Courses
Machine Learning Fundamentals
Statistical Methods for Data Science
Database Management Systems
Data Visualization and Communication
Python for Data Analysis
Big Data Technologies
Applied Predictive Modeling
Courses
Machine Learning Fundamentals
Statistical Methods for Data Science
Database Management Systems
Data Visualization and Communication
Python for Data Analysis
Big Data Technologies
Applied Predictive Modeling
Python, R, SQL, Bash.
Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, TensorFlow, Keras.
Supervised Learning, Unsupervised Learning, Regression, Classification, Clustering, Feature Engineering, Model Evaluation.
SQL Databases, Data Cleaning, ETL Processes, Data Warehousing Concepts.
Tableau, Power BI, Matplotlib, Seaborn, Interactive Dashboards.
Hypothesis Testing, Inferential Statistics, Descriptive Statistics, A/B Testing.
Jupyter Notebook, Google Colab, Git, GitHub, VS Code.
Artificial Intelligence, Big Data, Cloud Computing, Emerging Technologies.
Analytical Thinking, Strategic Planning, Complex Data Challenges.
Summary
Developed a machine learning model to predict customer churn for a simulated telecommunications company, identifying key factors influencing customer retention.
Summary
Conducted an in-depth exploratory data analysis (EDA) on a global superstore sales dataset to uncover sales trends, identify top-performing products, and analyze regional profitability.