{"id":15674,"date":"2022-12-19T11:31:35","date_gmt":"2022-12-19T06:01:35","guid":{"rendered":"https:\/\/www.guvi.in\/blog\/?p=15674"},"modified":"2026-01-08T11:59:43","modified_gmt":"2026-01-08T06:29:43","slug":"must-know-neural-networks-for-data-science","status":"publish","type":"post","link":"https:\/\/www.guvi.in\/blog\/must-know-neural-networks-for-data-science\/","title":{"rendered":"Neural Network Skills to Succeed in Data Science"},"content":{"rendered":"\n

If you\u2019re here, then you already understand what a huge grasp data scientists have in the tech world now and how important of a role they play for all kinds of organizations and businesses. <\/p>\n\n\n\n

As per the latest reports, in 2019 the global data science market was valued at $49 billion USD, more than double its worth in 2015. And, from 2022 to 2024, the market is projected to grow at a rate of about 30% each year.  <\/p>\n\n\n\n

Hence this comes as no shock that data scientist salaries in India range from Rs. 4L to Rs.20L\/annum with an average of  Rs. 9.4L\/annum according to Glassdoor.<\/p>\n\n\n\n

In order to completely understand neural networks and how they help you as a data scientist, we must first dive into deep learning.<\/p>\n\n\n\n\n\n

Data Science and Deep Learning<\/h2>\n\n\n\n

Data Science <\/a><\/strong>at its base has always been about incorporating the best-suited tools to get a particular job done. It simply means the extraction of knowledge from some data to answer a specific question. <\/p>\n\n\n

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\"data<\/figure><\/div>\n\n\n

Data science is a powerful asset that allows certain stakeholders and businesses to make informed decisions and create solutions to real-world problems using data.<\/p>\n\n\n\n

It consists of collecting, storing, visualizing, processing, and modeling data so that we can draw meaningful insights and information from it. And data science makes use of several subject matters to make sense of the collected data, such as data analytics and statistics, data mining, machine learning, and deep learning.<\/p>\n\n\n\n

Deep learning stands as one of the most intuitive fields in today\u2019s artificial intelligence research, with a varied range of applications from self-driving cars to natural language processing.<\/p>\n\n\n\n

As a data scientist, you may often have to gather and process vast amounts of data as well as perform certain predictive analytics on the same. And to make tasks like these easy, skills such as deep learning and neural networks become necessary to withhold.<\/p>\n\n\n\n

The supremacy of \u2018Deep learning\u2019 lies in its incredible ability to accurately train big data, making it an essential skill for data scientists.<\/p>\n\n\n\n

Hence, it is necessary for data scientists to understand deep learning techniques such as neural networks and use them, especially when working with massive amounts of data.<\/p>\n\n\n\n

Before moving forward, make sure you understand the basics of Artificial Intelligence & Machine Learning, including algorithms, data analysis, and model training. If you want to learn more, join HCL GUVI\u2019s AI & Machine Learning Courses<\/a><\/strong> with job placement assistance. You\u2019ll discover important tools like TensorFlow, PyTorch, scikit-learn, and others. Plus, you’ll work on real projects to gain practical experience and improve your skills in this fast-growing field.<\/p>\n\n\n\n

What exactly is Deep Learning?<\/h2>\n\n\n\n

Deep learning can be termed as an advancement to the more traditional \u2018machine learning\u2019, wherein artificial intelligence technologies handle big data.<\/p>\n\n\n\n

Artificial neural networks run a set of algorithms that assist machines in learning to perform tasks that would usually require human intelligence to operate.<\/p>\n\n\n

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The human brain is trained to function adequately and smartly evolve to encounter different situations by the repetition of tasks and procedures. <\/p>\n\n\n\n

Deep learning works similarly, wherein a deep learning algorithm is set to perform a particular task repeatedly so that it can learn the problem and improve the result.<\/p>\n\n\n\n

With cascading advancements in the fields of AI, deep learning is taking over machine learning in every sector, and its increase in popularity is indisputable.<\/p>\n\n\n\n

Now that you understand what Deep learning is, let us dive into Neural Networks.<\/p>\n\n\n\n

The Neural Networks you must know as a data scientist<\/h2>\n\n\n\n

Neural networks can be defined as rugged deep learning models that are capable of synthesizing huge amounts of data in merely a few seconds. <\/p>\n\n\n\n

Neural networks (or NNs) have been inspired by biological neural networks. And whilst the principles are the same, the processes and the structures are very different. <\/p>\n\n\n\n

In Neural Networks, the input is made to pass through an activation function along with weighted edges and the generated output may be passed through another activation function. <\/p>\n\n\n\n

Most of these activation functions are stacked up, and each of them is called a layer. Besides the input and output layers, there are multiple layers in the interiors of a neural network that are known as hidden layers.<\/p>\n\n\n\n

Now, there are many different types of neural networks that make a lot of everyday tasks simpler from recommending songs or movies to buying groceries online. Let\u2019s take a look at what will help you as a data scientist.<\/p>\n\n\n\n

Some of the Important Neural Network Skills:<\/h3>\n\n\n\n

1. Perceptron<\/strong><\/p>\n\n\n\n

Perceptron is a very simple neural network structure. This neural network model is also known as a single-layer neural network and consists of only two layers:<\/p>\n\n\n\n