Data Science Bootcamp- Bridging the Skills Gap
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This bootcamp addresses the growing demand for data scientists by bridging the gap between academic knowledge and industry-required skills. Data science combines tools from computer science, statistics, engineering, and domain expertise to extract insights from structured/unstructured data. Topics include:
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Python for Data Science
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Machine Learning (ML).
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Big data tools and ethical considerations
Focus: Job-specific training to equip learners with practical skills for the workplace.
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6
1. NumPy For Python Statistical AnalysisThis lesson introduces NumPy, Python's fundamental library for numerical computing, focusing on statistical analysis. You'll learn to:
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Create and manipulate multidimensional arrays for efficient data storage
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Perform vectorized operations (fast math without loops)
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Calculate key statistical measures (mean, median, standard deviation)
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Generate random distributions (normal, uniform) for simulations
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Apply linear algebra (dot products, matrix multiplication)
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Visualize data distributions with histograms
Perfect for data analysis, scientific computing, and machine learning prep. Includes hands-on exercises with real-world datasets.
Prerequisites: Basic Python (lists, loops).
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7
NumPy Industry Standard Assignment
Technical Skills:
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Programming: Python.
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Statistics: Probability theory, advanced methods
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Data Management: Cleaning, visualization, scraping
Concepts Covered:
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Data collection → analysis → visualization pipeline
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Machine learning fundamentals
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Ethical data handling
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Beginners from STEM and non-STEM backgrounds (e.g., humanities, social sciences, business).
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Professionals seeking foundational data literacy.