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Data Science & AI

Build a comprehensive understanding of Python and essential data processing tools

Duration

8-12 months

Level

Intermediate

What you’ll be able to do

Python and data processing

Build comprehensive understanding of Python and essential data processing tools like NumPy and Pandas.

Statistical inference

Master probability, A/B tests, and causal inference techniques.

Machine learning

Develop proficiency covering models such as KNNs, decision trees, and neural networks.

AI specialization

Specialize in computer vision or LLM engineering with real-world projects.

Data storytelling

Translate data insights into impactful business solutions.

Tools you'll learn

PythonPandasNumPyPysparkSQLMatplotlibSeabornPlotlyTableau/Looker StudioStatsmodelsScipyScikit-LearnXGBoostPyTorchDocker

Curriculum

4 Modules · 13 Sprints

01Data Wrangling with Python4 sprints

In this module, you'll dive into Python programming essentials, getting to know how to manipulate and process data with powerful libraries like NumPy and Pandas. You'll also learn to visualize your findings using tools like Matplotlib, Seaborn, and Plotly. Through hands-on projects such as building a calculator and analyzing real-world datasets, you'll not only grasp Python syntax and key data science libraries but also develop the skills to clean, analyze, and present data effectively.

Programming with Python

  • Python fundamentals, including creating and using functions and variables

  • Introduction to conditional statements and loops in Python for decision-making and repetitive tasks

  • Using Python libraries

  • Techniques for reading from and writing to files in Python

  • Project: Applying programming concepts to develop the Tic Tac Toe game

  • Project: A complex problem inspired by chess for deepening programming skills

Intermediate Programming with Python

  • Object-oriented programming concepts and their implementation in Python

  • The basics of version control: using Git to enhance code management and collaboration

  • Regular expressions for pattern matching and text manipulation

  • Additional Python concepts: libraries, type hinting, and clean code

  • Project: Building a functional calculator to apply object-oriented programming concepts

Data Processing with NumPy and Pandas

  • Handling numerical data with NumPy

  • Exploratory data analysis with Pandas

  • Project: Applying data processing skills to analyze the top tracks on Spotify for 2020

Data Visualization with Python

  • Introduction to data visualization concepts and creating basic charts

  • Advanced data visualization techniques and data cleaning processes

  • Project: Applying visualization skills to analyze data from Coursera courses

02Statistical Inference3 sprints

This module will give you the skills to analyze data using SQL and apply statistical principles with Python, including inferential statistics and statistical modeling focused on linear and logistic regression. You'll work on hands-on projects analyzing real-world data, such as mental health conditions in the tech industry, A/B testing e-commerce website data, comparing wine qualities through causal inference, and completing a capstone statistical analysis project. This comprehensive approach will help you confidently tackle data-driven questions, build models, and make informed decisions in your future roles.

Introduction to SQL and Statistics Fundamentals

  • SQL for data analysis

  • Fundamental statistical concepts relevant to data science

  • Project: Applying SQL and statistical knowledge to analyze mental health trends in the tech sector

Statistical Inference

  • Introduction to inferential statistics

  • Statistical metrics and their applications

  • Statistical testing and p-values in hypothesis testing

  • Presenting statistical findings clearly and understandably

  • Project: Use SQL and apply statistical inference techniques to analyze e-commerce data

Regression

  • Introduction to statistical modeling

  • Linear and logistic regression models for causal inference

  • Project: Apply regression techniques to a wine dataset

03Machine Learning4 sprints

This module covers the fundamentals of machine learning, from supervised and unsupervised learning techniques to advanced topics like gradient-boosted trees and hyperparameter tuning, all applied to real-world scenarios. Using Python and tools like scikit-learn, XGBoost, and LightGBM, you'll complete hands-on projects such as predicting travel insurance outcomes, modeling Portugal housing prices, forecasting stock prices, and applying comprehensive machine learning strategies. You'll gain practical experience in feature engineering, training and evaluating predictive models, tackling time-series, ranking, multi-label, and survival analysis problems, as well as deploying models locally and on the cloud. This practical approach will equip you with the skills to tackle complex machine learning challenges and successfully launch models into production.

Machine Learning Fundamentals and Introduction to Supervised Machine Learning

  • Introduction to machine learning

  • Core machine learning algorithms

  • Feature engineering

  • Hyperparameter tuning

  • Project: Apply supervised learning techniques to predict travel insurance outcomes

Advanced Supervised Learning and Deployment

  • Non-linear Models I

  • Non-linear Models II

  • Local deployment

  • Cloud deployment

  • Project: Train and deploy a machine learning model on the Portugal Housing prices

Other Supervised Problems

  • Time-series forecasting

  • Ranking

  • Survival analysis

  • Multi-label classification

  • Project: Train statistical and machine learning models on the stock prices

Unsupervised and Reinforcement Learning

  • Unsupervised learning fundamentals

  • Advanced unsupervised learning

  • Reinforcement learning fundamentals

  • Advanced reinforcement learning

04Optional Modules2 specializations · 2 sprints

Choose from specialized AI tracks to deepen your expertise in cutting-edge technologies.

Natural Language Processing (AI Specialization)

Master the essentials of NLP through hands-on techniques and real-world applications. Leverage pre-trained models to solve real-world NLP problems effectively.

  • Learn text preprocessing, tokenization, and text representations

  • Gain proficiency in deep learning models like BERT, GPT, and T5 for text analysis

  • Explore sentiment analysis, classification, and named entity recognition

  • Understand key NLP metrics and text similarity measures

  • Discover topic modeling with BERTOPIC and LDA, and summarization

Computer Vision (AI Specialization)

Explore digital image processing, image classification, and detection and segmentation. Throughout the module, you'll complete practical projects, applying your skills to real-world datasets and building sophisticated models for tasks like quality assurance, image labeling, and self-driving car applications.

  • Learn the fundamentals of digital image processing, including pixel manipulation, image transformations, and spatial filtering using libraries like PIL, NumPy, OpenCV, Matplotlib, and scikit-image

  • Master image classification with convolutional neural networks (CNNs)

  • Learn essential techniques and architectures such as AlexNet, VGG, ResNet, and DenseNet, using PyTorch and torchvision

  • Explore advanced detection and segmentation methods, including R-CNN, Fast R-CNN, SSD, YOLO, and Mask R-CNN

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