Skills:

Languages: Python, SQL, R, Java

Databases: Spark Databricks, Snowflake, Redshift, Microsoft SQL Server, Postgres, Neo4j, DynamoDB, IBM DB2

Cloud Technologies: AWS

Version Control: Git, SVN

Proficient in the entire Data Science process: Define the question, Get the data, Explore the data, Model the data, Interpret the results, Evaluate the impact of the results, Communicate the results.

Machine Learning – Modeling Algorithms:

– Supervised Learning:

1. Linear Regression
2. Logistic Regression
3. Decision Trees
4. Random Forest
5. KNN – K Nearest Neighbor
6. SVM (Support Vector Machines) for Classification
7. Neural Networks
8. Convolutional Neural Networks for Image Analysis

– Unsupervised Learning:

1. K-means Clustering
2. Hierarchical Clustering
3. Principal Component Analysis

Other Skills:

Data Mining, Data Cleaning, Data Wrangling, Data Visualization – Tableau, Storytelling, Ethics in Data Science, Probability and Statistics, LCA (Latent Class Analysis), FA (Factor Analysis), Sentiment Analysis, Data Visualization (Glyphs for Multidimensional Data, Gephi for Network Data, Tableau and R for Spatial Data, Shiny App for Interactive Dashboards), Time Series and Forecasting.