Data science bootcamp online

In this module, we'll focus on data exploration, visualization, and feature engineering—essential steps in preparing data for analysis. We'll learn how to use techniques like summary statistics and visual tools to understand the structure of your data, spot patterns, and identify issues such as missing values or outliers. We'll also cover how to transform and create new features that make your data more useful for modeling. By the end, you'll be able to uncover insights, clean your data, and shape it for better analysis and predictive performance.

What you'll learn

• Explore the idea behind exploratory data analysis using Python
• Understand the importance of feature engineering in building effective machine-learning models
• Apply summary statistics to interpret data
• Analyze the importance of segmentation in data analysis and recognize pitfalls of data analysis
• Understand how domain knowledge helps us to create informative and relevant features

In this module, we'll explore how to turn raw data into compelling visual stories. We'll learn how to choose the right visualizations for different data types, and apply tools like pandas, matplotlib, and seaborn to analyze real datasets. Through hands-on exercises, we'll uncover key insights and develop a deeper understanding of the data. We'll also discuss how to design and present visuals that clearly communicate the message to our audience. By the end of the module, you'll be equipped to present data in a clear, impactful, and audience-friendly way.

What you'll learn

• Understand how to select appropriate visualization types for different types of data
• Analyze data using Pandas, Matplotlib, and Seaborn through hands-on exercises
• Unlock insights and gain a deeper understanding of data
• Discuss how to deliver effective visuals for data storytelling

In this module, we'll explore how to use predictive modelling to create real business impacts. We'll learn to identify the right opportunities for machine learning, translate business goals into actionable models, and recognize when good models might still lead to poor outcomes. We'll also cover essential data requirements and key ethical considerations. By the end, you'll be able to understand predictive systems that are both effective and aligned with business strategy.

What you'll learn

• Learn how to identify the right business opportunities for real analytics impact
• Investigate conditions where a good machine learning model leads to adverse business impact
• Understand various types of data necessary for data collection and analysis
• Identify how to translate business goals into actionable machine learning solutions
• Understand key ethical and practical challenges in building effective predictive systems

In this module, we will explore decision tree learning and focus on how decision trees are constructed for supervised classification tasks. We will learn to apply splitting criteria like Gini index and how to evaluate model performance. Practical exercises and quizzes at the end will help you to apply the learned skills to real datasets and benchmark your performance. By the end of this module, you will be equipped to confidently implement decision tree classifiers.

What you'll learn

• Understand the fundamentals of decision tree learning
• Learn how to choose optimal split points using criteria like Gini index
• Explore key decision tree concepts such as depth, overfitting, and pruning
• Apply decision tree algorithms to real-world datasets through hands-on exercises
• Gain experience with model interpretation and visualizing tree-based decisions

In this module, we'll focus on evaluating classification models and understanding their impact in real-world applications. We'll begin by discussing why accuracy alone isn't a reliable metric and how different types of errors affect business decisions. Through the confusion matrix and key performance metrics like precision, recall, and F1 score, we'll learn to interpret model results with clarity. We'll also explore advanced evaluation tools like ROC curves and AUC to help us assess model performance across thresholds. By the end of this module, you'll be able to choose appropriate metrics, interpret model results effectively, and make evaluation decisions that align with real-world goals.

What you'll learn

• Explain why accuracy alone may be misleading when evaluating classification models
• Interpret the confusion matrix to assess classification performance
• Differentiate between key metrics such as precision, recall, and F1 score
• Evaluate metric trade-offs in different business contexts
• Apply ROC curves and AUC to compare model performance beyond threshold-based metrics

Master the essentials of hyperparameter tuning to improve your model's accuracy and reliability. This module covers key concepts like generalization, overfitting, and the bias-variance tradeoff, alongside practical validation techniques and strategies to handle real-world data challenges. We'll learn how to fine-tune models for better performance and robust predictions.

What you'll learn

• Describe the role of hyperparameter tuning in improving machine learning model performance
• Identify and apply key techniques for effective hyperparameter tuning
• Explain what overfitting is and how it affects model generalization
• Recognize the characteristics of a well-generalized model across different data samples
• Understand the bias-variance tradeoff and how it influences tuning decisions

Unlock the potential of ensemble methods and elevate your predictive power. This module aims to go deep into techniques such as bagging and random forests, explore key concepts like the bias-variance trade-off and out-of-bag evaluation, and build practical skills through interactive notebooks. Whether you're working with messy real-world data or aiming for stronger model performance, ensemble learning is the next step forward!

What you'll learn

• Explore key techniques like bagging and random forests in depth
• Apply concepts such as out-of-bag evaluation and binomial probability
• Understand how ensemble methods balance bias and variance
• Gain hands-on experience through interactive, real-world exercises

Boosting is a powerful ensemble technique that builds better models by learning from previous mistakes, one step at a time. In this course, we'll explore the core ideas behind boosting, explore popular methods like AdaBoost, and apply them through real-world examples and hands-on exercises. Whether you're just starting out or looking to sharpen your skills, this course will help you create smarter and more accurate predictive models.

What you'll learn

• Understand the concept of using weak classifiers to create a strong one
• Analyze boosting and its ability to adjust the weight of each classifier
• Apply the logic behind the boosting algorithm to real-world examples
• Evaluate the differences between bagging and boosting
• Discuss the potential drawbacks of the boosting algorithm

In today's data-driven world, successful digital products are not built on guesswork. They're built on evidence. This module will walk you through the essential principles and practical techniques of online experimentation. From understanding the basics of A/B testing to mastering advanced testing strategies and avoiding common pitfalls, you'll gain the knowledge and confidence to run meaningful experiments that drive better outcomes. Whether you're refining a feature, testing a new idea, or scaling insights across teams, this module sets the foundation for thoughtful, measurable, and impactful decisions.

What you'll learn

• Recognize the role of experimentation in online business and marketing
• Learn how to design and run effective experiments
• Distinguish between A/B, A/A, and multivariate testing and their use cases
• Apply best practices for metrics selection, hypothesis creation, and error avoidance
• Interpret results using statistical significance and confidence intervals

Every tweet, email, review, and support ticket holds valuable insights if we know how to extract them. This module introduces the tools and techniques businesses use to turn raw text into strategic decisions. We will learn how to clean, structure, and analyze text using NLP, TF-IDF, and word embeddings like Word2Vec. Whether you want to understand customer sentiment or automate tasks, this module will help you get started.

What you'll learn

• Analyze unstructured data types such as text
• Evaluate the process of converting text into structured data
• Understand the steps involved in pre-processing text
• Apply the concepts of TF-IDF and Word2Vec
• Use cosine similarity to determine the similarity between documents

Discover how unsupervised learning helps uncover hidden patterns in data without labels. This module introduces key concepts of clustering, with a focus on the k-means algorithm. We'll learn how k-means works, when to use it, and how to choose the right number of clusters for meaningful insights from raw data.

What you'll learn

• Discuss the basics of unsupervised learning and how it differs from supervised learning
• Explain the concept and steps of the K-means clustering algorithm
• Apply K-means to group unlabeled data and uncover patterns
• Evaluate the strengths and limitations of K-means clustering
• Determine the optimal number of clusters using the elbow method

Discover how a simple straight line can unlock powerful insights and help predict the future. In this course, we'll explore how linear models work, how to evaluate their performance, and how optimization techniques like gradient descent help improve accuracy. Through intuitive explanations and practical examples, we'll learn to build, interpret, and apply linear regression models with confidence.

What you'll learn

• How linear regression models predict continuous outcomes using input features
• The difference between parametric and non-parametric approaches
• The role of cost functions in quantifying prediction error
• How gradient descent optimizes model parameters step by step
• How to measure model performance using different metrics

What if our linear models could stay accurate and simple, even in messy real-world data? In this module, we will build models that balance precision and simplicity. We will learn how to control model complexity, use regularization to prevent overfitting, and tune hyperparameters so our models make reliable, real-world predictions.

What you'll learn

• Balancing model complexity to avoid underfitting and overfitting
• Applying L1 (Lasso) and L2 (Ridge) regularization to control complexity
• Tuning key hyperparameters for optimal performance
• Evaluating model fit and use cross-validation to improve generalization
• Interpreting the bias-variance trade-off when tuning linear models

Ever wondered how Netflix knows what we'll love next? This module unpacks the magic behind recommendation systems, from how they measure similarity and generate suggestions to choosing the right approach for your business. Discover how to use data-driven recommendations to boost engagement, delight customers, and drive results.

What you'll learn

• Different type of recommendation systems like collaborative and content-based
• Similarity measures to compare users or items and find the best matches
• Recommendation algorithms used to generate and rank suggestions
• Evaluation techniques to assess performance and effectiveness

Learn the core principles of big data engineering and distributed systems to confidently tackle large-scale data challenges. This module introduces key topics such as cloud infrastructure models, distributed computing frameworks, and scalable system design. We'll use tools like Hadoop and Spark to process data efficiently and explore modern architectures that support real-time analytics and machine learning workflows.

What you'll learn

• Explain the role of data engineering in enabling scalable machine learning workflows
• Describe how distributed systems process and manage large-scale data efficiently
• Compare cloud service models and assess their suitability for different applications
• Identify the core components and workflows of Hadoop, Spark, and MapReduce