By skyforbes Data science is a dynamic and rapidly evolving field that relies heavily on the effective manipulation and analysis of data. To excel in this domain, it’s crucial to have a solid understanding of data structures and algorithms(DSA). These foundational concepts form the backbone of efficient data processing and analysis, enabling data scientists to derive meaningful insights from large datasets. In this blog post, we’ll look at a comprehensive guide to learning data structures and algorithms designed exclusively for data science.
What is a Data Structure ?
A data structure is a storage system designed for storing and arranging data, making it easier for users to obtain and manipulate the data they need. These structures provide a framework for organizing information, which improves interpretation for both machines and humans.Imagine having colorful building blocks and sorting them by color to easily find the ones you want. This sorting is similar to using a data structure, making things neat and easy to find.
Why is Data Structure important for Data Science ?
The data science field is growing because companies need help understanding the big amounts of data they collect. If you’re interested in data science, learning about data structures and algorithms is important. It helps manage data better and makes coding easier for data scientists. Knowing about data structures is useful for both coding and getting hired in data science jobs.
What is an Algorithm?
An algorithm is a set of instructions that a computer follows to turn input into a specific output through a series of steps. Algorithms are used in industries such as marketing, and healthcare to analyze, process, and extract insights from large amounts of data.
A Step by Step Guide to learn Data Structure & Algorithm for Data Science
Step 1: Learn a Programming Language of Your Choice
To begin, choose a language like R or Python. Before learning coding, understand the language’s basics: syntax, data types, variables, operators, conditional statements, loops, functions, etc. You can also explore Object-Oriented Programming (OOP) concepts.
Learning resource
- Google’s Python Class | Python Education
- Python for Everybody Specialization| Coursera
- Corey Schafer – YouTube
Step 2: Learn about Algorithmic Complexities
The initial important concept is algorithmic complexity. It’s a technique that helps you know how your code performs as the data size increases. Data scientists find this concept crucial because they often deal with processing ever-growing volumes of information generated daily. There are two types of complexities let’s briefly explore each:
1. Time Complexity:
Time complexity is not a primary focus of a data scientist’s work, however one needs to understand that it measures how long an algorithm takes to run based on the size of the datasets.
Importance in Data Science:
- Data scientists often work with large datasets and complex algorithms. Understanding the time complexity of algorithms is crucial for selecting efficient algorithms that can handle data processing tasks within acceptable time frames.
- Time complexity helps in assessing the scalability of algorithms. A data scientist needs to choose algorithms that can scale effectively as the size of the dataset grows.
2. Space Complexity:
Space complexity is a measure of the amount of memory or storage space an algorithm requires as a function of the size of the input data.
Importance in Data Science:
- Memory usage is a critical consideration in data science, especially when dealing with large datasets. Efficient use of memory ensures that algorithms can handle substantial amounts of data without causing memory-related issues.
- Data scientists often deploy models and algorithms in various environments, including resource-constrained systems. Understanding and managing space complexity are essential for deploying solutions in such environments.
- In the context of machine learning, models may have memory requirements, and features like feature engineering or dimensionality reduction techniques can affect space complexity.
Both time and space complexities are essential considerations for data scientists when designing, selecting, and optimizing algorithms for various tasks in data processing, analysis, and machine learning.
Learning resource
1. Complete Guide On Complexity Analysis – Data Structure and Algorithms Tutorial – GeeksforGeeks
2. Time and Space Complexity COMPLETE Tutorial – What is Big O?
Step 3: Learn Basic Data Structures
This is the step where you actually start learning DSA. Firstly get started with learning the fundamental data structures. The most important basic data structures include:
Next , learn how to implement and manipulate these structures in your chosen programming language.
Learning resource
1.Data Structures & Algorithms in Python by geeksforgeeks
2.Python data structures by Coursera
3.Programming, Data Structures And Algorithms Using Python – NPTEL Course
4. Data Structures in Python – Full Course for Beginners
Step 4: Basic Algorithms
After getting familiar with the basics of data structures, move ahead with learning the basic algorithms such as:
- Search algorithms: Help find a specific item in things like arrays, strings, linked lists, or other data structures. Some common searching algorithms are : Linear , Binary and Ternary search.
- Sorting algorithms: It is like a tool that helps rearrange a group of things, like an array or a list. Learn bubble sort, merge sort , quick sort , insertion and selection sort.
- Divide & Conquer algorithms: It divides a problem into smaller parts, solves each part, and then combines the solutions of those smaller tasks to solve the overall problem.
Learning resource
- MIT OpenCourseWare
- Algorithms and Data Structures by FreeCodeCamp
- Algorithms Tutorial – GeeksforGeeks
Step 5: Python Libraries for Data Science
Explore vital Python libraries for data science, such as NumPy and Pandas. Understand how these libraries leverage effective data structures and algorithms to handle tasks like manipulating and analyzing data.
Learning resource
Step 6: Master the Advanced Concepts
Once you are comfortable with the basics it’s time to learn more complex data structures and algorithms.
1. Advanced data structures
Get acquainted with the following advanced data structures:
- Binary indexed tree: It’s a data structure that can quickly update values and calculate prefix sums in an array of values.
- Disjoint-set: Monitors a set of elements divided into various separate (non-overlapping) subsets.
- Segment tree: It keeps details about array intervals in the form of a tree.
- Trie: Used to store a large amount of strings.
2. Advanced algorithm
Some important advanced algorithms include:
- Greedy algorithms: Find solutions to problems in the quickest time possible.
- Recursion: Recursion is when a function calls itself, either directly or indirectly. The function doing this is referred to as a recursive function.
- Backtracking algorithm: Approach for solving problems step by step, attempting to construct a solution incrementally.
Learning Resources
- Advanced Data Structures and Algorithms in Python ( DSA ) | Udemy
- Learn Advanced Algorithms and Data Structures with Python | Codecademy
Step 7 : Practice more and more
Now that you know about data structures and algorithms, practice a lot of DSA problems. You can also join coding challenges and competitions to move a step ahead in your learning journey. The following platforms can be helpful:
For Problem Practice
For Coding Challenges
For Building your own Projects
For Interview Preparation
- LeetCode’s Interview Crash Course: Data Structures and Algorithms
- Interview Questions & Answers | Glassdoor
- Interviewing.io
Conclusion
Mastering data structures and algorithms is a continuous process, and it plays an important role in a data scientist’s success. By building a strong foundation, learning to apply these concepts to real-world problems, and staying engaged with the data science community, you can improve your skills and contribute effectively to the growing field of data science. Keep coding, practicing, and exploring new challenges to continuously refine your expertise in data structures and algorithms.