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The data science industry is booming, and for good reason. Companies are desperate for data scientists who can help them make sense of the vast amounts of data they collect every day, and they’re willing to pay handsomely for those who can do it. If you’re one of the many people who want to get into the field, one question you might have is if you need to learn data structures and if learning it will help your career.
Data scientists need to understand data structure concepts to manage massive data and boost algorithm efficiency. Most data science solutions are expressed in code; therefore, an understanding of data structures will improve a data scientist’s programming abilities and enable them to code better.
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Data structures are complicated, and that makes them uninteresting to learn. However, their importance in data science cannot be overemphasized.
Data structures are important everywhere, even in our everyday life. For instance, it would be a hectic exercise trying to find a pencil in a messed-up room. But if the room is organized, you can immediately identify the pencil placed on the shelf.
Here are some reasons data structures are essential in data science.
Imagine trying to find a book in the library where books are placed in no particular order, like mathematics books kept among fiction storybooks. How much time would it take you to find a science book in such a mess?
To make it easier to spot a book, librarians organize the books in a particular order, like alphabetical or subject-related. Such order saves time, achieving results faster.
It works the same way in data science when handling data. Data structures arrange data in relatable structures for easy analysis. When working with organized data, you can find patterns and inconsistencies easily and therefore understand the problem better.
The demand for data-based solutions in companies is increasing, so is the volume of data collected. These large data volumes require complex algorithms to aggregate them and extract helpful information from them.
If the data is unstructured, the algorithms may not run efficiently, thus taking too much processing time. Using data structures will speed up the processing time for the applied algorithms, and the results will be reliable.
Once a data structure has been created and executed successfully, it can be used in other similar data sets. Data scientists find this useful because they can assemble previously used data structures in libraries and use them in their future for clients.
With increasing data size, a wide selection of data structures to use makes it easy for a data scientist to sort it depending on the suitable data structure and employ practical concepts to manipulate it.
There could be many data scientists working on the same data set on the web server in an organization, each handling different tasks. The server can lag or fail from the multiple user requests.
Data structures organize the data in such a way that every request is responded to promptly, and the servers continue working efficiently.
Different data structures influence how the program executes the instructions given to it. For instance, if you’re working on a data set that requires you to search and extract a particular record from it, the structure you choose for the data will determine how efficient your search will be.
If you choose to organize the data in an array, the search will follow a sequence that might not be efficient compared to another structure like a binary search tree.
As a data scientist, hiring managers expect you to be proficient in data structures and algorithms as it’s necessary for efficient programming. You’ll often be required to apply your knowledge of data structures in programming languages like Python to process data in your career.
This is common knowledge to your interviewers, so expect technical interviews touching on hash tables and other data structures.
Sound data structure skills are useful in problem-solving and programming, especially for data scientists whose primary job is to solve complex statistical problems. With the hype around this field, many people are looking to sharpen their skills in a data structure so as to remain ahead.
Thanks to technology advancement, you can learn data structures online in your preferred programming language and get the necessary certifications. Some of the places to get a quality course in the data structure are:
Coursera offers multiple courses in data structures and algorithms so that you can check through the course outlines to know the knowledge you’ll gain from the course. They include:
- Algorithms Specialization by Stanford University.
- Data Structures and Algorithms Specialization.
- Python Data Structures by University of Michigan
- Free Algorithm Course by Princeton University.
- Accelerated Computer Science Fundamentals Specialization by the University of Illinois.
Some of these courses are free while others require chucking out a few bucks, but they equip you with the fundamentals of data structures and algorithms, enough to thrust your data science career.
Udacity provides you with an opportunity to learn data structures and algorithms through their dedicated course, “Data Structures and Algorithms Nanodegree Certification.”
Udemy has a large selection of courses that you can choose from to learn data structures. They include:
- Data Structures and Algorithms: Deep Dive Using Java.
- Master the Coding Interview: data structures + Algorithms.
- Python for Data Structures, Algorithms, and Interview.
These are only a few places where you can learn data structures, but you can find other lucrative sources online like tutorials, how-to guides, and courses that can be helpful in your learning journey.
How quickly you master data structures and algorithms will depend on how much you already know about programming, your reasons for learning, and your resources.
Here’s a guide to learning data structures for data science.
Fundamental data structures & algorithms are almost similar in most programming languages, so you can identify your preferred language and start there. Learn basic data structures like String, Array, Stack, LinkedList, Queue, Binary trees, etc.
Familiarize with Conditional Logic (if), Control flow statement, the Big O Notation, and Sorting Algorithms. These concepts lay a firm foundation to build on advanced concepts. If you’re already well-versed with these, you can skip step 1.
Introduce yourself to advanced concepts and data structures like:
- Hash Table, Heap, Graph.
- Back Tracking
- Dynamic Programming
- System design
- Bit Manipulation
- Solving coding problems on Leetcode
- Object-oriented programming.
Use the data structures and the algorithms to code and solve hard problems on Leetcode. If your purpose for learning was to prepare for an interview, you can find mock interviews and practice answering the questions.
Practicing will give you an opportunity to identify areas that need polishing, and you’ll be able to gauge your performance.
Technological advancements happen every day, and people write about them. Constant reading will keep you in the know of what is going on in the world around you.
Open-source projects are occasionally organized to involve programmers in the community in solving real-life problems. Contributing to such projects will expose you to real-life projects which can boost your skills.
After accomplishing this milestone, go for the primary goal. Apply for that job and go for the interview. Use your new skills to promote your data science skills and push your career forward. For the courses that offer certification, ensure that you complete and get certified because hiring managers may consider that while hiring you.
Knowledge in data structures simplifies the work for a data scientist as it makes it easy to handle data. It’s not enough to know you can run algorithms on data, but how efficiently you run those algorithms matters too.