specializing in ETL pipeline for big data project and Data Analysis. Experienced with all stages of the development cycle for complex data loads and predictive analysis projects. I took several Data Science focused courses at Applied AI course: Python programming, Machine Leaning, Linear Algebra, Probability and Statistics, and Natural Language Processing. Although my curriculum prepared me well for the fundamentals of Data Science (i.e. cs, statistics, and machine learning). I am a technology-savvy and mathematically-equipped who gets a good kick out of analyzing data and have good hands on experince in processing the Big data using distributed framework(spark, scala, python), data warehouse tool(snowflake), distributed database(cassandra, Hbase), distributed file sytem(HDFS, Auxillo) Machine Learning knowledge in:• Unsupervised algorithms- K-means clustering - hierarchical clustering- DBSCAN• Dimensionality reduction and visualization tool's:- PCA- t-sne• Supervised algorithms- Naive Bayes- Logistic Regression- Linear Regression- SVM- Tree-based algorithms: Decision Trees, Random Forest, Adaboost, Grandient Boosting Machine• Deep Learning- Multi-layer preception(MLP)- Convolution neural network(CNN)- Recuurent nueral network(RNN)- Time Series Analysis• Statistics: - exploratory data analysis using statistics tools- Probality theory- Distribution function Programming and framework knowledge in:• programming language- Python - Scala- Shell scripting- SQL• Big data framework.- Spark- Snowflake- Hadoop- Databricks- NumPy- SciPy- Pandas- Matplotlib- NLTK- tensorflow- Keras• Big data filesystem- HDFS- Azure Blob• Database- Postgress- Snowsql - HBase- Hive- Cassandra• Schedulers- Airflow• Cloud- Azure