I am a professional data scientist with a strong background in statistics, development studies, data science, and machine learning. With over four years of work experience, currently working as a statistician for the World Bank. During this, I have developed an extensive knowledge of data analysis and visualization techniques. I am proficient in programming languages such as Python and R and possess excellent analytical and problem-solving skills. My passion for machine learning and my ability to communicate complex data insights in a clear and concise manner make me an asset to any team. I am dedicated to using data to drive meaningful change and help organizations make informed decisions.
Skills and Experiences:
Skills and Experiences:
- Proficient in programming languages such as Python and R
- Strong knowledge of machine learning algorithms and techniques
- Experience with data visualization tools like Tableau and Matplotlib
- Strong analytical and problem-solving skills
- Familiarity with big data technologies like Hadoop and Spark
- Understanding of statistical concepts and techniques
- Experience with data warehousing and ETL processes
- Familiarity with deep learning frameworks such as TensorFlow and PyTorch
- Experience with natural language processing and text mining
- Strong communication skills to effectively present and explain data insights to stakeholders
Research Interests:
- Predictive modeling: Developing and implementing machine learning models to make predictions and identify patterns in data. This could include developing models for predictive maintenance, fraud detection, or customer churn.
- Natural language processing (NLP): Applying statistical techniques and machine learning algorithms to analyze and understand human language. This could involve sentiment analysis, text classification, or entity recognition.
- Time series analysis: Using statistical methods to analyze time-dependent data and identify patterns and trends. This could include forecasting stock prices, predicting future sales, or analyzing trends in weather data.
- Experimental design: Designing and conducting experiments to test hypotheses and evaluate the effectiveness of interventions. This could include testing the impact of a new drug on patient outcomes.
- Data visualization: Using visualization techniques to communicate complex data insights to stakeholders. This could involve developing interactive dashboards or creating compelling data visualizations to support decision-making.
Industry Projects:
- Flower Species Prediction
- Diabetes Prediction
- Uber Case Study
- Credit Card Fraud Detection
- Sales Forecasting
- Data Warehousing Case Study
- Real-Time Twitter HashTag Analysis using Spark
- Streaming Recommendation System using ALS (Alternating Least Squares)
- Market Basket Analysis using Apriori Algorithm
- Ride Fare Prediction
- Telecom Churn Prediction
- Recommendation System using PCA and KNN
- E-Commerce Assignment
- Speech Recognition
- Gesture Recognition
