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Data Science (online)

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Data Science (online)

6 months duration
8 modules
Updated Apr 23, 2026
Data & Analytics
Data Science (online)
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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

8 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

Master the fundamental tool that every professional developer uses daily. Learn to track changes, collaborate with others, and manage your code like a pro from the very beginning of your development journey.

What you'll learn

  • Understand version control concepts and why Git is essential for modern software development
  • Use GitHub effectively for remote repositories, collaboration, and showcasing your work to potential employers
  • Master Git basics including repositories, commits, branches, and merging for effective code management.
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.

What you'll learn

  • 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.

What you'll learn

  • 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.

What you'll learn

  • 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
NumPy is the foundation of scientific computing and machine learning in Python — every library you'll use later (pandas, scikit-learn, PyTorch, TensorFlow) is built on top of it. In this module, you'll move beyond Python lists into the world of N-dimensional arrays, where calculations on millions of numbers happen in milliseconds. You'll learn how to create, reshape, slice, and operate on arrays; how broadcasting lets you write expressive, vectorized code without loops; and how to apply NumPy's mathematical, statistical, and linear algebra tools to real datasets. By the end, you'll have the numerical fluency that every data scientist, ML engineer, and AI builder relies on daily.

What you'll learn

  • Explain NumPy's role in the scientific Python ecosystem and why vectorized operations outperform native Python loops.
  • Create and inspect N-dimensional arrays using array(), arange(), linspace(), zeros(), ones(), and random generators.
  • Apply indexing, slicing, fancy indexing, and boolean masking to access and modify array elements.
  • Reshape, transpose, stack, split, and concatenate arrays to prepare data for analysis and modeling.
  • Perform element-wise operations and apply broadcasting rules to compute across arrays of different shapes.
  • Use universal functions (ufuncs) and aggregation methods (sum, mean, std, argmax) along specified axes.
  • Solve practical problems using NumPy's linear algebra and random sampling modules, benchmarking vectorized code against pure Python.
  • Conduct statistical analysis on datasets using measures of central tendency (mean, median), dispersion (std, var, percentile), and correlation, applied along specified axes.
Data without visualization is just numbers on a screen — Matplotlib is how you turn those numbers into stories, insights, and decisions. As the foundational plotting library in Python, Matplotlib powers nearly every chart you'll see in data science notebooks, research papers, and production dashboards. In this module, you'll learn to build clear, publication-quality visualizations from the ground up: line charts, bar plots, histograms, scatter plots, and more. You'll master the anatomy of a figure, control every visual element with precision, and learn when to use which chart type to communicate effectively. By the end, you'll be able to take any dataset and produce visualizations that don't just display data — they reveal what matters.

What you'll learn

  • Explain the architecture of a Matplotlib figure (Figure, Axes, Artists) and the difference between the pyplot and object-oriented interfaces.
  • Create core chart types — line, bar, scatter, histogram, pie, and box plots — and choose the right chart for the data and message.
  • Customize plots with titles, axis labels, legends, ticks, gridlines, colors, markers, and annotations to produce clear, professional visuals.
  • Build multi-plot layouts using subplots(), GridSpec, and twin axes to compare datasets side by side or on shared scales.
  • Visualize statistical relationships and distributions, including error bars, confidence bands, and density plots, to support data-driven storytelling.
  • Style plots for different audiences using built-in styles, custom rcParams, and themes, and export figures in publication-ready formats (PNG, PDF, SVG).
  • Integrate Matplotlib with NumPy and pandas to visualize real-world datasets and communicate insights effectively in notebooks and reports.
If NumPy gives you the numerical engine, pandas gives you the cockpit. It's the tool data scientists, analysts, and ML engineers reach for first when working with real-world data — messy CSVs, database exports, API responses, time series, and spreadsheets. In this module, you'll master the two core data structures (Series and DataFrame) and learn how to load, clean, transform, merge, and analyze data at scale. You'll handle missing values, reshape datasets, group and aggregate like a SQL pro, and work with dates and time-indexed data. By the end, you'll be able to take a raw, unstructured dataset and turn it into clean, analysis-ready insights — the single most valuable skill in modern data work.

What you'll learn

  • Explain the pandas data model (Series, DataFrame, Index) and load data from CSV, Excel, JSON, SQL, and APIs into DataFrames.
  • Inspect, select, filter, and modify data using label-based (.loc), position-based (.iloc), and boolean indexing.
  • Clean real-world datasets by handling missing values, duplicates, type conversions, and inconsistent or malformed entries.
  • Transform data using apply, map, vectorized string operations, and conditional logic to engineer new features.
  • Group, aggregate, and pivot data using groupby(), pivot_table(), and crosstab() to summarize and analyze patterns.
  • Combine datasets through merging, joining, and concatenation, and reshape data with melt(), stack(), and unstack().
  • Work with time series data using DatetimeIndex, resampling, rolling windows, and date-based selection for trend and temporal analysis.
This module introduces students to fundamental database design principles and SQL (Structured Query Language). Students will learn how to design efficient relational database schemas, implement entity-relationship diagrams, normalize databases, and write SQL queries to create, retrieve, update, and delete data. The course covers both theoretical concepts and practical applications, with hands-on exercises using industry-standard database management systems. By the end of this module, students will be able to design and implement a functional database solution for real-world applications.

What you'll learn

  • Design normalized relational database schemas that minimize redundancy and maintain data integrity
  • Create entity-relationship diagrams to model real-world data relationships
  • Implement database tables with appropriate data types, constraints, and relationships
  • Write SQL queries to create, retrieve, update, and delete data from databases
  • Perform complex data retrieval using SQL joins, subqueries, and aggregate functions
  • Master advanced query techniques using Common Table Expressions (CTEs) for recursive and hierarchical data
  • Apply window functions for advanced analytics, rankings, and running calculations
  • Develop stored procedures, triggers, and user-defined functions for business logic implementation
  • Apply transaction management concepts to ensure data consistency and integrity