Have you ever wondered why some ace jobs more easily than others, despite poorer grades? That’s because they prepare “smarter” for their jobs and begin their process early. They do whatever it takes to get jobs in fast-growth companies. It includes reading papers, joining communities, attending conferences, and more.
If you want to upskill with a Data Science Bootcamp, you must also design your learning path to set you ahead of your peers. From a first-class portfolio to curating your knowledge for the much-wanted Data Science job role, it calls for more rigor than many other job applications. Other aspects, such as reading Blogs and Research Papers, need equal consideration when preparing for a Data Science job.
Before we list some of the better-known Research Papers, let us quickly run through some reasons why any aspirant must read Data Science Research Papers.
Why Read Data Science Research Papers
Whatever your academic background, reading research papers is a part of your Data Science learning curve. It may seem intimidating for candidates without an engineering background or reading habit, but it should not stop you from checking out the extensive resources online. Blogs are a great place to start before you move on to research papers. Many Data Science websites and journals publish research papers that walk you through various new algorithms and use cases. They keep you on top of data trends and help you access research publications of industry leaders and free content on almost any topic related to Data Science. Reading papers help you be a more effective and creative Data Scientist who can think out of the box. Research publications also help you understand what the authors found to work and what did not. It allows you to adapt your approach and deliver a working solution with minimal resource use.
Some of the academic journals include NeurIPS, JMLR, and ICML.
Before diving into research papers, here is a short primer on how to get into the habit of reading research papers.
- To begin with, read on whatever topic interests you. That gets you into the habit of reading academic content and understanding graphics.
- Now that you have kick-started the preparation process, you must make your selection based on practicality. For a quick understanding of the subject, opt for a literature review. It can save you weeks of dead-ends and unnecessary revisits.
- Recommendations on social media or curated newsletters are also valuable sources of information.
- Now, what to read? Read papers related to your target job role or current work if you are a working professional. It allows you to get hands-on by reinforcing your learning.
- Otherwise, choosing a topic of interest is the ideal way to begin, such as Speech Recognition or Reinforcement Learning.
Tips on Reading Research Papers
Here are some tips for the wannabe Data Scientist:
- Compile a list of journals and topics you would like to read.
- Make a quick scan of papers you think are relevant to your learning goals.
- Generally, you must consider reading or skimming through a minimum of 15 papers for a good understanding of a topic and at least 50 for expertise in the area.
- Keep improvising on the go, even skipping papers or reading sections.
- When you read a paper, begin with the abstract. What is the statement of purpose? What does the research paper intend to achieve? Read in multiple phases, including figures, experiments, and conclusions.
- Skip the math and portions that don’t make sense to you.
- While reading the paper, understand what the author wants to accomplish. What are the main elements of the approach?
- Is there anything that you can use?
- Examine whether you want to follow up on any references.
- Create Google Scholar Alerts for alerts when new publications match your query.
Top Must-read Data Science Research Papers
Enhance your Data Science learning with research papers. Consider looking up job postings to understand various job roles and responsibilities. If you are still confused by the many research papers available, let us explore what to read to decode the various Data Science subjects. Topics covered range from neural networks and machine learning to revisiting problems with statistics or tools.
As Data Science is an extensive domain, we have arranged the papers according to their focus areas.
Here are some of the best research papers cherry-picked just for you!
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
This is a path-breaking paper on machine learning by Jonathan Frankle and Michael Carbin. It includes a pruning approach to uncover sparse sub-networks in larger neural networks, useful when NLP models are getting bigger, and you want smaller, faster, more efficient neural networks with fewer compute resources.
Neural Network pruning techniques reduce the parameter counts of trained networks by over 90%, thus cutting storage requirements and improving computational performance. The algorithm identifies winning tickets by showcasing a series of experiments that support the lottery ticket hypothesis and learn faster than the original network for higher test accuracy.
It is one of the most high-rated CNN papers published by Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick of Facebook AI Research. In this paper, the team presents a simple yet flexible framework for object instance segmentation to efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method is called Mask R-CNN and serves as a baseline for future research in instance-level recognition.
Published by the researchers from Google, BERT (Bidirectional Encoder Representations from Transformers)is a new language representation NLP model that enshrines a more general and theoretical approach and then applies it to specific, well-defined tasks.
BERT is well known for the state-of-the-art results for eleven key NLP tasks and for fine-tuning them to match NLP tasks.
The paper deals with the architecture of Deep Learning models used for YouTube recommendations. The high-level architecture and methods enlisted in the paper are go-to for Recommendations-using-Deep-Learning applications on massive scales, extreme multi-class classification, optimizing training- and test data for predictions, and much more.
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Bottomline
Although Data Science is a specialty turf with a multidisciplinary approach to data, job roles have a broad span of job descriptions, with various roles requiring different skill sets. So fine-tuning your learning to the type of job role is a critical part of your interview preparation. For instance, a Machine Learning Engineer role will require more Machine Learning and AI skillsets, whereas a Data Architect role will require knowledge of database structure, data warehousing, and ETL. In some job roles like Data Scientist and Machine Learning Engineer, it is necessary to keep updating yourself by reading
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So get going and make a habit of reading research papers, as Data Science is a fast-paced research environment that keeps exploring new techniques and algorithms to solve problems.
