Python Fundamentals:
The Python Fundamentals component of the scryptIQ course offers learners a comprehensive introduction to programming in Python. Some of the key study areas covered in this module are:-
Algorithmic thinking
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Variables, types and operations
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Conditional statements
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Arrays, tuples, lists and indexing
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Iterations: for and while loops
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Dictionaries: associative arrays
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Functions: defining functions, uses and applications
Data Processing:
The scryptIQ Data Processing module introduces and explores Pandas DataFrames and NumPy arrays: two core data structures for data science applications in Python.-
Import, structuring and manipulation of data using NumPy arrays and Pandas DataFrames
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Data characterisation, cleaning and transformation for analysis and machine learning
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Summary statistics and exploratory data analysis
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Univariate and multivariate analyses of complex datasets
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Visualisation of data using Matplotlib, Seaborn and Plotly
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Image handling and processing, including greyscale and colour images
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Image masking, segmentation and augmentation techniques
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Creation of publication-ready figures with precise control
Machine Learning & Artificial Intelligence:
This scryptIQ module provides an in-depth look at classical machine learning methods with an introduction to deep learning:-
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Preparation and optimisation of data for classical machine learning workflows
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Introduction to supervised and unsupervised learning using scikit-learn
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Training, refinement and evaluation of classifier models
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Interpretation of model outputs and predictive performance
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Comparison and selection of different classifier approaches
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Dimensionality reduction as a preliminary step for exploring high-dimensional datasets
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Gaussian Mixture Models (GMMs) K-means for probabilistic clustering of biological and medical data
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An introduction to neural networks and deep learning
- Their implementation with the Python package PyTorch
- How to build a neural network from scratch, layer by layer
