJE1QNHo8
Diabetes Diary - Smart Health https://youtu.be/ztlcYvRVByE

24. Showcase 2 - Facial recognition for patients
Recognizing accident patients
using AI
Using Smartglass to see
patients details in the prism
Using Smart Watch to
measure body vitals
Python + OpenCV

25. Teaching Certifications
*Studied Learning how to learn with Barbra Oaklay from Oakland University
*Learning how to teach youth from Arizona State University (waiting for
Exam)
*Uncommon Sense Teaching (In progress)
*Practical Teaching with Technology (In progress)

26. Subjects I teach (more details here)
*American syllabus from college Board (AP CS A)
*IGCSE CS A- Level

27. Testimonials
“Mr. Ahmed is dedicated to helping my
daughter achieve her goals and is able to
easily explain difficult concepts” Madam Lee
“So far from a few tutors for A-Levels
Computer Science I’ve tried, Mr Ahmed has
been the best at explaining at teaching the
concepts so far” Joy Chandran
“The class went smoothly and it was very
understandable, i liked that you were not too
slow but fast in a good way” Al-Shammry

28. Qualifications (more details here)
I am professional computer engineer with certificates from Stanford, Alberta,
and California Irvine universities.

29. Why should you you hire me ?
I experienced the software development process
I have worked with world class teams from all over the
world
I developed software solutions on international level
I listen to my students and enjoy seeing them excel

30. At the End
Thank you for your time
https://elmalla.info/#portfolio

31. Smart Cameras Projects (more details here)
Smart Cameras are used
to detect and locate the
exact position of a 80
micron wire.
Bonding is done using 0.8
mm bonding tip on a 1.2
mm space

32. Desktop application Inspection machine
Desktop application was
developed using VB to
control cartesian robotic
arm with a smart camera
to measure product
dimensions

33. German Passport Machinery (more details here)
German passport
production facility, a trip
for Machine
commissioning

34. Camera single Line Diagram (Machine)

35. Textile Machinery
Training in a german
machinery manufacturer
in 1999

36. Safety Syringe Machinery
Worked with swiss teams
around the globe to deliver
MedTech machinery for US
customers.
Safety syringe is being
inspected with smart
cameras

37. AWCS system Installation (more details here)
Installation of an
automated waste
collection system in the
city of Vällingby Parkstad,
Sweden