Data Science Foundations with Python
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Next Start
October 5, 2026
(Offered monthly)Duration
5 Days
ClassroomProgram Fee
$1,250
Exclusive 16% VATOther Dates
November 2, 2026
December 7, 2026
December 7, 2026
Course Description
This comprehensive professional course provides a complete foundation in data science using Python, starting from the very basics. Participants will learn Python programming from scratch, then progress through data manipulation with NumPy and pandas, data visualization with matplotlib and seaborn, statistical analysis, and machine learning. The curriculum emphasizes practical, hands-on skills using Python's powerful scientific ecosystem.
The program begins with Python fundamentals and progressively builds through data wrangling, exploratory data analysis, statistical modeling, and predictive analytics. Participants will work with real-world datasets, build visualizations, and develop the practical skills needed for data science roles - all while learning Python from the ground up.
Prerequisite note: This course does not assume any prior Python knowledge. The curriculum starts with Python fundamentals and builds progressively to advanced data science applications. Basic computer literacy is recommended.
Course Outline
This course is structured into 6 core modules, each carefully designed to build progressively on the previous one.
Learning Outcomes
Technical Skills
- Write Python code from scratch for data science applications.
- Work with Python data structures including lists, tuples, dictionaries, and sets.
- Write functions, use control flow, and handle errors in Python.
- Perform numerical computing with NumPy arrays and vectorized operations.
- Import, clean, and preprocess data from multiple sources using pandas.
- Perform data manipulation, transformation, and aggregation using pandas.
- Create professional data visualizations with matplotlib and seaborn.
- Conduct exploratory data analysis to uncover patterns and insights.
- Apply statistical methods and hypothesis testing using scipy.stats.
- Build and evaluate predictive models using scikit-learn.
- Implement machine learning algorithms including regression, classification, and clustering.
- Evaluate model performance using appropriate metrics and validation techniques.
Career Benefits
- Build a strong, practical foundation in Python programming for data science.
- Learn to solve real-world data problems using Python's scientific ecosystem.
- Master the complete data science workflow from Python basics to model deployment.
- Gain hands-on experience with Python, NumPy, pandas, matplotlib, seaborn, and scikit-learn.
- Develop proficiency in data manipulation, visualization, and statistical analysis.
- Build a portfolio of data science projects demonstrating practical skills.
- Prepare for advanced roles in data science, analytics, and machine learning.
- Establish a solid foundation for specialized study in deep learning and big data.
Who Should Attend
- Beginners — Individuals with no prior Python experience wanting to enter data science.
- Aspiring Data Scientists — People looking to build Python skills for data science roles.
- Career Changers — Professionals transitioning into data science and analytics.
- Data Analysts — Analysts wanting to transition from Excel/SQL to Python for data analysis.
- Researchers & Academics — Individuals applying Python to research data analysis.
- Students — Learners building essential data science skills for their careers.
- Business Professionals — Anyone who wants to use Python for data-driven decision making.
- Data Enthusiasts — Individuals curious about data science and Python programming.
Prerequisites & Requirements
Prerequisites
- Basic computer literacy and familiarity with file management.
- Ability to install software (Anaconda, Python, or Jupyter).
- Basic high school mathematics (algebra, functions) is recommended.
- No prior Python experience is required.
- A willingness to learn programming and work with data.
- Curiosity and enthusiasm for data-driven problem solving.
Requirements
- Laptop with at least 2GB RAM (GB recommended for smoother performance).
- All learning facilities are provided on-site, including high-speed internet, projectors, writing materials, and refreshments.
- Administrative rights on your laptop to install R, RStudio, and necessary packages.
- Sufficient free disk space (at least 2GB) for software installation and project files.
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