Data Science Part-Time Bootcamp

Curriculum Overview

This bootcamp is a deep dive into the fundamentals of data science and machine learning in Python. You’ll walk away with a portfolio of projects showcasing your data science acumen to prospective employers!

• • Week 1Foundations in Python

    Learn the Python fundamentals needed for data science.

    Technologies:

    • Google Collaboratory
    • Python

  • Week 2Manipulating & Understanding Data

    Learn how to load, clean, and manipulate data using the Python library Pandas. Additionally, you will learn the strengths and weaknesses of using Python to manipulate data.

    Technologies:

    • Numpy
    • Pandas

  • Week 3Foundations of Data Modeling & Intro to Machine Learning

    Build visualizations to not only understand your data, but also how to communicate results to stakeholders.

    Technologies:

    • Folium
    • Matplotlib
    • Pandas
    • Seaborn

  • Week 4Statistical inference

    Learn how to use Python to implement key statistical techniques and understand statistics better by experimenting with Python on real-world datasets. This week concludes with a project to showcase your knowledge.

    Technologies:

    • Matplotlib
    • Pandas
    • SciPy
    • Seaborn
  Week 1Foundations in Python

  Learn the Python fundamentals needed for data science.

  Technologies:

  • Google Collaboratory
  • Python

  Week 2Manipulating & Understanding Data

  Learn how to load, clean, and manipulate data using the Python library Pandas. Additionally, you will learn the strengths and weaknesses of using Python to manipulate data.

  Technologies:

  • Numpy
  • Pandas

  Week 3Foundations of Data Modeling & Intro to Machine Learning

  Build visualizations to not only understand your data, but also how to communicate results to stakeholders.

  Technologies:

  • Folium
  • Matplotlib
  • Pandas
  • Seaborn

  Week 4Statistical inference

  Learn how to use Python to implement key statistical techniques and understand statistics better by experimenting with Python on real-world datasets. This week concludes with a project to showcase your knowledge.

  Technologies:

  • Matplotlib
  • Pandas
  • SciPy
  • Seaborn

• • Week 5Introduction to Machine Learning

    What is machine learning and why should you use the Python Scikit-Learn for Machine Learning. Topics include types of machine learning, how to format your data to be acceptable for an algorithm, and how to train an algorithm.

    Technologies:

    • Matplotlib
    • Pandas
    • Scikit-Learn
    • Seaborn

  • Week 6Decision Trees and Random Forests

    Learn about tree based machine learning algorithms, how to tune them to maximize their performance, and the strengths and weaknesses of each algorithm. Additional topics include feature selection for machine learning, and comparing machine learning algorithms.

    Technologies:

    • Matplotlib
    • Pandas
    • Scikit-Learn
    • Seaborn

  • Week 7Logistic Regression and Regularization

    Learn about the logistic regression algorithm and get a visual understanding of how the algorithm works. Additional topics include: logistic regression for multiclass classification, L1 and L2 regularization, and hyperparameter tuning the algorithms learned so far.

    Technologies:

    • Matplotlib
    • Pandas
    • Scikit-Learn
    • Seaborn

  • Week 8KMeans, Hierarchical Clustering, and DBSCAN

    What is unsupervised learning and what are its applications. You’ll learn about a host of clustering algorithms, how to tune them, and the strengths and weaknesses of each. This week concludes with a machine learning project to showcase your knowledge.

    Technologies:

    • Matplotlib
    • Pandas
    • Scikit-Learn
    • Seaborn
  Week 5Introduction to Machine Learning

  What is machine learning and why should you use the Python Scikit-Learn for Machine Learning. Topics include types of machine learning, how to format your data to be acceptable for an algorithm, and how to train an algorithm.

  Technologies:

  • Matplotlib
  • Pandas
  • Scikit-Learn
  • Seaborn

  Week 6Decision Trees and Random Forests

  Learn about tree based machine learning algorithms, how to tune them to maximize their performance, and the strengths and weaknesses of each algorithm. Additional topics include feature selection for machine learning, and comparing machine learning algorithms.

  Technologies:

  • Matplotlib
  • Pandas
  • Scikit-Learn
  • Seaborn

  Week 7Logistic Regression and Regularization

  Learn about the logistic regression algorithm and get a visual understanding of how the algorithm works. Additional topics include: logistic regression for multiclass classification, L1 and L2 regularization, and hyperparameter tuning the algorithms learned so far.

  Technologies:

  • Matplotlib
  • Pandas
  • Scikit-Learn
  • Seaborn

  Week 8KMeans, Hierarchical Clustering, and DBSCAN

  What is unsupervised learning and what are its applications. You’ll learn about a host of clustering algorithms, how to tune them, and the strengths and weaknesses of each. This week concludes with a machine learning project to showcase your knowledge.

  Technologies:

  • Matplotlib
  • Pandas
  • Scikit-Learn
  • Seaborn

• • Week 9Uses of Dimensionality Reduction

    What is unsupervised learning and what are its applications. You’ll learn about a host of clustering algorithms, how to tune them, and the strengths and weaknesses of each. This week concludes with a machine learning project to showcase your knowledge.

    Technologies:

    • Matplotlib
    • Pandas
    • Scikit-Learn
    • Seaborn

  • Week 10Gradient Boosting Algorithms and Kaggle Competitions

    Learn what gradient boosting algorithms are, why they are so performant, and how to get started with Kaggle competitions.

    Technologies:

    • XGBoost
    • LightGBM

  • Week 11Using SQL with Python

    Working with databases is an essential part of being a data analyst, data scientist, and data engineer.

    Technologies:

    • SQL
    • SQL Alchemy
    • SQLite

  • Week 12Deep Learning Frameworks

    Learn about why deep learning has transformed industries, various deep learning frameworks, and when to use deep learning techniques. Topics include recurrent neural networks (RNN) and Convolutional Neural Networks (CNN).

    Technologies:

    • Keras
    • TensorFlow
  Week 9Uses of Dimensionality Reduction

  What is unsupervised learning and what are its applications. You’ll learn about a host of clustering algorithms, how to tune them, and the strengths and weaknesses of each. This week concludes with a machine learning project to showcase your knowledge.

  Technologies:

  • Matplotlib
  • Pandas
  • Scikit-Learn
  • Seaborn

  Week 10Gradient Boosting Algorithms and Kaggle Competitions

  Learn what gradient boosting algorithms are, why they are so performant, and how to get started with Kaggle competitions.

  Technologies:

  • XGBoost
  • LightGBM

  Week 11Using SQL with Python

  Working with databases is an essential part of being a data analyst, data scientist, and data engineer.

  Technologies:

  • SQL
  • SQL Alchemy
  • SQLite

  Week 12Deep Learning Frameworks

  Learn about why deep learning has transformed industries, various deep learning frameworks, and when to use deep learning techniques. Topics include recurrent neural networks (RNN) and Convolutional Neural Networks (CNN).

  Technologies:

  • Keras
  • TensorFlow

• • Graduation

    You made it! Upon graduation, you have proven yourself as a full stack developer. Not only do you possess a backlog of assignments that test your dev skills, but you have built compelling projects that prove you as a commodity in the industry!

  What You'll Learn

  • Full-Stack Skills

    Your web development foundation is built. Do not stop learning! You will find that while syntax and technologies change, the fundamentals do not. Continue applying yourself to further your web development skills!

  • Learner for Life

    You have learned how to learn. Software is ever-changing, and the value of constant learning cannot be overstated. Not only does this grit and mindset apply to computer science, but to all aspects of your life.

  • Alumni Connections

    Coding dojo graduates come from all backgrounds and perspectives, thus our alumni community is one of the broadest you can find. Over the course of your learning, you will make lifelong connections!