Summer coding bootcamps are here!

Available for beginner to more advanced. Flexible options to fit your families summer plans.

Python Level 3: Algorithms Master

4.8 (281 ratings)
|
Taken by 1,382 students

It’s time to watch algorithms come to life. In this course, learners create data structures and algorithms and use them on large data sets. These skills are the backbone of companies like Google, Amazon, and Facebook, so learning them now could be a secret weapon for their future career.

Private 1:1
Format
Age 14-18
Learners
Weekly
Classes
50 minutes
Per class
Python Level 3: Algorithms Master

About this Course

Python Level 3 introduces students to exciting and practical coding topics like algorithms, APIs, and visualizing data. Your learner will work one on one with a rigorously screened instructor to develop real-world projects like a web-scraper and an IMDB data visualization tool.

Billed as

$250 / month

4 classes / month × $62.50 / class
First class free!
  • First class free!
  • Cancel anytime

1:1 classes are 50 minutes long and can be scheduled anytime Monday-Sunday from 7am to 7pm PT. To begin, select Try a Free Class to signup. Our team will then match you and set up your first class!

  • Course duration
    4-6 months
  • Lesson
    50 minutes
  1. Func-1: I know how to take a substring of a string in Python

  2. Func-2: I can use the split() function correctly in Python

  1. Recursion-1: I know what a recursive function is and its two key components

  2. Recursion-2: I can write a basic recursive function in Python

  3. Recursion-3: I can write more complex recursive functions involving multiple self calls in Python

  4. Recursion-4: I can predict the outcome of a recursive function

  1. Stacks-1: I know what a stack is

  2. Stacks-2: I can create a stack in Python

  3. Stacks-3: I can use the append() function correctly with stacks in Python

  4. Stacks-4: I can use the pop() function correctly with stacks in Python

  5. Stacks-5: I know when it is appropriate to use a stack to solve a problem in code

  1. Time-1: I know what Big-O analysis is

  2. Time-2: I am able to identify the time complexity of a function

  3. Time-3: I understand the difference between the best-case and worst-case scenario of an algorithm

  4. Time-4: I am able to identify the time complexity of an algorithm with loops

  5. Time-5: I am able to identify the time complexity of a recursive function

  1. Linear-1: I understand and can explain the process by which the Linear Search algorithm searches for a piece of data

  2. Linear-2: I can implement a Linear Search algorithm in Python

  3. Linear-3: I know the time complexity of Linear Search

  1. Binary-1: I understand and can explain the process by which the Binary Search algorithm searches for a piece of data

  2. Binary-2: I can implement a Binary Search algorithm in Python

  3. Binary-3: I know the time complexity of Binary Search

  1. Selection-1: I understand and can explain the process by which the Selection Sort algorithm sorts data

  2. Selection-2: I can implement a Selection Sort function in Python

  3. Selection-3: I know the time complexity of Selection Sort

  1. Insertion-1: I understand and can explain the process by which the Insertion Sort algorithm sorts data

  2. Insertion-2: I can implement an Insertion Sort function in Python

  3. Insertion-3: I know the time complexity of Insertion Sort

  1. Bubble-1: I understand and can explain the process by which the Bubble Sort algorithm sorts data

  2. Bubble-2: I can implement a Bubble Sort function in Python

  3. Bubble-3: I know the time complexity of Bubble Sort

  1. Merge-1: I understand and can explain the process by which the Merge Sort algorithm sorts data

  2. Merge-2: I can implement a Merge Sort function in Python

  3. Merge-3: I know the time complexity of Merge Sort

  1. Quick-1: I understand and can explain the process by which the Quicksort algorithm sorts data

  2. Quick-2: I can implement Quicksort in Python

  3. Quick-3: I know the time complexity of the best and worst case scenarios of Quicksort

  1. File-1: I know how to open a file in Python

  2. File-2: I know how to read from a file in Python

  3. File-3: I can use the strip() function correctly in Python

  4. File-4: I can use the read() and readlines() functions correctly in Python, and I understand the difference between them

  1. I use spacing and logical variable names to improve my code's readability

  2. I regularly comment my code to improve my code's readability

  3. I appropriately and frequently test my code

  4. I can independently debug my code

  5. I can independently determine which data structures are best for a project

  1. Joyful Collaboration

    1. I practice listening to my instructor and sharing my ideas to co-create understanding.
    2. I attempt tasks independently and ask my instructor questions when I need help.
  2. Unlimited Curiosity

    1. I take ownership of my learning by asking meaningful questions both when I need clarification and when I want to know more about a topic.
  3. Nimble Determination

    1. I practice resilience when I am frustrated that I have not yet achieved mastery of a new concept or skill; instead of complaining about challenges, I try new approaches and creative solutions.
  4. Invest in Excellence

    1. I arrive to class on time and prepared to learn, with my computer set up with a strong internet connection.
    2. I complete my homework on time, and if I cannot complete my homework due to other obligations, I honestly communicate the reasons for late homework to my instructor.
    3. I do my best to stay present and on task for the whole session. I support my focus by putting away any distracting technology and setting notifications on my device to “do not disturb” mode.

Instructors will assign students roughly 60 minutes of project related homework at the end of each session. Homework is designed to complement the class experience and ensure the student continues to gain practical experience outside of the session.

  1. Completion of Python Level 2 (or evidence of mastery of key concepts from Python Level 2)

  2. Familiarity with exponetial and logarithmic functions is recommended

Who are Juni’s Instructors?

Our instructors are subject matter experts from top US universities. Instructors are highly-vetted and background checked prior to joining and undergo extensive training before ever teaching on our platform.

Upon signing up, parents are asked a series of questions that allow us to match your child with an optimal instructor based on their unique needs and interests. Factors that are considered in our matching process include Learning Style, Personality, Personal Experience, and Academic & Career Aspiration.

Headshot for Puja D
Puja D
I really like bringing a strong energy to my classes, and sharing excitement with my students about learning new topics.

More Courses You Might Like

Real reviews from real parents

4.8 out of 5(281 ratings)
5 stars
4 stars
3 stars
2 stars
1 stars
Justin is the best teacher
Marian T
Jan 6, 2022
Justin is the best teacher. He is knowledgeable and patient and professional.

Start Learning with Juni

Turn your child’s passion into a professional skill