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Introduction to Python for Data Science

Learn to use Python effectively for data analysis, machine learning and data engineering

Introduction to Python for Data Science Data science dojo

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Who is this bootcamp for?

The Python for Data Science Bootcamp is designed for learners who want to build a strong foundation in Python and apply it confidently to real-world data tasks—no prior coding experience required.

Aspiring data professionals

Start your journey with the most essential language in data science. Learn how to analyze datasets, write clean code, and automate tasks so you're set up for advanced analytics roles.

Business & tech students

Gain practical programming skills that help you stand out in internships, university projects, and job applications across tech, business, finance, and consulting.

Career switchers

Breaking into data from a non-tech background? This bootcamp gives you the fundamentals—syntax, data handling, and problem-solving with Python—so you can transition smoothly into analytics and data roles.

Instructors and guest speakers

Learn from though leaders at the forefront who build a strong foundation in Python and apply it confidently to real-world data tasks—no prior coding experience required.

Albar Wahab Data Science Dojo

Albar Wahab

Senior Data Scientist | Data Science Dojo
Zaid Ahmed's Picture

Zaid Ahmed

Senior Data Scientist | Data Science Dojo

curriculum

Explore the bootcamp curriculum

Overview of the topics and practical exercises.

Working with data starts with knowing how to access, store, and manage it effectively. In this module, we will explore how to load data from different sources like databases, CSV and Excel files, JSON, and web APIs while learning the best ways to store and organize it. Understanding different file formats helps ensure smooth workflows and compatibility across systems. Whether you’re a data scientist, analyst, or engineer, these skills are essential for working with data efficiently at every stage. 

What You’ll Learn 

  • Identify the distinction between structured and unstructured data 
  • Apply the syntax and behavior of loops in Python 
  • Explain how to read and write data in various file formats 
  • Evaluate the use of various data structures in Python and their suitability for specific tasks 
  • Utilize indexing and slicing for data handling in Python  

Before data can be analyzed, it needs to be clean, structured, and ready for use. This module covers essential data wrangling techniques to handle missing values, remove duplicates, merge datasets, reshape data, and manipulate text. By mastering these skills, we can ensure data quality, streamline analysis, and extract meaningful insights from raw information. Effective data wrangling is a crucial step in any data science workflow, making our datasets reliable and analysis ready. 

What You’ll Learn 

  • Apply techniques to handle missing and duplicate values in a dataset 
  • Evaluate best practices for data processing, transformation, and cleaning 
  • Integrate data from multiple sources and perform necessary transformations 
  • Implement string manipulation methods to clean and modify text data.

In this module, we’ll cover key techniques for data exploration and visualization using Python libraries like pandas, NumPy, Matplotlib, and seaborn. We’ll learn how to explore datasets, create visualizations, and uncover insights that help in understanding patterns and relationships within the data. By the end, you’ll be able to effectively use visual tools to communicate your findings. 

What You’ll Learn 

  • Understand why data exploration and visualization are essential in data analysis 
  • Use techniques like summary statistics, outlier detection, and correlation analysis 
  • Interpret and choose appropriate plots using Matplotlib and Seaborn 
  • Apply exploration and visualization skills to real-world datasets

In this module, we will explore how to build efficient data pipelines for integrating, transforming, and managing data from various sources. We will cover the fundamentals of RESTful APIs, HTTP protocols, and data extraction techniques. We will learn how to retrieve data from web services, understand the ETL (Extract, Transform, Load) process, and apply web scraping methods. These skills will help us automate data workflows and streamline data processing for analysis and decision-making. 

What You’ll Learn 

  • Understand the components and processes involved in data pipelines 
  • Apply techniques to extract data from websites using Python and web scraping methods 
  • Discuss the principles and architecture behind RESTful services and APIs 
  • Use the requests library to send HTTP requests and interact with RESTful APIs

In this module, we will cover the essentials of machine learning using Python. We will explore key concepts like supervised and unsupervised learning, model selection, and training, along with popular libraries like scikit-learn. We’ll work through the process of preparing data, selecting the right algorithms, evaluating model performance, and applying machine learning techniques to real-world problems. By the end of this module, you’ll have a strong foundation in applying machine learning methods and be equipped to take on machine learning projects with confidence. 

What You’ll Learn 

  • Explain core OOP concepts and how they help structure ML code 
  • Describe the machine learning process and key terminology 
  • Understand how scikit-learn’s estimator API works 
  • Choose models and tune hyperparameters using scikit-learn 
  • Use supervised learning to predict customer churn with real-world data

Earn a verified certificate

Earn a verified certificate from The University of New Mexico Continuing Education:

  • 2 Continuing Education Credit (CEU)
  • Acceptable by employers for reimbursements
  • Valid for professional licensing renewal
  • Verifiable by The University of New Mexico Registrar’s office
  • Add to LinkedIn and share with your network 
Python for Data Science Certificate | Data Science Dojo

We accept tuition benefits

All of our programs are backed by a certificate from The University of New Mexico, Continuing Education. This means that you may be eligible to attend the bootcamp for FREE.

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Introduction to Python for Data Science

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Frequently asked questions, answered.

How long do I have access to the program content?

Access to the program content depends on the plan you choose at the time of registration. Learn more about different plans here.

Introduction to Python for Data Science program is 5 days, 3 hours per day, for a total of 15 hours of training. There is additional practice if you would like to keep refining your python skills after the program ends.

There are no prerequisites for this program, however our pre-course prep work will include tutorials on fundamental concepts of data science and Python programming to help you prepare for the training program.

Classes are live and instructor-led. Office hours will be available multiple times a week in case students need assistance. The program is not self-paced but we do assign some homework and practical exercises to further facilitate your learning. Lectures will also be recorded to give students the option to go back and review.

The cost will depend on the plan purchased by the students and the discounts available at the time. Please contact us at [email protected] for updated information on discount availability and payment plans.

Yes, our live instructor-led sessions are interactive. During these sessions, students are encouraged to ask questions, and our instructors respond without rushing. Additionally, discussions within the scope of the topic being taught are actively encouraged. We understand that questions may arise during homework, and to assist with that, we offer office hours to help unblock students between sessions. Rest assured, you won’t have to figure everything out by yourself – we are committed to providing the support you need for a successful learning experience.

We’re offering three different plans for this program.
Dojo. With the Dojo plan, you will get 15 hours of live training, pre-training material, program content, and restricted access to Jupyter notebooks.
Guru. With the Guru plan, you will get everything in the Dojo plan including bonus Jupyter notebooks during the program, access to the learning platform during the program, access to collaboration forum, recorded live sessions, and a verified data science certificate from the University of New Mexico worth 2 Continuing Education Credits.
Sensei. With the Sensei plan, you will get everything included in the Guru plan with the additions of one year of access to the learning platform, Jupyter notebooks, collaboration forums, recorded sessions and office hours, and live support during the program. 

Yes, we are offering an early-bird discount on the Training Plan

To register for the program, simply view our packages and register for the upcoming cohort. The payment can be made online on our website, via invoice, or wire transfer.

Once you are registered for the program, you will receive a few emails from us. One of those emails will contain steps to create your learning portal account and access the program content. Please follow the steps in the email to create your account. If you’re facing any difficulty please email us at [email protected] for assistance.

Transfers are allowed once with no penalty. Transfers requested more than once will incur a $200 processing fee.

If for any reason, you decide to cancel, we will gladly refund your registration fee in full if notified the Monday prior to the start of the training. We would also be happy to transfer your registration to another bootcamp or workshop. Refunds cannot be processed if you have transferred to a different bootcamp after registration.

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