M O H A M E D I HAB K H A L I FA
Hardworking psychology graduate, Python programmer, and junior data scientist with a long record of
academic success. Acquired a solid research experience and the necessary skills for devising research
designs, data gathering, advanced statistical analyses, and academic writing. More recently, became
well-versed in Python programming, data science and machine learning. I have a keen interest in human
intelligence, artificial intelligence, and their intersection.
CONTACT
EDUCATION
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MA STE R OF SCIE NCE :
CLI NIC AL PSY C HOLOGY AND ME NT AL HE ALT H
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SEP 2019 NOV 2020
Swansea University - Swansea, United Kingdom
Sporting, Alexandria
Graduated with Distinction (equivalent to 4.0 GPA)
https://github.com/Mo-Khalifa96
Distinctions in: Psychotherapy, Psychology of Health,
Applied Behavioural Analysis, Neuropsychology, and
Statistical and Research Methods
SKILLS
Dissertation: "Evidence of outcome interaction during
Cognitive Behavioral Therapy (CBT)
Psychological Assessment
Research Skills (quantitative and
qualitative)
Academic and Critical Writing
Python Programming
human diagnostic learning and the influence of
schizotypy" (Distinction)
BA CHE L OR OF SCIE NCE: PSYCH O L O G Y
Eastern Mediterranean University - Northern Cyprus
MAR 2015 JAN 2019
Graduated with 3.81 GPA
Awarded the Eastern Mediterranean University 50%
Data Analysis
Scholarship
Machine Learning
Clinical psychology (A), Counseling psychology (A),
Algorithmic Trading
Psychopathology (A), Physiological psychology (A),
SPSS
Neuropsychology (A)
INTERESTS
HIG H SC HOOL DIP LOM A
Cognitive Science
Perception
4E Cognition
Epistemology
Psychopathology
Philosophy of Mind
Artificial Intelligence
Data Science
Machine Learning
Data Analysis
Deep Learning
Programming
Bayesian Brain
Writing
Taymour English School - Alexandria, Egypt
Math/Science (graduated with 66%)
SEP 2013 JUL 2014
PROFESSIONAL
DEVELOPMENT
MAY 2023 JUL 2023
SUPERVISED MACHINE LEARNING: CLASSIFICATI ON
IBM – Online
Learned to develop, evaluate and optimize machine learning classification models for classifying
categorical outcomes. Covered a wide variety of classification algorithms, including Logistic
Regression, K-Nearest Neighbors, Support Vector Machines, and Decision Trees, as well as the
essential error metrics for evaluating and understanding their performances. Covered, in
addition, each of the ensemble methods, bagging, boosting, random forests, and stacking.
Importantly, also, covered the essential approaches and techniques for model interpretation,
including permutation feature importance, partial dependence plots, and developing surrogate
models. Lastly, learned how to deal with imbalanced classes and extremely skewed datasets.
JAN 2023 MAR 2023
SUPERVISED MACHINE LEARNING: REGRESSION
IBM – Online
Had a general introduction to the world of machine learning more generally, and regression
more particularly. Learned to develop, evaluate and implement regression models as well as
techniques to optimize them to derive the best model performances. The course covered
everything from feature engineering and preprocessing (e.g., feature scaling, data splitting,
recursive feature elimination) to model development and hyperparameter tuning (e.g., ridge,
lasso, and elastic net regularization) to model optimization (e.g., grid search, gradient descent).
Finished the course with High Honors.
DEC 2022 JAN 2023
EXPLORATORY DAT A AN ALYSIS FOR M ACHINE LEARNING
IBM – Online
Covered the essential data analysis tasks required before building machine learning models.
Learned how to approach and analyze data to derive insights and understand it better, and how
to use these insights to guide the processes of engineering and preparing the data for
subsequent model development. Also, learned the different types of statistics, frequentist and
Bayesian statistics, and the hypothesis testing approach pertaining to both.
AUG 2022 SEP 2022
DATA ANALYSIS WIT H PYTHON
IBM – Online
Learned how to employ Python’s powerful data analysis libraries to perform data analysis, with a
heavy focus on data analysis for machine learning (e.g., feature standardization, dealing with
categorical variables, etc.). Learned some basic statistical analyses, from simple regression
analysis to analysis of variance testing. Also, learned how to develop regression models for
tasks such as predictive pricing, covering linear regression and polynomial regression. Finally,
covered some essential techniques for model parameter tuning and model optimization.
AUG 2022 AUG 2022
PYTHON PROJECT FOR DAT A SCIENCE
IBM – Online
Completed a Python project covering essential data science tasks. The project involved web
scraping stocks data for different companies and applying careful data analysis and
visualizations to derive insights and form a narrative about their performances over the years.
JUL 2022AUG 2022
PYTHON FOR D ATA SCIENCE, AI, & DEVELOPME NT
IBM – Online
Learned to employ essential Python libraries for data analysis and data science, such as Numpy,
Pandas, and Beautiful Soup. Learned about how to collect, analyze and format data from
databases, the different types of databases (e.g., SQL and NoSQL), web scraping, and
interacting with APIs.
JUL 2022 JUL 2022
DATA ANALYTICS ESSENTIALS
IBM – Online
Covered the fundamentals of data analytics and the different sub-specialities within the field,
including data engineering, data analysis, data science, business analytics, and business
intelligence analytics. Learned about the different data structures (e.g., SQL, NoSQL) and data
formats (e.g., excel, json), and common data sources (e.g., databases, data warehouses, data
marts, data pipelines), and had an overview of the data analysis process from data mining and
collecting to data mining to data visualization.
APR 2022 MAY 2022
DATA ANALYSIS USING PYTHON
University of Pennsylvania – Online
Learned how to use Python’s popular libraries for data analysis, including Pandas, Numpy, and
Matplotlib, in order to generate insights out of complex datasets, uncover trends, and visualize
the data.
MAR 2022 APR 2022
INTRODUCTION TO P YTHON PROGRAM MING
University of Pennsylvania – Online
Learned everything from basic Python syntax to custom functions to Python data structures and
how to utilize them to write fully functional Python programs, such as designing and
implementing a simple online banking system (see projects below).
MAR 2021 APR 2021
PYTHON DATA STRUCTURES
University of Michigan – Online
Learned how to use Python’s built-in data structures (lists, dictionaries, tuples, etc.) to write
increasingly complex and advanced programs. In addition, learned to read and write simple
files, data scraping, and text parsing.
FEB 2021 MAR 2021
PROGRAMMING FOR EVERYBODY
(GETTING STARTED WITH PYTHON)
University of Michigan – Online
Learned the basics of Python programming, covering the basic syntax, conditional statements,
loops, variables, and user-defined functions.
EXPERIENCE
FR E E LAN CE AC AD E M I C M E N T OR
NOV 2021 PRESENT
Academic Minds - Online
My role involves supporting university students with academic work in my field of
study, editing and marking academic papers, and helping with statistics.
C LI N I C AL PS YC HOL OG Y TR AI N E E
El-Nozha Hospital - Alexandria, Egypt
My experience involved shadowing professional psychologists, interacting with
hospitalized patients, reviewing reports, and more generally, learning about
various mental health conditions, their consequences on the victims’ lives, and
the therapies most commonly used for treating each.
PROJECTS
I have completed multiple projects with Python, ranging from data analysis and machine learning to
web scraping to bot development and algorithmic trading. I have included a sample of my projects
below. To preview all of the projects I have completed, you can visit my GitHub profile and view my
repositories (https://github.com/Mo-Khalifa96).
T R AN S AC T IO N F R AU D D E T E C T IO N ( M AC H IN E L E AR N IN G F O R C L AS S IF IC AT IO N )
https://bit.ly/nbviewer_github_Mo -Khalifa96_Transaction-Fraud-Detection
D AT A AN A L Y SI S & M AC H IN E L E AR N IN G F O R P R E D IC T IV E P R IC IN G
https://bit.ly/nbviewer_github_Mo -Khalifa96_Data-Analysis-and-Machine-Learning
M AC H IN E L E AR N IN G F O R P R E D IC T IV E P R I C IN G (P R E D IC T IN G H O U SE P R IC E S )
https://bit.ly/nbviewer_github_Mo -Khalifa96_Machine-Learning-for-Predictive-Pricing
F O R E X T R AD IN G B O T
https://github.com/Mo -Khalifa96/Forex-Trading-Bot
W E B SC R AP IN G AN D AN A L Y Z IN G ST O C K S D A T A
https://bit.ly/nbviewer_github_Mo -Khalifa96_Web-Scraping-and-Analyzing-Stock-Data
W E B SC R AP IN G P D F F IL E S U S IN G P Y T H O N
https://bit.ly/mybinder_github_Mo -Khalifa96_Web-Scraping-PDF-Files
S AL E S A N AL Y S IS
https://bit.ly/nbviewer_github_Mo -Khalifa96_Sales-Analysis
B U SIN E S S D AT A AN AL Y SI S
https://bit.ly/nbviewer_github_Mo -Khalifa96_Business-Data-Analysis-with-Python
JUL 2019 JUL 2019
