Data Science

Overview of Data Science and Its Importance: An overview of data science and its significance, highlighting its role in extracting insights and driving decision-making through data analysis.

Data Types and Sources: Learn in-depth exploration of various data types and the primary sources from which data can be obtained for data science applications.

Data Collection Methods: Learn various data collection methods essential for data science, including surveys, web scraping, and sensor data acquisition.

Handling Missing Values and Outliers: Understand techniques for identifying, managing, and imputing missing data and outliers to ensure accurate and reliable analysis.

Data Transformation and Normalization: Understand data transformation and normalization techniques essential for data science.

Data Quality Assessment: Evaluate and ensure the accuracy, consistency, and reliability of data.

Introduction to Data Visualization Tools: Understand essential data visualization tools for Data Science, covering techniques and software for effectively presenting and interpreting data.

Creating Charts, Graphs, and Plots: Create charts, graphs, and plots essential for data visualization.

Best Practices for Effective Visualization: Create effective visualizations in Data Science, emphasizing clarity, accuracy, and effective communication of insights.

Supervised and Unsupervised Learning: Understand supervised and unsupervised learning techniques essential for Data Science, including algorithms, model evaluation, and practical applications.

Regression, Classification, and Clustering: Understand fundamental techniques of regression, classification, and clustering, essential for data science applications.

Intro to Neural Networks and Deep Learning: Get familiar with neural networks and deep learning.

Working with Real-World Datasets: Acquire skills to handle and analyze real-world datasets, equipping students with essential data science skills.

Applying Data Science Concepts to Solve Problems: Apply data science concepts to solve real-world problems effectively.

Presenting Findings and Insights: Apply findings and insights through clear, concise, and impactful presentations.

Interactive Sessions: Engage in interactive sessions with instructors to facilitate a dynamic learning experience.

Practical Labs: Access practical labs for hands-on learning and skill application.

Assessments: Participate in assessments to track progress and reinforce understanding.