Core data science and Machine Learning skills with NumPy, SciPy, Pandas, Matplotlib, Random and Ufunc.
β±οΈ Length: 5.0 total hours
π₯ 16 students
π January 2026 update
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Course Overview
- This intensive 5-hour course offers a fast-paced, comprehensive introduction to core data science and machine learning skills, leveraging Python’s foundational libraries. Designed for 16 students and updated for January 2026, it serves as an ideal launchpad for aspiring data professionals seeking practical competencies.
- Master NumPy for high-performance numerical operations, SciPy for scientific computing, Matplotlib for data visualization, and Pandas for robust data manipulation. Focus on Ufunc (Universal Functions) for optimized computations and the Random module for data generation, crucial for efficient data handling and ML preprocessing.
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Requirements / Prerequisites
- A foundational understanding of Python programming basics is essential: syntax, data types, control flow, and fundamental data structures (lists, dictionaries).
- Basic familiarity with elementary algebra and array operations is advantageous. No prior experience with data science, machine learning, or these specific libraries is assumed.
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Skills Covered / Tools Used
- NumPy & Ufunc Mastery: Create, manipulate, and perform efficient numerical operations on N-dimensional arrays (ndarrays), including advanced indexing, broadcasting, and leveraging Universal Functions for high-performance, vectorized code.
- Pandas for Data Wrangling: Develop expert skills in DataFrames and Series for robust data loading, cleaning, transformation, and analysis; master handling missing data, filtering, grouping, and merging datasets.
- Matplotlib & SciPy Fundamentals: Gain ability to create static data visualizations (scatter plots, line plots, bar charts, histograms) to explore insights, and get introduced to SciPy’s scientific computing capabilities, including basic statistical functions.
- ML Data Preparation & Randomness: Learn to preprocess and format raw data into a suitable structure for machine learning models, covering feature engineering basics, and utilize the Random module for data generation and simulation.
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Benefits / Outcomes
- Solid Foundational Skills: Acquire a robust practical foundation in core data science methodologies, enabling confident execution of data cleaning, analysis, and visualization tasks with industry-standard Python libraries.
- Enhanced Efficiency: Develop the ability to write highly optimized code for numerical operations via NumPyβs vectorized operations and Ufuncs, significantly reducing computation time.
- Preparedness for Advanced ML: This course provides essential data handling and preprocessing skills fundamental to understanding and implementing complex machine learning algorithms.
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PROS
- Rapid Skill Acquisition: Concise 5-hour format delivers critical, actionable skills quickly.
- Industry-Standard Tools: Direct training with essential Python libraries used daily in data science.
- Performance Focus: Emphasizes vectorized operations and Ufuncs for efficient, scalable code.
- Personalized Learning: Small class size (16 students) fosters interaction and individualized attention.
- Direct ML Preparedness: Builds a strong data preprocessing foundation for machine learning studies.
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CONS
- Limited Depth: Due to its accelerated pace and broad scope, the course offers foundational knowledge without extensive deep dives into advanced features or complex theoretical underpinnings.
Learning Tracks: English,Development,Data Science
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