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Course Requirements

Prerequisites:

Basic knowledge of programming, preferably in Python or R.

Familiarity with basic statistics (mean, median, standard deviation) and linear algebra (matrix operations).

Analytical mindset and problem-solving skills.

Software and Tools:

Access to a computer with internet.

Python (or R) installed, with IDEs like Jupyter Notebook, VS Code, or RStudio.

Essential Python libraries for data science (NumPy, Pandas, Matplotlib, Scikit-Learn).

Access to SQL database systems and cloud platforms (optional but recommended for projects).

Course Description

This Data Science Complete Course provides a comprehensive foundation in data science principles and techniques. From data collection to data-driven decision-making, this course covers core concepts, methods, and tools essential for tackling real-world data science projects. It combines theoretical concepts with practical, hands-on projects to help students build a robust data science skill set, empowering them to analyze complex datasets, visualize data insights, and apply machine learning algorithms effectively.

Modules covered:

Introduction to Data Science – Overview, roles, and skills in data science.

Data Collection and Preprocessing – Data types, data cleaning, handling missing values, and data transformation.

Exploratory Data Analysis (EDA) – Visualizations, patterns, and insights using Python and tools like Matplotlib and Seaborn.

Statistical Analysis – Probability, statistical tests, hypothesis testing, and regression analysis.

Machine Learning Algorithms – Supervised and unsupervised learning techniques, including linear regression, decision trees, and clustering.

Model Evaluation and Optimization – Performance metrics, cross-validation, hyperparameter tuning.

Deep Learning (optional) – Introduction to neural networks and deep learning frameworks.

Projects and Case Studies – Real-world projects on predictive modeling, recommendation systems, and data storytelling.

Course Outcomes

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

Understand Core Concepts – Grasp fundamental principles of data science, including data analysis, data manipulation, and machine learning.

Perform Data Preprocessing – Clean, transform, and organize raw data into a usable format.

Conduct Exploratory Data Analysis (EDA) – Use data visualization to uncover trends, patterns, and insights within datasets.

Apply Statistical Methods – Conduct statistical analysis and hypothesis testing to support data-driven conclusions.

Build Machine Learning Models – Develop, train, and evaluate various machine learning models for predictive analysis.

Communicate Insights – Effectively communicate data-driven findings using visualization and storytelling techniques.

Work on Real-World Projects – Implement data science concepts on industry-based datasets, building portfolio-worthy projects that showcase data handling and analytical skills.

Prepare for Data Science Careers – Gain the knowledge and skills required to pursue entry-level positions as a data scientist, data analyst, or machine learning engineer.