Certificates#

Neo4j Graph Data Science Certified

  • General use of the Neo4j Graph Data Science Library

  • Graph Data Science workflow used during analysis

  • Using specific graph algorithms

Neo4j Certified Professional

  • Neo4j property graph model

  • Cypher queries

  • Graph data modeling

  • Importing data

  • Application development concepts

DataKitchen DataOps Fundamentals - May 2022

Course Overview

DataCamp Professional Data Scientist - May 2022

  • Time assessments on Data Management, Exploratory Analysis, Statistical Experimentation, Model Development, Coding for Production Environments, Communicating and Reporting

  • Case study with technical report and non-technical presentation.

DataCamp Professional Analyst - May 2022

  • Time assessments on Analytic Fundamentals, Exploratory Analysis, Data Management, Visualization and Reporting

  • Case study with technical report and non-technical presentation.

DeepLearning.AI Deep Learning Specialization- Dec 2021

  • Build and train deep neural networks, implement vectorized neural networks, identify architecture parameters

  • Use best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard NN techniques, apply optimization algorithms, and implement a neural network in TensorFlow

  • Use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end, transfer, and multi-task learning

  • Build a Convolutional Neural Network, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data

  • Build and train Recurrent Neural Networks and its variants (GRUs, LSTMs), apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform Named Entity Recognition and Question Answering

DeepLearning.AI - Machine Learning Engineering for Production (MLOps) - Sep 2021

  • Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements

  • Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application

  • Build data pipelines by gathering, cleaning, and validating datasets

  • Implement feature engineering, transformation, and selection with TensorFlow Extended

  • Establish data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas

  • Apply techniques to manage modeling resources and best serve offline/online inference requests

  • Use analytics to address model fairness, explain-ability issues, and mitigate bottlenecks

  • Deliver deployment pipelines for model serving that require different infrastructures

  • Apply best practices and progressive delivery techniques to maintain a continuously operating production system

FreeCodeCamp Data Analysis with Python - Jul 2021
FreeCodeCamp Machine Learning with Python - Jun 2021
IBM Data Science Professional - Jan 2021

Tools: Jupyter / JupyterLab, GitHub, R Studio, and Watson Studio

Libraries: Pandas, NumPy, Matplotlib, Seaborn, Folium, ipython-sql, Scikit-learn, ScipPy, etc.

Projects: random album generator, predict housing prices, best classifier model, Predicting successful rocket landing, dashboard and interactive map

CITI Group 2: Social Behavioral and Education Research Investigators - Oct 2019

This course is required for studies on sociological, psychological, anthropological or educational phenomena including observational and survey research and work with population and/or epidemiological studies.