What are Neural Networks & How Neural Networks Skills can Boost Your Data Science Career

The neural network is a branch of AI that uses a series of interconnected algorithms to form a structure like that of the human brain. But how do neural networks work? The neural network helps AI algorithms mimic the way the human brain operates. Neural networks resemble artificial neurons that represent close to human brain neurons. These ANNs help to identify the relationship between different data feeds to the algorithm.

We can use it in various use cases like classification, regression, image recognition, pattern analysis, prediction, etc. According to the Markets and Markets research report, the global ANN market cap was 117 million USD in 2019. As per the report, it will reach 296 million USD by 2024, with a CAGR of 20.5% from 2020 to 2024.

Neural network algorithms adjust as per the input changes. Hence, depending on the data change, the network can generate the best possible result without altering the design of the network or the output criteria. If you are looking for a data science role that deals with complex neural network algorithm development, this article is for you. In the next section, we will discuss the advantages of neural networks and their importance in data science.

There are numerous benefits that neural networks can provide to data science professionals. Some of them are listed below:

• Neural network algorithms help in parallel processing. It means that data science professionals can perform more than one job at a time. It boosts analyzing data and uses GPU computations effectively.
   
• The neural network stores the data in the entire network rather than any specific database. It then uses the stored data to make decisions.
   
• The neural network is fault-tolerant. It means that the breakdown or disintegration of one or more cells within the ANN will not prevent generating output. 
   
• It helps data science professionals to associate and correlate model nonlinear, complex relationships. It also helps create an association between input and output data.
   
• Using ANN, professionals can uncover or learn hidden relationships in the data without steering any fixed relationship between data.

Data scientists and data science professionals are using ANN and neural network code as a part of deep learning to leverage data for analyzing trends and making predictions. After mining and cleaning the data, the data scientists use that data to predict market trends, weather forecasts, health issue rates, etc. Again, the ANN also enables machines to make data-driven decisions. For automating various data-driven tasks like image recognition, real-time decision-making from data analytics, speech recognition systems, Natural Language Processing (NLP), and predictive analytics, neural network algorithms play a significant role.

Aspirants looking to enter and become successful in the data science career path often want to be fluent in data analysis. For diving into the neural network and ANN algorithm development for data science, aspirants should have substantial knowledge of the following domains.

• Mathematics
  If an aspirant wants to develop his/her career in neural networks for data science, a strong foundation in mathematics is necessary. Various mathematical concepts like linear algebra, calculus, derivatives, and probability theory are paramount to understanding the algorithms and techniques used in neural networks.
   
• Programming
  ANNs require programming to develop complex algorithms and train them using large data sets. For this, aspirants must have a thorough understanding of programming and writing codes or algorithms. Popular programming languages used in neural network programming for data science are Python, R, MATLAB, etc.
   
• Problem-Solving
  Neural Networks and Deep Learning are interrelated. Both need data to train to provide better insights from granular data. Aspirants must understand various problems a business encounters and solve them with programming skills along with data structures, control structures, and object-oriented design methodologies. Aspirants might also need to break down the problem into smaller components.
   
• Statistics
  Various statistical analyses and distributions play a significant role in understanding neural networks. Also, aspirants should have a basic knowledge of different techniques like standard deviation, statistical analysis, hypothesis testing, etc.
   
• Critical Thinking
  Neural networks can generate unexpected data-driven results within data science. But to assess the accuracy and reliability of the results, aspirants must need to have critical thinking skills. The ability to analyze and interpret data is also crucial.
   
• Communication
  Communication is another significant aspect that aspirants should cultivate to get an amazing career in neural networks and data science. If you know how to communicate your technical aspects (neural networks) with non-technical stakeholders and business executives, it will help you grow in this career.

There are various use cases for ANNs across different industries. As an aspirant, you must know the various applications neural networks deliver while dealing with data science to understand how neural networks work completely. The following are some impacts neural networks can portray in day-to-day activities.

• Image Recognition and Character Recognition
  One of the most demanding verticals of neural networks that use data and data science techniques is image and character recognition. If you get a job in this domain, you have to develop algorithms that can feed in various images and characters. It is when your algorithm goes through model training. Once your algorithm starts to identify different items, your application is ready to go. Similarly, the character recognition system also identifies patterns and characters trained by feeding image datasets. Image recognition is in demand in various companies and security organizations.
  
  If you have a knack for ML, neural networks, and digital image processing, this field is awesome. According to the Markets and Markets research report, the image recognition market cap will reach 26.2 billion USD in 2020 to 53.0 billion USD by 2025. It will reflect a CAGR of 15.1% during the forecast period. It shows that the market is expanding exponentially. So, if you want to build your career in image recognition through neural networks, you can start developing skills online.
  
  Careervira is an excellent online learning platform that offers image recognition courses.
  • Hands-On Image Recognition: Python Data Science Bootcamp
  • Python Image Recognition: Hands-On Artificial Intelligence
     
• Forecasting Customer Demands, Weather, Market Trends, and Others
  Demand forecasting and predicting market trends have become essential skills that use predictive analysis. Retail and e-commerce companies use historical data to predict the market trend or customers' future demand for various products and items. By forecasting the requirements, companies can make informed supply decisions. Forecasting in retail and online e-commerce businesses also helps estimate the total sales and revenue for a particular period in the future. Businesses use sales forecasting techniques to gauge the exact demand accurately. Weather forecasting and other natural calamities use data to predict them.
  
  As aspirants, you must have a keen interest in these verticals. Also, aspirants should have a thorough understanding of developing deep neural network models that use data to train themselves. Companies like Amazon, Netflix, Walmart, etc., use forecasting algorithms to understand customer demand and market trends. Other financial companies and weather forecasting organizations also hire aspirants to perform such analyses.
  
  Careervira is an excellent online platform where you can learn various courses on forecasting and predictive analytics that uses neural networks, ML, deep learning, and AI.
  
  Some well-known courses that can help you get a thorough idea on forecasting and predictive analytics using neural network and AI are:
  • Forecasting Product Demand in R
  • Demand Forecasting Using Time Series 
  • Lucrative Trend Forecasting 2 - 2021 Edition
  • Financial Analysis for Business Performance
  • Predictive Modeling and Analytics 
  • Predictive Analytics and Data Mining
     
• Credit Rating and Risks
  Credit rating is the evaluation of whether the debtor can pay back the debt or loan. It is like a financial risk analysis. Firms like Deloitte and HDFC often require skilled aspirants who can develop and run neural network models for predictive analytics and financial risk assessment. Financial organizations have started depending on data science professionals who are experts in neural network algorithm development. They use implicit forecasting based on the debtor's past record, wealth data, or other factors and determine the likelihood of the debtor defaulting.
  
  Aspirants can have a thorough knowledge of ML and deep learning to get into financial fraud detection and work as credit card risk analysts. Careervira is an online learning firm that offers cost-effective, self-paced courses on financial risk prediction that will teach you skills like neural networks, ML algorithms, risk analysis, etc.
  
  Some well-known courses on financial and credit risk analysis using ML and ANN available on the platform are:
  • Neural Networks and Deep Learning 
  • Professional Certificate in Machine Learning and Finance
  • Software Requirements Prioritization: Risk Analysis
     
• Security Fraud and Attack Detection
  Another vertical where aspirants can use neural networks is to detect fraud and security attacks. Financial, healthcare, IT, and other companies use automated fraud detection techniques and tools like Identity and Access Management solutions. Developing automated fraud detection tools require a neural network trained through massive data about fraud and attack patterns. Document forgery, identity theft, financial fraud, insurance fraud, etc., are some situations that need automated tracking and fraud identification.
  
  According to the Markets and Markets research report, the global technology-driven Fraud Detection and Prevention market cap will grow to 65.8 billion USD by 2026. It will reflect a CAGR of 21.5% during the forecast period. It shows that the demand for fraud and security detection through neural networks and ML algorithms is at its peak across many industries. Aspirants who want to build fraud detection apps and automated threat identification tools must learn neural networks and data science to stand out in the competitive data science job market.
  
  Careervira is an online learning platform offering incredible courses with industry-standard skills.
  
  You can grasp various fraud detection tools across various industries by opting for the following courses: 
  • Fraud Detection in Python
  • Fraud Detection in R 
  • Fraud Detection with Python 
  • TensorFlow and Linear Regression 
  • Financial Statements Fraud Detection Training 
     
• Face Recognition Apps and Services
  Many companies use apps and services that offer face recognition and user face identification systems as biometric authentication. This technology is in demand, and innovations are happening around it. Facebook uses human face recognition to suggest tagging, android phones and mobile phone apps use it for unlocking phones and apps, and traffic cameras use it to identify a person in real-time. If you are an aspirant looking to develop such human face recognition apps using neural networks, you must develop neural networks and programming skills to build effective AI solutions.
  
  According to the Allied Market Research report, the global technology-driven facial recognition market size was at 3.83 billion USD in 2020. According to their projection, it will reach 16.74 billion USD by 2030. The market will soon reflect a CAGR of 16.0% from 2021 to 2030. This report shows that the human face recognition market is growing at an exponential rate.
  
  If you have a deep fascination for how the human brain identifies people and develop apps that can detect the human brain, you must gather skills of neural networks that feed on data. Careervira is the leading platform for self-paced courses. You can learn AI and neural networks or get into specific courses that guide you on human face recognition project development.
  
  Some of the best courses on face recognition available on this platform are:
  • Automated Multiple Face Recognition AI Using Python
  • Computer Vision: Face Recognition Quick Starter in Python
  • Build a Face Recognition Solution with Microsoft Azure Face API

So far, we have understood what a neural network is, its advantages, its importance, and the various day-to-day activities of enterprises and businesses from ANN techniques. We are in a competitive data science job era. Aspirants should have specialized skills in data science to make themselves stand out from the rest of their peers in the interview room. Let us now explore the top 5 skills of data scientists required for a neural networking job. These skills are mandatory if you, as an aspirant, wish to join a data science job role.

• Applied Mathematics and Statistics for Data Science
  The first set of skills that will evolve you as an aspirant is mathematics and statistics. It helps to calculate various algorithms and unlocks the minds of aspirants to think of solving problems from a calculative and numerical perspective. Statistical solutions will help you analyze and chalk out the appropriate details from multiple data in Data Science. Mathematical problem-solving skills like linear algebra, calculus, metrics, vectors, etc., are some domains whose basic knowledge aspirants must have. Understanding standard deviation, probability, hypothesis testing, sampling, gradient descent, distance matrices, and solving problems using distributions are some problems that will help aspirants excel in neural network career development.
  
  Careervira is an excellent platform that can help you brush up on your applied mathematics and statistics skills. They offer various self-paced courses that are excellent and taught by industry-grade experts to help you stand out in this competitive data science job market.
  • Mathematics for Data Science: Essential Mathematics for Machine Learning and AI
  • Essential Mathematics for Data Science and Machine Learning: Using R
  • Mathematics for Machine Learning Specialization
     
• Programming Skills and Data Structures
  Programming is another must-have skill that data science aspirants must hone to excel in their neural networking job. If you want to become a neural network and deep learning expert, programming skills like Python, R, C++, MATLAB, etc., are necessary. Among all of these, the Python language is the most prominent. Neural network algorithm development also requires programming languages to develop them for data-driven training. Apart from programming and logic building using codes, data science professionals must also have an in-depth knowledge of data structures. According to a research report, the programming language market size was 154.68 billion USD in 2021. It will reach 343.84 billion USD by 2029, reflecting a CAGR of 10.5% during the forecast period.
  
  If you wish to develop outstanding skills, start learning programming languages from top institutes and instructors.
  
  Careervira offers an online platform where you can purchase self-paced courses:
  • Deep Learning & Neural Networks Python Keras For Dummies
  • AI Programming with Python
  • Programming Essentials with Python
  • R Programming Fundamentals
  • Build a Deep Learning Based Image Classifier with R 
  • Coding for Everyone: C and C++ Specialization
     
• Data Engineering, Analysis, Modeling, and Evaluation Skills
  Every company is a data-driven company as data has become the new oil. Enterprises leverage big data to train deep learning and neural network algorithms and to pull insights to streamline the business trajectory and operations. Data helps business executives to make the right decisions, analyze trends, etc., and maintain equilibrium within the company. Managing an organized way for all such massive data is the role of data engineering. Aspirants with proper managing skills get a competitive benefit in this cut-throat job competition. Again, knowledge of analyzing data, generating visuals, and modeling the programs for analyzing data is another skill also in demand. Finally, if an aspirant has critical thinking skills, it becomes easy to identify whether the automated neural network or deep learning algorithm generated the right report or not. It comes under evaluation skills.
  
  Seeing all these basic skills, you might already guess that the aspirant has the skills to become a data scientist. Yes, a combination of all these data-driven skills will surely land an aspirant in a data scientist job role. To push their career growth to the next level, Careervira is the best platform you can pick to grab these skills with the help of multiple flexible self-paced courses:
  • Data Engineering for Everyone
  • Data Analysis and Visualization with Power BI
  • Learning Data Modeling
  • Data Visualization Nanodegree Program
  • Data Modeling in Power BI
     
• Developing Artificial Intelligence and Machine Learning Algorithms
  Aspirants who want to stand out in the competitive data science job market with neural network domain must have proficiency in AI and ML algorithm development or how they work. An in-depth understanding of K-Means Clustering, Fuzzy C-means algorithms, etc., becomes mandatory for aspirants to understand as they proceed in their careers. Companies might ask what they are and how they work. Again, a deep understanding of supervised learning, unsupervised learning, and reinforcement learning, and their respective algorithms is also essential. According to the Grand View Research report, the global AI market was 136.55 billion USD in 2022. They are expecting it to expand at a CAGR of 37.3% from 2023 to 2030.
  
  If you want to stay on the leaderboard during a written or an interview, sharpen your AI and ML skills. The most well-known and easy-to-go learning is online learning. Careervira excels in providing different generalized and specialized courses on AI and ML domains. Their self-paced courses remain aligned with industry standards. If you are an aspirant or a working professional who wants to upskill AI and ML skills, Careervira is the best learning platform. Some well-known AI and ML courses available on the platform are:
  • Digital Skills: Artificial Intelligence
  • Artificial Intelligence by Udacity
  • Introduction to Artificial Intelligence (AI)
  • Artificial Intelligence (AI) Education for Teachers
  • Machine Learning Fundamentals
  • Machine Learning Tutorial: Build Machine Learning Applications
     
• Understanding Deep Learning and Artificial Neural Networks
  Once you, as an aspirant, master the skills of AI and ML, you must gain proficiency in deep learning and ANN algorithms. Deep learning offers more detailed data-driven learning to machines. Data scientists and deep learning engineers automate the machines to think on their own and produce decision-making abilities through algorithms like generative adversarial networks, deep belief networks, Long Short-Term Memory Networks, etc. Deep learning also uses ANNs and algorithms like convolutional neural networks, recurrent neural networks, etc. As a budding aspirant, you must gain proficiency in deep-learning libraries like PyTorch, Keras, DL4J, Caffe, Microsoft Cognitive Toolkit, etc.
  
  Unlike all the mentioned skills, understanding how the input layer gets trained, the neural network model works, and what type of data to feed when in the ANN model will give you a competitive edge to leverage data and help machines think based on the trained data. To gather such skills or to upskill yourself, refer to online course-providing platforms. Careervira is an excellent online course provider, offering self-paced courses on generalized and specialized verticals. Some top-notch industry-standard courses it provides are 
  • Deep Learning for NLP in Python
  • DeepLearning.AI TensorFlow Developer Professional Certificate
  • Artificial Neural Network for Regression
  • Neural Network from Scratch in TensorFlow
     

Apart from these skill combinations, a thorough understanding of Big Data, cloud technology for computing massive data, and GPU technologies will also make you stand out in the competitive Data Science job market. Careervira offers various courses on these verticals to prepare you to stand out in this data science career. Some well-known courses to upskill yourself are 

• Big Data Analytics
• Big Data And Data Analytics Program
• Azure Cloud Fundamentals for Data Science with Python
• Cloud Computing Essentials
• GPU Programming Specialization

There are numerous job roles in the field of data science where ANN and deep neural networks play a significant role. In this section, we will dive deep into those roles and their salary.

• Data Scientists
  Dealing with accurate data and training algorithms using those data to bring value to a company is one of the most critical phases for every business's growth. Data Science uses statistics, data structures, AI, ML, ANN, and other technical verticals to extract actionable insights hidden in a company's data.Top companies like Amazon, Google, Netflix, IBM, etc., provide both entry level data science jobs and advanced positions to add value to their business. As per the US Bureau of Labor Statistics, there will be an exponential rise of Data Scientists' jobs by 36% growth between 2021 and 2031. On average, the annual salary of a Data Scientist with high skills is $144,327.
   
• Neural Network Engineer
  Neural network engineers are specialized data science engineers who work with neural network algorithms. They design and build neural networks for various applications, including image recognition, NLP, and predictive modeling. They use processed data to train their algorithms. They are responsible for selecting appropriate algorithms, tuning hyper-parameters, and optimizing the neural network architecture. On average, the annual salary of a neural network engineer with high skills is $118,424.
   
• AI Engineering
  They are computer science engineers who build AI algorithms and projects. They also use data to train neural network models. They also use neural networks as part of their development process that can be used for tasks such as classification, regression, and clustering AI algorithms. Many companies hire AI engineers to comprise multiple roles and skills like understanding data structures, AI algorithms, app development processes, software engineering, SDLC, etc. 
  
  They work with ML algorithms, data analytics automation, and other AI technologies to create intelligent systems that can analyze data, make predictions, and automate tasks. They also ensure that AI systems are reliable, scalable, and secure. According to an Emergen Research report, the global AI engineering market reached USD 8.05 billion in 2020. As per the expectation, the market will register a revenue or CAGR of 41.2% from 2021 to 2030. On average, AI engineers and professionals earn $135,500 to $165,000 annually.
   
• Machine Learning Engineer
  ML engineers are professionals who train machines to learn on their own. They use supervised, unsupervised, and reinforcement learning algorithms and train them with enterprise data to automate various tasks and detections. ML engineers should have proficiency in programming, software engineering principles, neural network modeling, analytical skills, and data science knowledge. Customer-driven companies like Netflix, Hulu, Alibaba, Uber, etc., hire ML engineers who help understand the customers' moves and choices and develop direct marketing or advertising campaigns. They develop and research ML-based programs that make predictions and learning. On average, ML engineers make a salary of $136,150 annually.
   
• Deep Learning Engineers
  Deep learning engineers are IT professionals who specialize in developing, designing, and implementing deep learning algorithms and training models using big data. They leverage the data generated by companies and customer-facing apps. They typically develop programs using technologies like AI, ML, ANNs, etc. They are responsible for designing and developing complex neural networks and optimizing deep learning algorithms to accomplish higher accuracy and efficiency. They are also responsible for training and validating models. They automate various data-driven predictions using neural network algorithms. On average, deep learning engineers make a salary of $142,282 annually.
   
• Computer Vision Engineers
  Computer vision engineers are IT engineers who develop CV projects like color detection systems, people-counting tools, and apps used in traffic system cameras, hand gesture recognition, etc. They have specialization in the development and implementation of computer vision technology by training neural network models with large datasets. This interdisciplinary field focuses on allowing computers to analyze and examine visual or digital data around them, such as images, animated graphics, and videos. Aspirants who want to become computer vision engineers must have thorough skills in computer graphics, AI/ML, neural networks, and digital image processing. On average, computer vision engineers make a salary of $130,783 annually.
   
• NLP Engineers
  NLP is another significant branch of AI where machines can understand human languages and interpret them meaningfully. NLP engineers develop NLP technologies and algorithms that help computers and digital systems understand, analyze, and generate human language. If you want to be an NLP engineer, you have to have a thorough knowledge of neural network program development, ML, and big data to design and develop software applications that can process and analyze natural language data, such as text and speech. For training NLP algorithms, NLP engineers have to use massive datasets and feed them into ANN models. Various virtual assistants, chatbots, sentiment analysis, and machine translation use NLP technologies. On average, NLP engineers make a salary of $155,000 annually.
  They work with ML algorithms, data analytics automation, and other AI technologies to create intelligent systems that can analyze data, make predictions, and automate tasks. They also ensure that AI systems are reliable, scalable, and secure. According to an Emergen Research report, the global AI engineering market reached USD 8.05 billion in 2020. As per the expectation, the market will register a revenue or CAGR of 41.2% from 2021 to 2030. On average, AI engineers and professionals earn $135,500 to $165,000 annually.
   
• Machine Learning Engineer
  ML engineers are professionals who train machines to learn on their own. They use supervised, unsupervised, and reinforcement learning algorithms and train them with enterprise data to automate various tasks and detections. ML engineers should have proficiency in programming, software engineering principles, neural network modeling, analytical skills, and data science knowledge. Customer-driven companies like Netflix, Hulu, Alibaba, Uber, etc., hire ML engineers who help understand the customers' moves and choices and develop direct marketing or advertising campaigns. They develop and research ML-based programs that make predictions and learning. On average, ML engineers make a salary of $136,150 annually.
   
• Deep Learning Engineers
  Deep learning engineers are IT professionals who specialize in developing, designing, and implementing deep learning algorithms and training models using big data. They leverage the data generated by companies and customer-facing apps. They typically develop programs using technologies like AI, ML, ANNs, etc. They are responsible for designing and developing complex neural networks and optimizing deep learning algorithms to accomplish higher accuracy and efficiency. They are also responsible for training and validating models. They automate various data-driven predictions using neural network algorithms. On average, deep learning engineers make a salary of $142,282 annually.

We hope this article provided a comprehensive guide on how neural network skills can help you get specialized data science job roles in this competitive market. The article also discussed the various advantages of neural networks and their particular importance in data science. Then, it highlighted the diverse neural network applications in data science. Aspirants who want to gather the top 5 skills in neural networks for data science job roles can also get a fair idea of it in the subsequent section, along with some cutting-edge industry-standard courses. Finally, the comprehension also mentioned the highest paying data science jobs that demand neural network skills with a data-driven skillset. This section discussed the salary for various roles and what roles they play in the organization. Careervira is an excellent option for pursuing career-upskilling courses and developing new skills through its self-paced courses.