Data Structures Algorithm DSA

Student Testimonials:

"The teacher excels in explaining complex concepts clearly." - Liam Bailes
"I have just started but the quality of explanation is superb . I had seen many videos on time complexity but he explained very well."-Deepak Reddy
"So far, I am finding this course really helpful, and the trainer is really sorted about what he needs to teach and is completely prepared with his plan and material. I feel this is one of the best courses available in Udemy and outside to learn DSA because it is well structured and is delivering what we are looking for."-Ankur Saxena
"Great course. Lecturer is full of in depth knowledge and able to pass it on. Its not easy to find this out there. Thank you."-Mark Corrigan
"Because of this course I understand how to find complexity of the program. Teacher has explained concept in very easy manners, so that any body can understand it properly."-Amritesh Kumar Singh
"I really love the way you have explained it, and thanks for such a great course."- Soeng Kanel
"The course is a rare find for in-depth knowledge." - Mark Corrigan
"Well-structured and thorough preparation for DSA." - Ankur Saxena
"Easy to grasp concepts in a single go." - Shaik Asrar
"Effortless concept assimilation." - Elisha Benjamin
"A great foundation in DSA." - Prince Roy Sharma
"Simplifies understanding DSA." - Rahul
"Clarifies program complexity." - Amritesh Kumar Singh
"Clarified Big O notation for me." - Aaron Engelmann
"Excellent for problem-solving and reasoning." - Parth
"Comprehensive overview of Data Structures." - Newton
"Highly recommended for Tier 1 company preparation." - Dennis Paul

About the Course:

Welcome to the Coding Interview Bootcamp with a focus on Python and JavaScript.

The primary goal of this course is to prepare you for coding interviews at top tech companies. By tackling one problem at a time and understanding its solution, you'll accumulate a variety of tools and techniques for conquering any coding interview.

Daily Coding Challenges:

The course is structured around daily coding challenges. Consistent practice will equip you with the skills required for coding interviews and allow you to practice on Leetcode.

Topics Covered:

We start from the basics with Big O analysis, cover common data structures, and discuss real-life problems asked in interviews at tech giants like Google, Meta, Amazon, Netflix, Apple, and Microsoft.

For each question, we will:

Discuss the optimal approach
Explain time and space complexity
Code the solution in JavaScript (you can follow along in your preferred language)

Additional Resources:

The course includes downloadable resources, motivational trackers, and cheat sheets.

Course Outline:

Day 1: Arrays, Big O, Sorted Squared Array, Monotonic Array
Day 2: Arrays, Rotate Array, Container with Most Water
Day 3: Hash Tables, Two Sum, Isomorphic Strings
Day 4: Recursion, Fibonacci, Power Sum
Day 5: Recursion, Permutations, Power Set
Day 6: Strings, Non-Repeating Character, Palindrome
Day 7: Strings, Longest Unique Substring, Group Anagrams
Day 8: Searching, Binary Search, Search in Rotated Sorted Array
Day 9: Searching, Find First and Last Position, Search in 2D Array
Day 10: Sorting, Bubble Sort, Insertion Sort
Day 11: Sorting, Selection Sort, Merge Sort
Day 12: Sorting, Quick Sort, Radix Sort
Day 13: Singly Linked Lists, Construct SLL, Delete Duplicates
Day 14: Singly Linked Lists, Reverse SLL, Cycle Detection
Day 15: Singly Linked Lists, Find Duplicate, Add 2 Numbers
Day 16: Doubly Linked Lists, DLL Remove Insert, DLL Remove All
Day 17: Stacks, Construct Stack, Reverse Polish Notation
Day 18: Queues, Construct Queue, Implement Queue with Stack
Day 19: Binary Trees, Construct BST, Traversal Techniques
Day 20: Binary Trees, Level Order Traversal, Left/Right View
Day 21: Binary Trees, Invert Tree, Diameter of Tree
Day 22: Binary Trees, Convert Sorted Array to BST, Validate BST
Day 23: Heaps, Max Heap, Min Priority Queue
Day 24: Graphs Enroll today.
- Jackson

What's inside

Learning objectives

Common data structures such as arrays, hash table,linked list,binary trees,graphs etc.
Real coding interview questions from google, meta,amazon,netflix ,microsoft etc.

Time and space complexity of algorithms, detailed discussion of logic to solve questions
Code implementation in javascript, python

Common data structures such as arrays, hash table,linked list,binary trees,graphs etc.
Real coding interview questions from google, meta,amazon,netflix ,microsoft etc.
Time and space complexity of algorithms, detailed discussion of logic to solve questions
Code implementation in javascript, python

Syllabus

Day 1: Arrays Data Structures and Algorithms

Welcome! How to make best use of this course

Best Study Technique to prepare for Coding Interviews

Interview Question 1

Clarifying Questions

Test Cases

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Offers daily coding challenges and consistent practice, which will equip learners with the skills required for coding interviews and allow them to practice on Leetcode

Covers real-life problems asked in interviews at tech giants like Google, Meta, Amazon, Netflix, Apple, and Microsoft, which can help learners prepare for specific companies

Starts with the basics of Big O analysis and covers common data structures, which builds a strong foundation for understanding algorithm efficiency and data organization

Provides code implementation in both JavaScript and Python, allowing learners to follow along in their preferred language or expand their proficiency in both languages

Includes downloadable resources, motivational trackers, and cheat sheets, which can help learners stay organized and motivated throughout their interview preparation

Focuses on tackling one problem at a time and understanding its solution, which helps learners accumulate a variety of tools and techniques for conquering any coding interview

Reviews summary

Dsa for interviews with python/js

According to learners who provided testimonials, this course is a rare find that offers in-depth knowledge in Data Structures and Algorithms. Students praise the instructor's ability to explain complex concepts clearly and in easy manners, making it easy to grasp concepts and facilitating effortless concept assimilation. The course is described as well-structured, providing a great foundation in DSA and helping learners understand how to find complexity and clarify Big O notation. It is considered excellent for problem-solving and reasoning and highly recommended for Tier 1 company preparation, effectively delivering what students are looking for to prepare for coding interviews.

Concepts are presented in an accessible way.

"Easy to grasp concepts in a single go."

"Effortless concept assimilation."

"A great foundation in DSA."

"Simplifies understanding DSA."

The course follows a logical and prepared plan.

"the trainer is really sorted about what he needs to teach and is completely prepared with his plan and material."

"I feel this is one of the best courses available... because it is well structured and is delivering what we are looking for."

"Well-structured and thorough preparation for DSA."

Provides deep coverage of core DSA topics.

"Lecturer is full of in depth knowledge and able to pass it on. Its not easy to find this out there."

"The course is a rare find for in-depth knowledge."

"Comprehensive overview of Data Structures."

Instructor excels at making complex topics simple.

"Simplifies understanding DSA."

"The teacher excels in explaining complex concepts clearly."

"I have just started but the quality of explanation is superb."

"Teacher has explained concept in very easy manners, so that any body can understand it properly."

"I really love the way you have explained it, and thanks for such a great course."

Good for coding interviews and problem-solving.

"understand how to find complexity of the program."

"Excellent for problem-solving and reasoning."

"Highly recommended for Tier 1 company preparation."

"Clarifies program complexity."

"Clarified Big O notation for me."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Structures Algorithm DSA | Python+Javascript LEETCODE with these activities:

Review Big O Notation

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Solidify your understanding of Big O notation to better analyze the efficiency of algorithms covered in the course.

Browse courses on Big O Notation

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Read articles and watch videos explaining Big O notation.
Practice determining the time and space complexity of simple code snippets.
Review examples of common Big O complexities like O(1), O(log n), O(n), O(n log n), and O(n^2).

LeetCode Array Problems

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Enhance your problem-solving skills by practicing array-based questions on LeetCode, reinforcing concepts covered in the first few days of the course.

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Solve 3-5 LeetCode easy/medium array problems daily.
Analyze the time and space complexity of your solutions.
Compare your solutions with others and identify areas for improvement.

Cracking the Coding Interview

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Supplement your learning with 'Cracking the Coding Interview' to gain a broader perspective on interview preparation and problem-solving strategies.

View Cracking the Coding Interview on Amazon

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Read relevant chapters covering data structures and algorithms discussed in the course.
Solve practice problems from the book and compare your solutions.
Review the interview strategies and tips provided in the book.

Four other activities

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Show all seven activities

Help others in online forums

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Reinforce your understanding by helping other students in online forums, solidifying your knowledge and identifying areas where you may need further clarification.

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Actively participate in online forums related to the course.
Answer questions from other students and provide helpful explanations.
Seek clarification from instructors or other students when you are unsure of an answer.

Implement a Hash Table

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Deepen your understanding of hash tables by implementing one from scratch in Python or JavaScript, reinforcing the concepts learned in the course.

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Design the hash table with collision resolution strategies (e.g., chaining, open addressing).
Implement the basic operations: insert, delete, search.
Test your implementation with various inputs and edge cases.

Create a Data Structures Cheat Sheet

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Consolidate your knowledge by creating a cheat sheet summarizing the key properties, operations, and complexities of different data structures covered in the course.

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Summarize the key properties of each data structure (e.g., arrays, linked lists, hash tables, trees, graphs).
List the common operations for each data structure (e.g., insert, delete, search, sort).
Include the time and space complexity of each operation.
Organize the cheat sheet for easy reference.

Introduction to Algorithms

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Use 'Introduction to Algorithms' as a reference to deepen your understanding of the theoretical underpinnings of data structures and algorithms.

View Introduction to Algorithms, fourth edition on Amazon

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Consult the book for detailed explanations and proofs of algorithms covered in the course.
Work through the exercises and problems to test your understanding.
Use the book as a reference for more advanced topics and algorithms.

Career center

Learners who complete Data Structures Algorithm DSA | Python+Javascript LEETCODE will develop knowledge and skills that may be useful to these careers:

Software Engineer

A software engineer designs and builds software applications. This role requires a deep understanding of data structures and algorithms, precisely what this course provides. The course's focus on coding interview preparation, including tackling real-world problems from top tech companies, is directly applicable to the daily work of a software engineer. Furthermore, the course covers Big O analysis, which is essential for writing efficient and scalable code, a core concern for any software professional. The Javascript and Python implementation included in this course will be valuable to those who build applications using these languages.

See salaries and explore the career path for Software Engineer

Algorithm Developer

An algorithm developer creates and implements algorithms to solve complex problems and is able to analyze these algorithms. This course directly prepares individuals for such tasks, given its focus on data structures, algorithms, and the analysis of time and space complexity with Big O notation. The course provides a structured approach to understanding and applying algorithms, with daily coding challenges and real coding interview questions, precisely the kind of work an algorithm developer routinely executes. It also emphasizes efficient coding practices with languages like Javascript, and Python which are useful for this kind of role.

See salaries and explore the career path for Algorithm Developer

Backend Developer

A backend developer builds and maintains the server-side logic and databases for applications. The course material provides a direct benefit to an individual who wishes to become a backend developer, as data structure knowledge is essential for designing efficient systems that can handle large amounts of data. The course's emphasis on time and space complexity also comes into play when creating APIs and databases that must operate at scale. The practical coding practice, especially in Javascript, and Python makes this an excellent option for backend developer hopefuls.

See salaries and explore the career path for Backend Developer

Full-Stack Developer

A full stack developer needs a well rounded skill set to manage both the front and backend of web development, and this course will help one build a foundation in algorithms and data structures. Since this course teaches both Javascript and Python, a fullstack developer would greatly benefit from its content. The knowledge of data structures and algorithms covered will help a programmer write performant backend code. Additionally, the course includes practice with coding challenges. This experience provides a solid foundation for approaching a wide array of tasks encountered during fullstack development.

See salaries and explore the career path for Full-Stack Developer

Test Automation Engineer

Test automation engineers are responsible for writing automated tests to verify software quality. This course, which covers data structures and algorithms, may help a test automation engineer write more efficient and robust testing code. The course's focus on problem-solving and coding in JavaScript and Python are crucial components of test automation and may help one create and analyze test data structures and test algorithms. This course's practical coding exercises and understanding of time complexity will prove especially valuable.

See salaries and explore the career path for Test Automation Engineer

Data Scientist

A data scientist analyzes large datasets to extract meaningful insights. Although it is not the sole requirement, this course's coverage of data structures and algorithms can equip a budding data scientist with a core understanding of how to efficiently process data. The course's focus on algorithmic efficiency and complexity is crucial for optimizing data analysis pipelines. While data science is a wide field that requires statistical modeling and machine learning knowledge, a foundation in data structures, algorithms, and related programming practice is often essential before more advanced methods can be applied, which this course helps build.

See salaries and explore the career path for Data Scientist

Game Developer

A game developer creates the software that powers video games. This course, which focuses on data structures and algorithms, can help aspiring game developers. Game development often requires complex algorithmic thinking to manage game logic, physics, and artificial intelligence. The course’s coverage of data structures, like graphs and trees, can be invaluable for creating efficient game structures. The course would be particularly useful for those interested in the programming side of game development, rather than the artistic elements.

See salaries and explore the career path for Game Developer

Mobile Application Developer

A mobile application developer creates apps for mobile devices, where performance is critical. This course, with its emphasis on efficient algorithms and data structures, may be a good fit for mobile application developers. Mobile apps often handle large amounts of data, and knowing how to process it efficiently is an important element of a successful mobile application. The coding challenges practiced throughout the course, as well as the discussion of time and space complexity, are essential for writing code that performs well on phones or tablets. Although the course does not train directly on Android or IOS platforms, the underlying computational concepts are applicable.

See salaries and explore the career path for Mobile Application Developer

Quantitative Analyst

A quantitative analyst, often called a 'quant,' develops and implements mathematical and statistical models for financial analysis. This course can help a quantitative analyst by solidifying their understanding of algorithms and data structures. The course’s focus on algorithmic efficiency may assist a quantitative analyst to optimize their models. Additionally, a solid understanding of data structures is crucial for handling the massive financial datasets that quants frequently work with. While financial modeling requires a separate body of knowledge, the base skills in algorithms and data handling provided will be very useful.

See salaries and explore the career path for Quantitative Analyst

Embedded Systems Engineer

An embedded systems engineer designs software for hardware systems, where resource efficiency is paramount. This course, with its focus on data structures and time complexity, may be a benefit to an embedded systems engineer; this role may require the building of software for constrained hardware, for example, those with limited RAM. This course provides instruction in efficient data structures, which is highly relevant to an embedded systems engineer's daily tasks. Although this course does not train directly on embedded environments or programming languages such as C or C++, it can provide valuable context.

See salaries and explore the career path for Embedded Systems Engineer

Machine Learning Engineer

A machine learning engineer develops and implements machine learning models. To do this effectively, he or she must not only understand machine learning concepts, but also be able to develop algorithms and manipulate numerical data. While this course does not cover core machine learning topics, it provides a crucial foundation in data structures and algorithms. The course content on algorithmic complexity and efficiency may assist a budding machine learning engineer in implementing optimized solutions and preparing data for processing. This course may be most useful as a foundational step to a career in machine learning.

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Frontend Developer

Frontend developers craft the user-facing aspects of applications. While not as algorithmically intensive as backend work, a foundation in data structures and algorithms may still be beneficial. This course may help frontend developers better understand how data flows in an application and how to optimize code for performance. Although not the primary skill used by frontend developers, the course may also be useful to those planning to work on more complex frontend projects or to those who would like to move into fullstack roles. The Javascript instruction provides a direct connection to frontend work.

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Research Scientist

A research scientist conducts research, often at an university or research institution, and this course may be useful to individuals in this role. The course's focus on algorithms and data structures, along with coding implementation in Python and Javascript, are crucial for those who study and implement computational methods in scientific research. For example, those who conduct research in areas such as computer vision, machine learning or bioinformatics may find that this course helps them better perform their research. A Research scientist often needs an advanced degree such as a masters or a doctorate to practice.

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DevOps Engineer

DevOps engineers focus on streamlining the software development and deployment process. While not the primary skill set, a solid foundation in data structures and algorithms, taught in this course, may be useful for building and optimizing infrastructure automation tools. This course's focus on efficient code and algorithmic complexity may also assist DevOps engineers in evaluating the performance of systems in a production environment. A DevOps role will typically require expertise in cloud computing, containerization, and CI/CD pipelines, but this course may be a useful additional tool in the skill set.

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Technical Lead

A technical lead is a leadership position requiring not only core technical skills but also the ability to guide a team to success. This course builds a foundation in data structures and algorithms that can help a technical lead make crucial architectural and engineering decisions. A technical lead usually possesses expertise in a wide array of topics and also participates in technical planning for a team. This course demonstrates how to think about effective design which is relevant for this role. A technical lead will typically have significant prior experience and may find this course useful as a means to add to their well rounded background.

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Python+Javascript LEETCODE

What's inside

Learning objectives

Syllabus

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Reviews summary

Dsa for interviews with python/js

Activities

Career center

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