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Table of contents
1.
Introduction
2.
What is Deep Learning?
2.1.
Deep Learning Categories
3.
What is Data Science?
4.
Data Science vs. Deep Learning
5.
Frequently Asked Questions
5.1.
What is meant by overfitting in machine learning?
5.2.
What is gradient descent?
5.3.
What is Stochastic Gradient Descent (SGD)?
6.
Conclusion
Last Updated: Mar 27, 2024

Deep Learning vs Data Science

Leveraging ChatGPT - GenAI as a Microsoft Data Expert
Speaker
Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM

Introduction

Electronic markets have different stakeholder touchpoints. Like websites, apps, and social media platforms. Apart from common numerical data. They generate a vast amount of versatile data. Particularly unstructured and non-cross-sectional data such as time series, images, and text.

This data can be exploited for analytical model building for better decision support or business automation.

Deep Learning vs Data Science

However, extracting patterns and relationships by hand would exceed. The cognitive capacity of human operators, which is why algorithmic support is indispensable. When dealing with large and high-dimensional data. In this article, we learn about Deep Learning and Data Science and will compare them.

What is Deep Learning?

As the name suggests, this is a whole new way of focusing on how our brain and the human nervous system works. This Deep Learning is closely observing the neural system of a human being. 

This helps it to understand the neural system and communication better. Through this, we can learn how a normal human brain thinks. And we can use it to map a new algorithm for it so that we can solve a problem through a machine just as it has been solved by a human brain.

Deep learning is influenced by the biological process of the nervous system. To think better and solve better in a whole new way. It also focuses on how a brain recognizes. And processes based on an image. 

Deep Learning can also be seen as neural networks with multi-layer architectures. Huge parameters on which it works. Deep Learning is also defined as the working of a neural network.

Deep Learning Categories

  • Unsupervised Pre-trained Network: This pre-training learning technique extracts features. That makes it easy to use in supervised learning to train data.
     
  • Convolutional Neural Network (CNN): CNN is a special architecture of artificial neural networks (ANN). That works with the assistance of the visual cortex.
     
  • Recurrent Neural Network: This network is also a class of ANN that extracts a sequence from a directed graph made by connecting each node to one another. This helps in speech recognition.
     
  • Recursive Neural Network: RNN is a name for a deep neural network made by recursively using or applying weights.
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What is Data Science?

The systematic extraction of knowledge and information from data is supported and guided by a set of fundamental principles known as  Data science. The idea is that data science is most similar to data mining. The actual extraction of knowledge from data via technologies. 

  • That incorporates these principles. There are hundreds of different data-mining algorithms. And a great deal of detail in the methods of the field. Underlying all these many details is a much smaller and more concise set of fundamental principles.
     
  • The principles and techniques are applied broadly across functional areas in business. The broadest business applications are in marketing for tasks. Such as targeted marketing, online advertising, and recommendations for cross-selling
     
  • Data science also is applied to general customer relationship management. To analyze customer behavior. To manage attrition and maximize expected customer value. The finance industry uses data science. For credit scoring, trading, and operations via fraud detection and workforce management
     
  • Major retailers, from Walmart to Amazon, apply data science throughout their businesses. From marketing to supply chain management. But data science involves much more than just data-mining algorithms
     
  • Successful data scientists must be able to view business problems from a data perspective. Data science draws from many "traditional" fields of study. Fundamental principles of causal analysis must be understood
     
  • A large portion of what has traditionally been studied within the field of statistics is essential to data science. Methods and methodology for visualizing data are vital. There are also particular areas where intuition, creativity, common sense, and knowledge of a specific application must be brought to bear
     
  • A data-science perspective provides practitioners. Structure and principles give the data scientist a framework to systematically. Treat problems of extracting valuable knowledge from data.

Data Science vs. Deep Learning

Data Science

Deep Learning 

The systematic extraction of knowledge and information from data is supported and guided by a set of fundamental principles known as data science Deep learning, a subset of machine learning, aims to train. Deep neural networks carry out specific tasks automatically.
Data collection, cleaning, analysis, visualization, and interpretation are all topics focused by data science. Deep learning primarily prepares artificial neural networks. To handle challenging problems like image recognition and natural language processing.
Numerous industries, including business analytics, healthcare, finance, and social sciences, use data science in various ways. Deep learning is used in tasks like speech and image recognition, language translation, and playing strategic games.
Decision trees, regression, clustering, and other statistical and machine-learning methods are used in data science. Convolutional neural networks(CNN) and recurrent neural networks(RNN) are two artificial neural networks used in deep learning.
Data requirements for data science depend on the objectives of the analysis and can be applied to both structured and unstructured data. Deep learning is better suited to tasks with lots of data. Because it frequently needs a lot of labeled data for training.

In data science, models may be easier to understand, enabling users to comprehend how particular judgments or predictions are made.

 

Deep learning models, which are frequently referred to as "black boxes," can be more complicated to interpret.

 

Although data science models may perform well, they might not match the accuracy achieved by deep learning models in certain tasks. Deep learning models have demonstrated exceptional performance in tasks involving large and complex datasets. Frequently outperforming conventional machine learning techniques.
To prepare the data for analysis in data science. A significant amount of work may be required in feature engineering and data preprocessing. Deep learning models don't require as much feature engineering because they can learn feature representations directly from raw data.
More human involvement is frequently required in feature selection, model tuning, and result interpretation in data science. Deep learning models can partially automate feature learning, requiring less manual work.

Frequently Asked Questions

What is meant by overfitting in machine learning?

Data scientists call this overfitting. When a statistical model fits its training data exactly, when this occurs, the algorithm's goal is lost because it cannot accurately perform against unseen data.

What is gradient descent?

Gradient descent means updating the weights iteratively to descend. The slope of the error curve until we get the point with minimum error.  It is widely used in machine and deep learning for training models, specifically in parameterized learning.

What is Stochastic Gradient Descent (SGD)?

SGD is the most used optimization algorithm for machine learning. SGD randomly picks one instance in the training set. For each one-stop and calculates the gradient-based only on that single instance.

Conclusion

In this article, we learn about Deep Learning vs Data Science. We also learn about Deep Learning and also Data Science. We concluded the article by discussing the definition, use cases, and comparison.

To better understand the topic, refer to 

You can also consider our online coding courses such as the Data Science Course to give your career an edge over others.
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