Covers techniques for collecting data from various sources, importing datasets, and preparing them for analysis. Students will learn how to clean data by handling missing values, removing duplicates, correcting inconsistencies, and transforming data into a suitable format for analysis.

Focuses on understanding datasets through descriptive statistics and visualizations. Students will perform exploratory data analysis using charts and graphs such as histograms, scatter plots, bar charts, box plots, and heatmaps to identify trends, patterns, relationships, and outliers.

Introduces methods for transforming and organizing data to improve analytical outcomes. Students will learn techniques such as filtering, sorting, merging datasets, grouping, feature selection, encoding categorical variables, scaling numerical features, and creating new meaningful attributes.

Provides an overview of machine learning concepts, including supervised and unsupervised learning. Students will build simple predictive models using algorithms such as Linear Regression, Logistic Regression, Decision Trees, and K-Means Clustering, while learning basic model evaluation techniques.

Demonstrates the application of data science in solving real-world problems across domains such as healthcare, finance, retail, and business analytics. Students will complete an end-to-end mini data science project involving data collection, cleaning, visualization, basic modeling, and result interpretation while understanding ethical considerations in data science.

Introduction to the field of Data Science, its importance, lifecycle, and real-world applications. Students will understand the roles of data scientists, data analysts, and data engineers while becoming familiar with Python programming and essential data science libraries such as NumPy, Pandas, Matplotlib, and Seaborn.

Covers techniques for collecting data from various sources, importing datasets, and preparing them for analysis. Students will learn how to clean data by handling missing values, removing duplicates, correcting inconsistencies, and transforming data into a suitable format for analysis.

Focuses on understanding datasets through descriptive statistics and visualizations. Students will perform exploratory data analysis using charts and graphs such as histograms, scatter plots, bar charts, box plots, and heatmaps to identify trends, patterns, relationships, and outliers.

Introduces methods for transforming and organizing data to improve analytical outcomes. Students will learn techniques such as filtering, sorting, merging datasets, grouping, feature selection, encoding categorical variables, scaling numerical features, and creating new meaningful attributes.

Provides an overview of machine learning concepts, including supervised and unsupervised learning. Students will build simple predictive models using algorithms such as Linear Regression, Logistic Regression, Decision Trees, and K-Means Clustering, while learning basic model evaluation techniques.

Demonstrates the application of data science in solving real-world problems across domains such as healthcare, finance, retail, and business analytics. Students will complete an end-to-end mini data science project involving data collection, cleaning, visualization, basic modeling, and result interpretation while understanding ethical considerations in data science.