Data Science

6 months duration

3 modules

Updated Jun 13, 2025

Data & Analytics

Course Overview

Get to know what this course is all about and what you'll learn

Course Description

Master the complete data science workflow from data collection through web-based model deployment with this comprehensive professional program. Learn to build predictive models using Python, NumPy, Pandas, and Scikit-learn, create compelling visualizations with Matplotlib and Seaborn, and deploy machine learning solutions through web applications using HTML, CSS, JavaScript, and FastAPI. This hands-on course combines statistical analysis, machine learning, and web development skills to prepare you for modern data science roles.

Through real-world projects spanning finance, healthcare, and technology sectors, you'll develop expertise in statistical modeling, machine learning algorithms, and full-stack deployment while building a portfolio that demonstrates your ability to create end-to-end data science solutions. By completion, you'll confidently build, visualize, and deploy machine learning models through professional web interfaces that stakeholders can actually use.

What You'll Learn

This comprehensive program develops your expertise across the complete data science pipeline through integrated technical modules. You'll begin with Python programming fundamentals and statistical mathematics, building the analytical foundation necessary for advanced data science work. Database skills follow with SQL mastery for extracting and manipulating large datasets from various sources.

Core data science tools form the program's technical backbone as you master NumPy for numerical computing, Pandas for data manipulation and analysis, and advanced data visualization techniques using Matplotlib and Seaborn to create publication-quality charts and interactive dashboards. Machine learning implementation becomes practical through Scikit-learn, where you'll build classification, regression, and clustering models with real datasets.

The program extends beyond traditional data science into practical deployment through web development fundamentals including HTML, CSS, and JavaScript for creating user interfaces, followed by FastAPI for building robust APIs that serve your machine learning models. Model deployment techniques ensure your solutions reach end users through scalable web applications. An introduction to TensorFlow provides exposure to deep learning frameworks for advanced modeling scenarios.

This comprehensive approach ensures you understand not just how to build models, but how to deploy them as usable products that deliver business value. Each module integrates theoretical concepts with hands-on implementation, culminating in capstone projects where you build complete data science applications from data collection through web-based deployment.

This program serves software developers entering data science, analysts expanding into machine learning, entrepreneurs building data products, and professionals seeking to combine analytical and technical skills. Prerequisites include basic programming experience and high school level mathematics.

Upon completion, graduates are prepared for roles including data scientist, machine learning engineer, full-stack data scientist, and analytics developer positions. The 6 months program combines interactive lectures, hands-on coding sessions, and project work, with flexible pacing options and comprehensive support. Students earn a professional certificate demonstrating proficiency in the complete modern data science toolkit from analysis to deployment.

Course Curriculum

3 modules • Learn at your own pace • Hands-on experience

Data Science Curriculum

Prerequisites

Be ready to learn

Learning Objectives

Write efficient Python code for data manipulation, analysis, and machine learning applications
Apply statistical and mathematical concepts to analyze datasets and validate model performance
Extract and manipulate data from databases using complex SQL queries and joins
Perform numerical computations and array operations using NumPy for scientific computing
Create professional data visualizations and interactive dashboards using Matplotlib and Seaborn
Clean, transform, and analyze large datasets efficiently using Pandas DataFrames
Build and evaluate machine learning models for classification, regression, and clustering using Scikit-learn
Develop responsive web interfaces using HTML, CSS, and JavaScript for data science applications
Create robust APIs and web services using FastAPI to serve machine learning models
Deploy machine learning models as web applications that end users can interact with

Course Modules

Python serves as the foundation of modern data science, providing essential programming skills for data manipulation, analysis, and machine learning. This module develops your Python proficiency from basics through data science applications.

You'll master Python fundamentals including data types, control structures, functions, and essential libraries. Hands-on exercises with real datasets teach you to write efficient code for data processing tasks and establish the foundation for advanced data science work.

Module Objectives

Write efficient Python code using data types, control structures, and functions for data science applications
Import and utilize essential Python libraries and packages for data manipulation and analysis
Handle file input/output operations and work with different data formats (CSV, JSON, etc.)
Debug Python code effectively and implement error handling techniques
Apply object-oriented programming concepts to organize and structure data science projects
Write clean, readable code following Python best practices and coding standards
Process and manipulate datasets using core Python programming techniques

Mathematics provides the theoretical foundation for understanding and implementing data science algorithms effectively. This module builds essential skills in linear algebra, discrete mathematics, coordinate geometry, and functions that underpin machine learning and analytical techniques.

You'll master linear algebra concepts including vectors and matrices that form the backbone of data representation and algorithmic computations. Discrete mathematics develops logical thinking and combinatorial methods, while coordinate geometry builds spatial reasoning for data visualization. Relations and functions establish the framework for understanding data transformations and model relationships that drive machine learning algorithms.

Module Objectives

Perform vector and matrix operations including addition, multiplication, and transformations
Apply linear algebra concepts to represent and manipulate datasets in mathematical form
Use set theory, logic, and combinatorial methods to solve discrete mathematical problems
Interpret and create geometric representations of data using coordinate geometry principles
Define and analyze mathematical functions and their relationships to model data transformations
Apply distance metrics and geometric concepts for data analysis and algorithm implementation

Statistics forms the analytical backbone of data science, providing methods to extract meaningful insights from data and validate findings. This module develops your statistical literacy from fundamental concepts through advanced techniques essential for machine learning and predictive modeling.

You'll master descriptive statistics to summarize datasets, probability theory to understand uncertainty, and inferential statistics to draw conclusions from sample data. Correlation and regression analysis reveal variable relationships and enable predictive modeling, while hypothesis testing provides systematic validation of assumptions and results.

Module Objectives

Calculate and interpret descriptive statistics including measures of central tendency and variability
Apply probability theory concepts and work with common probability distributions
Conduct hypothesis testing and interpret p-values, confidence intervals, and statistical significance
Perform correlation analysis and build linear regression models to identify variable relationships
Select and apply appropriate statistical tests based on data types and research questions
Evaluate model performance using statistical metrics and cross-validation techniques
Distinguish between statistical significance and practical significance in data analysis results