Programming & Data
Core Stack
Python
Writing clean, efficient Python for data manipulation, analysis, and building end-to-end data pipelines.
Pandas
Using Pandas DataFrames for data ingestion, transformation, cleaning, grouping, and aggregation tasks.
NumPy
Applying NumPy for numerical computing, array operations, and mathematical data transformations.
Analysis Techniques
Data Analysis
Data Cleaning
Handling missing values, removing duplicates, fixing data types, and normalizing datasets for accurate analysis.
Data Wrangling
Reshaping, merging, and transforming raw data into structured formats suitable for analysis and modelling.
Exploratory Data Analysis
Uncovering patterns, distributions, correlations, and outliers using statistical summaries and visual analysis.
Feature Engineering
Creating, selecting, and transforming features to improve machine learning model performance.
Charts & Graphs
Data Visualization
Matplotlib
Creating line charts, histograms, scatter plots, and custom multi-panel figures for data storytelling.
Seaborn
Producing statistical visualizations including heatmaps, pair plots, and distribution charts for EDA.
Predictive Modelling
Machine Learning
Basic ML Concepts
Understanding core concepts including supervised learning, classification, regression, and model evaluation metrics.
Scikit-Learn
Applying Scikit-Learn to implement and evaluate classification and regression models on real datasets.
Tools & Platforms
Other Skills
Google Colab
Running data science notebooks in the cloud with GPU support, collaborating and sharing analysis work.
Business Insights
Translating complex data findings into clear, actionable recommendations that drive business decisions.