Get Ready For a Modern Career in the Era of Automation and Industry 4.0

No Prior Experience Required

STEM (Science, Technology, Engineering and Mathematics) are the words which drive the future. NCLab courses will help you be in demand.

Computational On-Ramp

Computational Literacy
Skills Learned:
  • Computational thinking
  • Problem solving
  • Algorithmic design
  • Conditional logic
  • Accuracy
  • Attention to detail
  • Perseverance
  • Ability to learn from failure
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Data Literacy
Skills Learned:
  • Types and forms of data
  • Relational and non-relational databases
  • RDBMS and SQL
  • Excel spreadsheets
  • Data analytics and visualization
  • Data mining and governance
  • Data quality and controls
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Spatial Literacy
Skills Learned:
  • Understand the 3D space
  • Think spatially
  • Solve problems using visual-spatial reasoning
  • Extrude planar shapes to 3D
  • Move and rotate 3D shapes
  • Build 3D models
  • Export models for 3D printing
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Data Analytics

SQL Fundamentals
Skills Learned:
  • RDBMS and SQL
  • Write basic queries
  • Order and filter data
  • Combine multiple conditions
  • Group results
  • Use aggregate functions
  • Create and manage tables
  • Insert and delete data
  • Join tables
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Advanced SQL
Skills Learned:
  • Conditional expressions
  • SQL errors
  • Text operations
  • Search-and-replace
  • Sets, relations, bags
  • Subqueries
  • Advanced joins
  • Functions
  • Data types
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Predictive Data Analytics with Python
Skills Learned:
  • Import data from CSV files
  • Work with data frames
  • Clean and manipulate data
  • Visualize and present data
  • Simple linear regression
  • Multiple linear regression
  • Logistic regression
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Computer Programming

Intro to Computer Programming
Skills Learned:
  • Computational thinking
  • Logic and problem solving
  • Algorithms and programs
  • Syntax and code formatting
  • Loops and conditions
  • Variables and functions
  • Recursion
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Python Fundamentals
Skills Learned:
  • Calculations with Python
  • Text string operations
  • Loops and conditions
  • Variables and functions
  • Operations with data files
  • Visualization of data
  • Using Python libraries
  • Principles of software design
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Advanced Python
Skills Learned:
  • Introduction to OOP
  • Advanced concepts of OOP
  • Recursion, binary trees
  • Anonymous lambda functions
  • Iterables, iterators, generators
  • Filters and maps. decorators
  • JSON and XML
  • Basics of working with data
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Applied Python
Skills Learned:
  • Using Linux
  • Using Vim and Bash
  • Working remotely via ssh
  • Using Git and Github
  • Writing large programs
  • Writing event-driven programs
  • Writing client-server applications
  • Ensuring software quality
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Linear Algebra for Data Science

Linear Algebra
Skills Learned:
  • Vector and matrix calculus
  • Systems of linear equations
  • Linear transformations
  • Eigenvectors and eigenvalues
  • Determinants, linear spaces
  • Inner product, norm, orthogonality
  • Matrix factorizations
  • Least-squares problems
  • Singular value decomposition
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