Data Science

Understand the fundamentals of data science, its lifecycle, applications, tools, and relationship with analytics, machine learning, and artificial intelligence. Learners will understand how data scientists solve business and technical problems using data.

Learn Python programming fundamentals required for data manipulation, statistical analysis, automation, visualization, and machine learning. This module focuses specifically on practical python programming for data science.

Learn the core Python libraries used by data scientists to manipulate numerical and tabular data. Learners will work with arrays, DataFrames, filtering, grouping, merging, transformation, and data preparation.

Build the statistical foundation required for data science, machine learning, experimentation, and analytical decision-making. Learners will understand descriptive and inferential statistics through practical examples.

Learn how to prepare raw data for analysis and machine learning. This module focuses on identifying data-quality issues and applying appropriate preprocessing techniques before developing analytical or predictive models.

Learn exploratory data analysis in data science to understand datasets, discover patterns, identify relationships, detect anomalies, and generate meaningful insights before building machine learning models.

Learn how to communicate analytical findings using effective visualizations. Learners will create charts and visual stories using Python libraries and understand how to select the right visualization for different analytical problems.

SQL in data Science, Develop SQL skills for extracting, filtering, aggregating, and analyzing data from relational databases. SQL is integrated into the data science workflow for accessing and preparing data for statistical analysis and machine learning.

Understand the foundations of machine learning and learn how predictive models are developed, trained, evaluated, and improved. This module connects data science for machine learning with practical modeling workflows.

Explore commonly used machine learning algorithms and understand when to apply them. Learners will implement models using Python and Scikit-learn and evaluate their performance using appropriate metrics.

Explore the relationship between artificial intelligence, machine learning, deep learning, and data science. This module introduces neural networks and modern AI concepts relevant to today's data-driven applications.

Apply the complete data science workflow to real-world projects. Learners will develop solutions from data collection and preprocessing through analysis, modeling, evaluation, visualization, and deployment fundamentals.