About Me
I am an experienced Data Scientist proficient in R, Python, and JavaScript programming languages, I possess a comprehensive skill set in the field of Data Science. I have acquired practical expertise in various Machine Learning algorithms such as Random Forest, Support Vector Machines (SVM), Gradient Boosting, K-Nearest Neighbors (KNN), Regression, and more. I have successfully developed end-to-end Machine Learning models, starting from their inception to deployment. Additionally, I have utilized Natural Language Processing (NLP) techniques to create chatbots and design neural network models to process complex information.
Throughout my career, I have focused on developing key performance indicators (KPIs) and constructing Business Intelligence (BI) analytical dashboards and applications. These solutions have empowered businesses to leverage machine learning capabilities, enabling them to make informed decisions and drive intelligent decision-making systems. My expertise lies in analyzing and engineering data and conducting ad-hoc analysis to extract valuable business insights through exploratory data analysis and statistical techniques. Furthermore, I have a proven track record of building robust Machine Learning solutions.
Apart from my technical skills, my biggest strength lies in my adaptability and ability to learn efficiently. I am not merely a coder but dedicated to building meaningful and effective solutions. I invest the time and effort to thoroughly comprehend the problem at hand, optimize my coding practices, and go the extra mile to ensure successful project completion.
Business Intelligence
- WebFocus
- Power BI
- Tableau
Data EnGineering
- SQL/ T-SQL
- Oracle SQL/ PL-SQL
- PostgreSQL
- SQLAlchemy/sqlite 3
- ETL(Extract, Transform, Load)
- Pandas
- BigQuery
- Snowfalke
- Data Warehousing
DATA SCIENCE
- Programming Languages: Python, R, Js
- Machine Learning: Supervised Learning, Unsupervised Learning, Regression, Classification, Clustering, Dimensionality Reduction
- Data Manipulation and Analysis: Pandas, NumPy, SQL, Data Cleaning, Exploratory Data Analysis (EDA)
- Data Visualization: Matplotlib, Seaborn
- Statistical Analysis: Hypothesis Testing, Probability Theory, Statistical Modeling
- Natural Language Processing (NLP): Text Mining, Sentiment Analysis
- Deep Learning: TensorFlow, Keras
- Time Series Analysis: ARIMA, Exponential Smoothing
Machine Learning by standford university - Andrew ng
Skills : Logistic Regression, Artificial Neural Network, Machine Learning (ML) Algorithms, Machine Learning (ML) Algorithms.
Managing Machine Learning Projects with Google Cloud
Skills : ML Use case, Build, train, and evaluate an ML model, data characteristics and biases that affect the quality of ML models.
Python and Statistics for Financial Analysis
Skills : ML Use case, Build, train, and evaluate an ML model, data characteristics and biases that affect the quality of ML models.
Machine Learning with Python
Skills : Logistic Regression, Artificial Neural Network, Machine Learning (ML) Algorithms, Machine Learning (ML) Algorithms
REinforcement Learning for Trading
Skills : Reinforcement Learning, Artificial Intelligence (AI), Q-Learning, Deep Q-Network (DQN), Trading indicators( Exponential moving averages (EMA), Relative Strength Index (RSI) & etc..)
Machine Learning
- Machine Learning By Andrew Ag ( Stanford University)
- Python and Statistics for Financial Analysis ( The Hong Kong University of Science and Technology )
- Managing ML Projects with Google Cloud ( Google Cloud )
- Machine Learning with Python
Deep Learning
- Deep Learning Fundamentals
- Accelerating Deep Learning with GPUs
- Deep Learning with TensorFlow
Applied Data SCience
- Python for Data Science
- Data Analysis with Python
- Data Visualization with Python
Big Data
- Hadoop 101
- MapReduce and YARN
- Spark Fundamentals I
- Spark Fundamentals II
- Spark MLlIB
- Exploring Spark's GraphX
