By the end of this course, learners will be able to:

1. Data Understanding and Management

• Identify and describe key concepts, processes, and roles in data science.

• Collect, clean, and preprocess structured and unstructured data from multiple sources.

• Apply techniques for handling missing data, outliers, and inconsistencies.

• Use databases (SQL/NoSQL) to store, retrieve, and manage datasets efficiently.

2. Exploratory Data Analysis (EDA)

• Perform exploratory analysis using statistical summaries and visualizations.

• Interpret trends, patterns, and relationships within datasets.

• Use data visualization tools (e.g., Matplotlib, Seaborn, Tableau, Power BI) to communicate insights effectively.

3. Statistical and Mathematical Foundations

• Apply probability theory, hypothesis testing, and regression analysis in data-driven problem solving.

• Understand and use key mathematical concepts such as linear algebra and calculus in modeling.

4. Machine Learning and Predictive Modeling

• Build, train, and evaluate supervised and unsupervised machine learning models.

• Select appropriate algorithms for classification, regression, clustering, and dimensionality reduction.

• Assess model performance using metrics such as accuracy, precision, recall, F1-score, and ROC curves.

1. Programming Languages and Tools

• Use programming languages such as Python or R for data analysis and model development.

• Employ libraries such as NumPy, pandas, scikit-learn, and TensorFlow/PyTorch for data manipulation and modeling.

• Implement reproducible workflows using Jupyter Notebooks or similar environments.

2. Data Ethics and Communication

• Demonstrate awareness of ethical considerations, data privacy, and bias in data-driven decision-making.

• Communicate analytical results clearly to both technical and non-technical audiences.

• Create data-driven reports and visualizations that support business or research decisions.

3. Applied Data Science and Problem Solving

• Design and implement end-to-end data science projects addressing real-world problems.

• Work effectively in teams to analyze data and deliver actionable insights.

• Apply critical thinking to evaluate the reliability and limitations of data-based conclusions.

A Data Science course is designed for learners who want to understand, analyze, and make data-driven decisions using statistical, computational, and analytical techniques.

1. Students and Academics

• Undergraduate or postgraduate students in Computer Science, Mathematics, Statistics, Engineering, Economics, or related fields.

• Researchers seeking to apply data analysis and machine learning techniques in academic projects.

2. Working Professionals

• IT professionals, software developers, and engineers who want to transition into data science or enhance their analytical skills.

• Business analysts and data analysts aiming to strengthen their statistical modeling and predictive analytics capabilities.

• Managers and decision-makers who want to use data insights to drive strategy and improve operations.

• Domain experts (e.g., healthcare, finance, marketing, logistics) who wish to apply data-driven techniques in their specific fields.

3. Career Switchers

• Professionals from non-technical backgrounds (such as economics, social sciences, or business) who want to move into data-oriented roles.

• Individuals interested in new career opportunities in data analysis, data engineering, machine learning, or AI.

4. Entrepreneurs and Innovators

• Startup founders and product managers who want to use data to understand markets, optimize performance, and improve decision-making.

5. General Enthusiasts

• Anyone with a curiosity about how data shapes decisions, technology, and society, and who wants to build practical skills in data-driven reasoning.

By the end of this course, students will be able to:

1. Write, debug, and execute Python programs for data processing and analysis.

2. Apply Python libraries to clean, transform, and visualize data.

3. Perform exploratory data analysis to uncover trends and patterns.

4. Manage code and collaborate on projects using Git and GitHub.

5. Communicate data-driven findings through clear visual and written reports.

No prior programming experience required. Basic understanding of mathematics and statistics is helpful but not mandatory.

Course Format:

• Delivery: Lectures, coding labs, and project-based assignments

• Tools Used: Python, Jupyter Notebook, pandas, NumPy, Matplotlib, Seaborn, Git, GitHub