With over seven years of dedicated experience as a data analyst, I bring a wealth of expertise to the table, ensuring the delivery of top-tier data analytics services. My proficiency extends across a multitude of statistical software packages, including
Stata, Excel, Minitab, IBM SPSS, Gretl, JASP, EViews, Python, and R.
My skills include :
Data Analysis and Statistical Expertise:
-Proficient in statistical analysis, hypothesis testing, and survey analysis, ensuring data-driven decision-making.
-Skilled in performing Analysis of Variance (ANOVA) for robust statistical comparisons.
-Expertise in both qualitative and quantitative analysis techniques to uncover valuable insights.
Data Management and Preprocessing:
-Adept at data cleaning and preprocessing, ensuring the accuracy and reliability of datasets.
-Experienced in transforming raw data into structured, usable information for analysis.
Data Visualization and Interpretation:
-Strong ability to create visually appealing and informative data visualizations.
-Capable of interpreting complex data and conveying meaningful insights to stakeholders.
-Proficient in Python, R, and SQL, enabling efficient data manipulation and analysis.
- Skilled in automating tasks and creating data-driven solutions through programming.
Data Mining and Pattern Recognition:
-Experienced in data mining techniques to uncover hidden patterns and trends.
- Proficient in pattern recognition and predictive modeling for informed decision-making.
Machine Learning - I'm proficient in both supervised and Unsupervised Machine Learning.
Supervised Learning - Including Support Vector Machine, Logistic Regression, Random Forest,
Decision Tree etc.
Unsupervised Learning - Proficiency in clustering algorithms (e.g., k-means, hierarchical
clustering) and dimensionality reduction techniques (e.g., PCA) for unsupervised learning tasks.
Before building any Machine learning model, I employ the following features to improve accuracy:
-Feature Engineering: I have the ability to select, transform, or create relevant features from raw data to improve the performance of machine learning models.
-Model Evaluation: I have familiarity with metrics like accuracy, precision, recall, F1-score, and area under the ROC curve (AUC-ROC) to evaluate the performance of machine learning models.
-Cross-Validation: I can conduct techniques like k-fold cross-validation to assess a model's performance and generalizability.
-Hyperparameter Tuning: I can optimize model hyperparameters using methods like grid search, random search, or more advanced techniques like Bayesian optimization.
-Overfitting Prevention: I possess techniques such as regularization and dropout to prevent overfitting in machine learning models.
-Ensemble Learning: I can employ ensemble techniques like bagging, boosting, and stacking to combine predictions from multiple models for improved accuracy and robustness.
Time Series Analysis - Univariate Time series - Including Autoregressive Integrated Moving Average
(ARIMA), Moving Average (MA), AutoRegressive (AR), ARCH, SARIMAX,
Seasonal Exponential Smoothing (ETS) Models, Holt-Winters Exponential
Smoothing, Simple Exponential Smoothing etc.
- Multivariate Time Series - Including Vector AutoRegressive and Structural
Streamlit Web Applications and Deployment.
If you seek a highly skilled and seasoned data analyst with a proven track record of success, your search ends here. My diverse skill set and extensive experience make me an ideal collaborator to help you achieve your objectives. You can rely on my commitment to delivering quality work within stipulated timelines. Let's join forces to extract meaningful insights from your data and drive transformative outcomes.
Machine Learning Algorithm
Time Series Analysis