Data Scientist Engineer
  • Type Learning
  • Level Intermediate
  • Time Months
Additional Details

Data Scientist Engineer

Issued by Talent Path

Earners of this designation are critical thinkers and technologists who apply advanced analytics skills, using data science models and tools, to create data-driven insights. Individuals who earn this badge use their industry knowledge, professional best practices and communication skills, along with their hands-on technology acumen, to derive actionable business intelligence for stakeholders and decision-makers. (D1)

  • Type Learning
  • Level Intermediate
  • Time Months
Additional Details

Skills

  • Advanced Learning Algorithms
  • Advanced SQL
  • Artificial Neural Networks
  • AWS Cloud Deployment and Operation
  • Basic Data Visualization Using Seaborn
  • Basic Docker
  • Business Presentations
  • Business Process Modeling
  • Classification ML
  • Convolutional Neural Network (CNN)
  • Creating And Connecting Local Relational Databases
  • Critical Thinking
  • Data Analysis
  • Data Mining
  • Data Pipelines
  • Data Preperation in Pandas ML
  • Data Visualization
  • Data Wrangling with Pandas
  • GPU Multiprocessing
  • Heroku Deployment
  • HTML And CSS
  • Jinja2
  • Jupyter Notebook
  • Leadership
  • Matplotlib
  • NLP Using Python & NLTK
  • Plotly
  • Problem Solving
  • Pyspark
  • Python for Data Science
  • Python For Visual Analysis
  • Regression ML
  • Scikit-learn
  • TensorFlow
  • Visual Studio Code

Earning Criteria

  • Passed rigorous entry examinations and multiple rounds of personnel meetings to assess technological knowledge, practical capabilities, emotional intelligence, motivation, and adaptability.
  • Successfully engaged in 240 hours of online training focused on the comprehensive application and analysis of modern data science techniques.
  • Verified proficiency in data mining, cleaning, analysis, modeling, performance tuning, and deployment of models using custom APIs, Python, Multiprocessing Techniques, SQL, Local Databases, AWS, Flask, Jinja Templating, Docker, Heroku, Git Pages.
  • Demonstrated, within a working group, the ability to produce in a highly collaborative environment, acting as lead and follower, giving and receiving feedback through weekly presentations to technical resources and executive leadership.
  • Completed multiple business case projects by interfacing with corporate executives to assess industry and market trends, applying research and technological skills, and proposing solutions shown through training and visualizing ML Models.