Aspiring Data Scientist & Analyst
Turning Data into insights, Challenges into Solutions - A London Based Data Scientist with a Passion for Technology, Learning and Exploration.
Latest Work
McKinsey.org
Forward Program
British Airways
(Virtual Internship)Customer Insight
Deloitte
(Virtual Internship)Analytics Project
Citi
(VI )Financial M&A Analysis
Personal Project
Library Management System
Data Enthusiast
About Me
Brief initial presentation of myself and my previous experiences.
Available for work
Available for work
Available for work
MSc Data Science graduate from the University of Nottingham with hands-on experience in machine learning and data projects. Passionate about using data to solve real-world problems and aiming to grow in AI-focused roles.
Currently seeking data analyst opportunities.
Python
SQL
Power BI
Tableau
Microsoft Suite
R programming
Data Analyst & Logistic Coordinator
Foliage Outdoors
2021-2023
Data Analyst & Logistic Coordinator
Foliage Outdoors
2021-2023
Data Analyst & Logistic Coordinator
Foliage Outdoors
2021-2023
Cloud Reliability Engineer and Information Security
e-Emphasys Technologies
2023
Cloud Reliability Engineer and Information Security
e-Emphasys Technologies
2023
Cloud Reliability Engineer and Information Security
e-Emphasys Technologies
2023
How it works
Process Is Everything
Simple, streamlined process is what get's you results
1
Understand the Problem
I start by clearly defining the business objective and aligning analytical goals to ensure the work drives meaningful impact.
Step 1
2
Collect & Clean Data
I gather data from reliable sources and clean it by handling missing values, removing noise, and formatting it for consistency and accuracy.
Step 2
3
Explore & Analyze
I analyze patterns, trends, and correlations using statistical techniques and visualizations to extract valuable insights and guide decision-making.
Step 3
4
Modeling & Insights (for DS)
I build and fine-tune machine learning models—or perform advanced analyses—that translate complex data into actionable outcomes.
Step 4
5
Communication & Delivery
I present findings through compelling visualizations and narratives, ensuring stakeholders clearly understand the results and their implications.
Step 5
1
Understand the Problem
I start by clearly defining the business objective and aligning analytical goals to ensure the work drives meaningful impact.
Step 1
2
Collect & Clean Data
I gather data from reliable sources and clean it by handling missing values, removing noise, and formatting it for consistency and accuracy.
Step 2
3
Explore & Analyze
I analyze patterns, trends, and correlations using statistical techniques and visualizations to extract valuable insights and guide decision-making.
Step 3
4
Modeling & Insights (for DS)
I build and fine-tune machine learning models—or perform advanced analyses—that translate complex data into actionable outcomes.
Step 4
5
Communication & Delivery
I present findings through compelling visualizations and narratives, ensuring stakeholders clearly understand the results and their implications.
Step 5
1
Understand the Problem
I start by clearly defining the business objective and aligning analytical goals to ensure the work drives meaningful impact.
Step 1
2
Collect & Clean Data
I gather data from reliable sources and clean it by handling missing values, removing noise, and formatting it for consistency and accuracy.
Step 2
3
Explore & Analyze
I analyze patterns, trends, and correlations using statistical techniques and visualizations to extract valuable insights and guide decision-making.
Step 3
4
Modeling & Insights (for DS)
I build and fine-tune machine learning models—or perform advanced analyses—that translate complex data into actionable outcomes.
Step 4
5
Communication & Delivery
I present findings through compelling visualizations and narratives, ensuring stakeholders clearly understand the results and their implications.
Step 5
I am with you in every step
Projects
Data Science
My Data Science Projects in Action
Python
Machine Learning
Computer Vision
Python-OOP
Semi-Supervised Learning
Vega-Altair
Polynomial
SVR
Random Forest
Contrastive Learning
Why choose me
Why me as Data Partner
Communication
Effectively conveying complex data insights to both technical and non-technical stakeholders is crucial. Clear communication ensures that findings are understood and actionable.
Critical Thinking
Ability to analyze and evaluate data objectively allows for sound decision-making and problem-solving. Critical thinking helps in identifying patterns and drawing meaningful conclusions.
Communication
Effectively conveying complex data insights to both technical and non-technical stakeholders is crucial. Clear communication ensures that findings are understood and actionable.
Critical Thinking
Ability to analyze and evaluate data objectively allows for sound decision-making and problem-solving. Critical thinking helps in identifying patterns and drawing meaningful conclusions.
Collaboration
Working effectively with cross-functional teams, including engineers, business analysts, and domain experts, enhances the quality and impact of data-driven projects
Adaptability
The field of data science is ever-evolving. Being adaptable allows professionals to embrace new tools, methodologies, and challenges, ensuring continuous growth and relevance.
Collaboration
Working effectively with cross-functional teams, including engineers, business analysts, and domain experts, enhances the quality and impact of data-driven projects
Adaptability
The field of data science is ever-evolving. Being adaptable allows professionals to embrace new tools, methodologies, and challenges, ensuring continuous growth and relevance.
Attention to Detail
Precision is vital when handling large datasets. A keen eye for detail helps in identifying errors, inconsistencies, and nuances that could impact analysis outcomes.
Time Management
Balancing multiple projects and meeting deadlines require effective time management skills. Prioritizing tasks and managing workloads ensure timely delivery of quality results.
Attention to Detail
Precision is vital when handling large datasets. A keen eye for detail helps in identifying errors, inconsistencies, and nuances that could impact analysis outcomes.
Time Management
Balancing multiple projects and meeting deadlines require effective time management skills. Prioritizing tasks and managing workloads ensure timely delivery of quality results.
