Get Ready For a Modern Career

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.

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

Introduction 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, Vim and Bash
  • Working remotely via ssh
  • Using Git and Github
  • Writing event-driven programs
  • Writing client-server applications
  • Working with Docker images
  • Creating and using Jupyter notebooks
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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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