The impact of data science on the manufacturing industry in Bangalore

The impact of data science on the manufacturing industry in Bangalore

by with No Comment Data Science

Data science and artificial intelligence has completely reformed the manufacturing industry in Bangalore and enabled the sector to achieve all its objectives. The present manufacturing industry is using data science to boost its productivity and production and reduce its energy cost. Data science offers manufacturers meaningful insights to increase profit, minimize risk, and speed up the execution time. The following guide will illuminate the data science that is dramatically changing the manufacturing industry.

  • Product design as well as development 

Data science enable manufacturers to understand customer behavior, preference, and taste to fulfill their demands and also satisfy their requirements in designing the product to grab the attention of customers and assess the amount of competition risk to launch a new product in the market or bring improvement in the existing production services. With the help of management tools and technologies, manufacturing companies gain insights into product modeling, decision-making, and ineffective planning. Data science enables them to collect customer feedback and generate ideas about customer sentiments.

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  • Predictive analysis

Data science helps the manufacturing industry to analyze data and forecast future problems to eliminate them from the rules. Data scientists analyze the business challenges and works accordingly to eliminate errors in the future. Manufacturers use predictive analysis at its full potential to monitor their company operations, performance, and function and find out a suitable solution in order to overcome challenges and difficulties. 

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  •  Preventive maintenance and fault prediction

 Manufacturers leverage data science to predict faults of manufacturing equipment. As data science helps to make fault predictions, these failures can be prevented timely or reduced to a certain extent. Predictive techniques of data science help eliminate such failures, and today, manufacturers in Bangalore are using preventive maintenance methods such as usage-based methods and time-based approaches to properly plan manufacturing operations. Manufacturers can plan a shutdown or breakdown for repairing work equipment. These breaks will help to avoid potential failures and dealings with the equipment. 

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  •   Price optimization

Manufacturing sector must consider several factors before deciding the price of the products, from the stock price of products consisting of manufacturing cost, price of raw materials, maintenance, and distribution cost. Price optimization helps manufacturers to decide the best pricing structure that can be charged to the customers. With the help of price optimization, manufacturers neither charge too high nor too low prices from customers and, at the same time, gain profit. With the help of data science, manufacturers can analyze cost data and pricing data from both internal and external sources, and this gives them a competitive edge for deriving optimized price variants.

  •  Robotization And Automation

 Bangalore's manufacturing sector uses robots to perform routine activities and tasks that can be dangerous and difficult for human workers to manage. Manufacturers have to invest a huge sum of money in automation and robotization each year. Data science helps the manufacturing industry in the smooth functioning and programming of robots that can enhance the quality of products. New robots are coming into the production line every year, and data science is enabling robots to revolutionize and benefit the manufacturing sector. With the help of data science, manufacturing robots have become much more affordable for manufacturing industries.

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  • Supply Chain Management

 Manufacturers in Bangalore are leveraging data science analytics to manage supply chain management and risks. The supply chain is complex with big data analytics and data science; the complex tasks can be easily managed. Data science enables manufacturers to analyze the potential delays or risks involved in the manufacturing operation and calculate all the probabilities regarding
problematic issues. In this way, the manufacturing industry can make effective
plans beforehand and detect backup suppliers. Real-time data science and data analytics tools help keep up with the revolutionizing world. The predictive analysis combined with preventive maintenance help to manage their supply chain management and operate a successful manufacturing company.

  •  Inventory management and demand forecasting

 Online inventory management tools and software help manufacturers collect data and make future analyses. This further helps improve supplier- manufacturer relationships and properly regulate the supply process and stocks. Demand forecasting includes the analysis of accountants' work and data thoroughly. Demand forecasting combined with inventory management benefits the manufacturing industry in Bangalore. They help manufacturers avail raw materials, use artificial intelligence to analyze the current market, and use other effective technologies to control the inventory and restrict storing of unrequired and useless products.

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  •  Warranty analysis

In Bangalore, manufacturers have to spend a huge chunk of money on warranty claims, reliability, and quality of products. Data helps manufacturers to analyze their defective products and reveal information about early warnings. Manufacturers are leveraging data science tools and technologies to understand a product's potential defects and collect data to analyze them and improve existing products or develop new products for customers. Warranty analysis combined with artificial intelligence is enabling manufacturers to process massive amounts of warranty-associated data derived from different sources and also discover issues about the warranty of products.

  • Computer vision applications and AI-powered technologies

For big firms and corporations, sustainability is a primary concern as a part of their long-term strategy. Today's manufacturing industry is setting ambitious objectives and trying to save energy and reduce carbon commissions to reduce environmental crises. It includes critical calculations about supply chain management and energy usage prediction to maintain efficient manufacturing production. Data science helps the manufacturing industry achieve its objectives by powering AI technologies and computer vision applications. Data science has brought modern quality control approaches like detection, object identification, classification, and monitoring the process through computer vision to achieve the desired outcome. Computer vision application enables manufacturers to improve product quality, achieve high-speed processing capacity, and lower labor costs.

  • Conclusion

 The application of data analytics and data science in the manufacturing industry is improving business productivity and automating work. You can expect the introduction of advanced manufacturing techniques and tools that will completely revolutionize the manufacturing sector. For instance, Amazon's delivery of products through drones is being built by Amazon  to locate the location of customers and deliver the products directly to facilitate efficient and easy delivery of products. The application of data science in the manufacturing industry is immense, and it is also changing the present trends of conventional methods that manufacturers used in the past. It is boosting the revenue generation of the manufacturing companies, and in this way, it contributes to the overall economic growth of the sector.

The Importance of Data Governance in Data Science

The Importance of Data Governance in Data Science

by with No Comment Data Science

 In order to get insights and power decision-making processes, enterprises across sectors now depend on the rapidly expanding discipline of data science. A key component of data science is data governance, which guarantees optimal management of data throughout its existence. This post will examine the significance of data governance in data science. Kick start you career by enrolling in this data science certification.   

Data governance: What is it?

The administration of the accessibility, usability, consistency and security of the data utilized in an organization is referred to as data governance. It consists of the standards, guidelines, and policies put in place to guarantee efficient data management. Data governance aims to guarantee reliable, consistent, and correct data.

What justifies the importance of data governance in data science? 

Because it guarantees the accuracy and dependability of the data used in the research, data governance is essential to data science. Data scientists need high-quality data to generate valuable insights that inform business choices. The organization may suffer serious repercussions if the data is of poor quality or unreliable, resulting in erroneous outcomes. Organizations can comply with rules and legal obligations with the aid of data governance. Strict laws governing the use of data are prevalent in several sectors, including healthcare and banking. The use of data must be ethical and lawful, and data governance makes sure that the company is adhering to the relevant rules. Moreover, data governance encourages cooperation and communication between divisions within a business. Effective data management simplifies information sharing across teams, facilitating improved decision-making. For instance, the marketing team can leverage well-managed consumer data to create more successful marketing plans if it is available to them. 

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Data science components of data governance 

To guarantee that data is managed successfully, a number of important factors related to data governance in data science must be taken into account. These elements comprise:

  1. Data governance’s underlying principle is data quality. It guarantees the completeness, accuracy, and consistency of the data. To avoid having their research tainted, data scientists must make sure the data they utilize are of the highest caliber.
  2. Data governance must prioritize data security in order to prevent data from illegal access or usage. To prevent data breaches and theft, organizations must put in place the necessary security measures.
  3. Data privacy: Data privacy is necessary to guarantee that data is used morally and that people’s privacy is safeguarded. To ensure that they are utilizing data in a way that is legal and ethical, organizations must adhere to regulations like GDPR and HIPAA.
  4. The design and organization of the data an organization uses is called its data architecture. The organization of data must be trustworthy, safe, and easily accessible data management: The police
  5. Data management: The policies and practices established to manage data at all stages of its lifespan are referred to as data management. Collection, archiving, processing, and analysis of data are all included.

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Data governance benefits for data science 

 Effective data governance procedures can provide a business with a number of advantages. These advantages consist of the following:

  • Better decision-making: Good data governance makes sure that the information utilized for analysis is correct and dependable. This then results in making wiser decisions.
  • Efficiency gains: Data governance ensures that data is properly organized and maintained, which can improve the efficiency of processes and procedures.
  • Regulation adherence: Data governance guarantees that enterprises abide by rules and regulatory obligations pertaining to the use of data, shielding the firm from negative legal or financial repercussions.
  • Improved departmental collaboration is encouraged by data governance, which can improve communication and decision-making inside an organization.
  • Improved data governance guarantees that data is reliable and of high quality, which increases data confidence. So, the organization as a whole becomes more confident in the information utilized to make decisions. Don’t delay your career growth, kickstart your career by enrolling in this  data science course in Hyderabad with placements.

 

Challenges of Data Governance in Data Science

  • Lack of resources: Adopting sound data governance principles necessitates substantial financial, human, and technological investments. Implementing thorough data governance standards may be challenging for smaller firms since they may find it difficult to allocate these resources.
  • Lack of stakeholder support: All stakeholders, including executives, managers, and employees, must support effective data governance. Stakeholders may fail to provide the required funding or support for necessary reforms if they do not comprehend the significance of data governance.
  • Opposition to change: Adopting data governance frequently necessitates considerable adjustments to an organization’s policies and procedures. It might be difficult to put into practice good data governance practices due to resistance to change.
  • Data silos: An organization’s departments must work together to regulate data effectively. Data silos, on the other hand, are commonplace in corporations, where various departments each have their own data and don’t share it. Getting rid of these barriers and encouraging teamwork might be difficult.
  • Data complexity: Complex data systems are used by many organizations, and data is stored in many places and forms. Effective data governance procedures might be difficult to execute when managing this complexity.
  • Lack of knowledge about data quality: Although it is an important part of data governance, many businesses are unable to define or assess data quality. The implementation of efficient data governance practices may be difficult as a result.
  • Ineffective data governance is a result of a lack of standards. It might be difficult to manage effectively because a lot of firms have data that is inconsistent or non-standardized.
  • Data governance platforms, data management tools, and data quality software are all necessary for the implementation of successful data governance procedures. Yet, there is a lack of these tools in many organizations. Establishing thorough data governance practices may be difficult due to limited technological infrastructure.
  • Data privacy issues: Data privacy is a key aspect of data governance, yet firms may find it challenging to abide by laws like GDPR or HIPAA. Implementing efficient data governance procedures while still safeguarding individual privacy may be difficult as a result.
  • Lack of training: Personnel who are trained in data management, data quality, and data governance procedures are necessary for effective data governance. Effective data governance procedures might be difficult to execute since many firms do not offer enough training. Kickstart your career data science course in Pune with a placement guarantee.  

Conclusion

The administration of data throughout its lifecycle, including data quality, privacy, security, and compliance, is referred to as data governance in the field of data science. Good data governance procedures guarantee that data is trustworthy, accurate, and consistent and that it can be applied to making decisions. It also aids in establishing policies and procedures for data management, encouraging teamwork, and guaranteeing legal compliance. Collaboration amongst stakeholders, including executives, managers, and employees, as well as the usage of cutting-edge technical infrastructure, are necessary for effective data management. Using efficient data governance procedures may enhance decision-making, increase efficiency, and boost data trust.  

 

 

Data Science course (11)

Top data science tools used in Bangalore

by with No Comment Data Science

Data science is a field that requires the practice of scientific methods, algorithms, systems, and processes for extracting insights and knowledge from both unstructured and structured data. It is an interdisciplinary and revolutionary field that combines statistics, mathematics, information science, computer science, and domain expertise for uncovering hidden patterns, insights, and correlations from data. 

What are data science tools?

 Data science tools and technologies are software applications enabling users to extract, analyze, and process huge volumes of data and information. These important tools and technologies help analyze hidden patterns, build predictive models, and uncover valuable insights from raw data. These tools are utilized for various purposes and tasks like determining customer segments, forecasting market trends, and customer churn.  

In this article, you will read about the top ten data science tools that are used widely in different industries 

  1. Python:

An interpreted, general-purpose, high-level programming language is Python. It is a simple-to-learn and understandable language that offers a wide variety of frameworks, tools, and libraries that have immense use in machine learning and data science projects. In addition, Python includes several libraries and packages for data analysis, manipulation, machine learning, data visualization, etcetera, making this data science tool a perfect choice for data analysts and scientists. It includes strong libraries used for image processing, language processing, deep learning, etc. Python is used in data analytics and data science projects because it is a universal programming language and has a strong user and developer community. 

  1. Apache Spark:

Developed in 2009, Apache spark is an important data analytics and processing tool. This open-source distributed computing platform was originally developed at the University of California and enabled professionals to process huge data sets. It is a fast in-memory data processing engine designed for data science professionals to facilitate ease of use. It offers different libraries for streaming machine learning, graph processing, and SQL. It Includes high-level APIs in Python, R, Scala, and Java. 

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  1. TensorFlow:

There are diverse applications of this data science tool. This open-source machine learning library is used to make data analytics and data science projects successful. TensorFlow offers powerful tools, libraries, and technologies that assist data analysis, predictive analytics, and data visualization. TensorFlow is a common choice for data engineers and data scientists because this tool has high scalability and flexibility. The TensorFlow application includes natural language processing and image processing. It comprises algorithms that are used for supervised learning, deep learning, and unsupervised learning.

  1. Hadoop:

Hadoop helps in storing massive amounts of data within a distributed space. This data science tool enables distributed processing of data with the help of the MapReduce programming model. In other words, it can run continuously even in case of failure of a specific node in the cluster.

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  1. Tableau:

This is a powerful and commonly used data visualization tool used for creating interactive dashboards and other visuals. Tableau enables data science professionals to analyze data and information quickly, develop visuals, and collaborate. This data science tool has immense use in the business sector for analyzing data and making informed business decisions. It is used for exploring as well as understanding data and information in a better way. 

  1. Apache Hadoop:

This is a powerful data science tool that comprises of distributed computing platform that enables data processing as well as data analytics. It is an open-source program that comes with distributed processing of massive amounts of data across different computer systems. Hadoop uses a distributed file system to facilitate data storage amounts and MapReduce to facilitate data processing. Apache Hadoop is used for analyzing huge sets of data from distinct sources. Some applications of Apache Hadoop in the field of data science are machine learning, web indexing, and data mining.

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  1. AWS:

AW stands for Amazon Web Services, a popular cloud-based computing platform that facilitates data computing, storage, networking, and many other services. It enables users to access computing resources like virtual machines, databases, and storage. This is a secure, reliable, and cost-effective cloud-based platform that helps business to manage their tasks. It enables users to scale up or down their infrastructure and make payments for the resources that they are using. There is a wide range of services and tools that AWS comprises to enable users to develop, deploy as well as manage applications within the cloud platform.

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  1. Excel

Excel is a popular data science tool that helps in managing different tasks like data processing, data calculation, and visualization. It is used mostly for analyzing data, so it is an efficient and highly used data science analytical tool. It has different filters, tables, slicers, and formulas. Excel is a highly popular data science tool that is used for spreadsheet calculation and data visualization. 

  1. SAS

 A closed-source software designed mainly for performing analytical tasks is SAS. It supports different data format types and is known as the fourth generation of programming languages, as the cost of SAS is quite high. Therefore, big and large companies mainly use this data science tool. SAS is an efficient and trustable closed-source tool that provides different analytical libraries as well as devices to users for organizing data. 

  1. RapidMiner 

Rapid miner is a powerful and widely used integrated data science tool developed mainly for performing predictive analysis, advanced analytics such as text analytics, data mining, visual analytics, and machine learning without requiring any programming. This data science platform can work with any data source, such as Microsoft SQL, IBM, Excel, Access, Terra data, Sybase, MySQL, Dbase, SPSS, Oracle, etc. This powerful tool helps generate analytics based on real-life data conversion settings. 

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Conclusion:

Data and science professionals must extract, organize and analyze huge data sets to derive meaningful insights. As they have to examine and handle different data types, they work with various tools and technologies. The most common data science tools beginners use are SQL, R, and Python. In the present technological world, data is the new oil. Because of the rising demand and importance of data science and data analytics by all businesses, organizations and companies’ recruiters are hiring more data science professionals. It can be overwhelming for data science beginners as they have to deal with a variety of tools and technologies. The above-mentioned list will help both budding and experienced data scientists choose and get trained on the right tool. 

A complete overview of data science

A complete overview of data science

by Data Science

What is data science? 

An overview of data science that helps in extracting useful insights and knowledge from huge sets of unstructured and structured data using mathematical algorithms and scientific inference is known as data science. Data science includes a combination of computer science, statistical mathematics, data analysis, machine learning, domain knowledge, and data visualization. These mathematical algorithms require lots of processing and are implemented using computer programs and run on hardware. 

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Application of data science

Following are the application of data science that depends on data science and ML technologies to solve certain problem sets.  Are you looking to become a Data Scientist? Go through 360DigiTMG’s PG Diploma in Data Science and Artificial Intelligence!

Internet search results 

When a person searches on the internet or Google, critical ML algorithms decide the most relevant search results. Using these ML algorithms, the pages can be ranked accordingly so that the person gets the most relevant page just by clicking a button. 

Recommendation engine

The recommendation engine is a great example of a data science application. It is used by the music streaming service Spotify to recommend its user’s music lists as per their taste and preference. Data science algorithms generate data of each user over some time and recommend the music of their taste. This helps companies to attract more customers. Earn yourself a promising career in data science by enrolling in the Data Science Certification in Chennai offered by 360DigiTMG.

Intelligent digital assistant

One application of data science is using advanced machine learning algorithms in text or voice-based digital assistants, which are also called chatbots. For instance, Google assistant uses ML algorithms to translate the user’s speech to text, know about the context and provide relevant information. 

Addition to the field of data science

Data science has been evolving since the early 200s and includes more technologies in this multidisciplinary field. Some recent additions include-

Artificial intelligence

One core component of data science is known to be machine learning. With the rapid parallel computing capabilities, the latest addition to the field of data science is known to be deep learning.  Also, check the Best Data Science Institute in Bangalore with Placement to start a career in Data Science.

Intelligent systems 

Data science has included data-driven smart apps and intelligent applications accessible in portable form. A significant portion of data science is centered around machine learning which is the basis of these intelligent systems and smart apps. 

Edge computing 

This computing concept is related to the Internet of Things and has been recently incorporated into this field. It includes putting data science pipeline of data processing, collection, and delivery. This recently developed concept is availed through IoT. Become a Data Scientist with 360DigiTMG Data Science Coaching in Hyderabad. Get trained by the alumni from IIT, IIM, and ISB.

Security

One main challenge in the field of digital space is security. Digital systems are more vulnerable to hacking and malware injection concepts. Data science has recently added technologies and advancements to prevent the vulnerability of the digital system. Machine learning algorithms can now detect malware and computer viruses more effectively than traditional ML algorithms.

The life cycle of data science

Project analysis – the first step of data science is to determine the needs of the project regarding availability, source, the budget of data 

Data preparation: Here, raw data is converted to structured data and includes data visualization, data cleaning, transformation, etc. 

Exploratory data analysis- data scientist uses different tools and programming languages to explore data and extract insights. It includes feature selection, visualization, model selection, and rapid prototyping.

Model building – this step model is developed using ML technologies and an ideal hyperparameter. 

Deployment – after building the model, the next step is to deploy it in the real world in mobile or web apps. 

Real-world testing and results-Model output is monitored to detect where the output is failing. The key findings are noted and informed to stakeholders.

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What are the different types of data science certification?

What Are The Different Types of Data Science Certification

by Data Science

The data science certification curriculum prepares you for every job prospect. Data science certification can be both an online and offline skill assessment certificate that makes you industry-ready in front of the company that hires you to obtain meaningful data-driven insights. Data science is important because of the growing importance of data visualization, data analysis, and machine learning. Earn yourself a promising career in data science by enrolling in the Data Science Course in Pune offered by 360DigiTMG.

Data Science Training in Hyderabad

Top data science certification

Data science offers a lucrative package. To get recruited by a top IT company, you need to boost your resume with the help of data science programs that come in the form of certification and help you earn the standard market accordance data science skills. Different varieties of data science certifications help you go the extra mile to boost your performance and credibility in your portfolio. This data science helps in career transitioning and lets you earn career growth opportunities and perks. Looking forward to becoming a Data Scientist? Check out the Data Science Course and get certified today.

Dell EMC Proven Professional Certification Program

It offers comprehensive learning, practitioner, hands-on learning, and certification program. You can be a professional data scientist as soon as you pass this data science exam. Passing this certification would mean to your recruiters that you have possessed all the necessary skills. It offers specialist level and associate level programs considered to be advanced certification. The expiration of this certification is valid for up to two years, and the cost incurred to earn this certification is $200.

SAS Academy for Data Science

There are three programs included in this data science certification. They are-

  • A program that emphasizes big data skills
  • A program that emphasizes data analysis skills
  • A program that focuses on big data as well as data analytics skills

In this data science certifications, you get both the experience and the exposure to using data science tools and earn this certification. This credential certification doesn’t expire, and the minimum price incurred is $2250 for an individual certification course. Also, check this Data Science Chennai to start a career in Data Science.

Google professional data engineer certification

Google offers this kind of data science certification which helps candidates, especially those who want to design and monitor data processing systems and are interested in gaining fundamental knowledge about ML models. The cost of this certification is $200 and helps you learn Cloud SQL, Dataproc to transfer your present workplace to the cloud. It is a two-hour certification examination that sets you up for operationalizing, designing, monitoring, and building data processing system or other ML models. It helps you to learn quality industry-level experience.

Certified Analytics Professional

A vendor-neutral data scientist is offered by a certified analytics professional (cap) earning this certification would mean that you are not biased towards particular software. It also shows your recruiters that you have extensive knowledge in this domain. The expiration of this certification is valid for up to three years. Become a Data Scientist with 360DigiTMG Data Science Coaching in Bangalore. Get trained by the alumni from IIT, IIM, and ISB.

BM Data Science Professional Certification 

This certification helps candidates become job-ready and learn trendy data science tools and additional features, having beginner-level knowledge of deep learning algorithms, machine learning, and several data visualization techniques. Earning this certification helps you to learn practical skills in an industry-relevant way. You get real-time data solutions and unlimited access to all ten courses at 39 US dollars.

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Artificial Intelligence and Machine Learning Basics

Artificial Intelligence and Machine Learning Basics

by Data Science

What Do We Understand by Artificial Intelligence?

Artificial intelligence refers to the engineering and science of developing intelligent machines that can perform and exhibit cognitive functioning like humans. It consists of learning, reasoning, perceiving, and solving capabilities and abilities. The benchmark of intelligence for artificial intelligence stands at the human standard. It relates to factors and parameters such as vision, speech, and thinking. 

AI does not confine itself to the methods and practices that can get biologically observed. It can extend to the ways that work within the computer and machine system. Nevertheless, the overall purpose of artificial intelligence to make human life easier remains the same. 

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What Do We Understand by Machine Learning?

Machine learning can get defined as a subset or part of artificial intelligence. It is a process in which a machine gets “trained” or “educated” to learn and develop from its past skills and experiences. The subject of the teaching gets derived from the data collected and stored. Generally, it gets done when the said machine operated in the past.  

Machine learning involves a combination of the data with a set of required algorithms. It helps generate and deliver the final results. 

To summarize, machine learning is an application of artificial intelligence. It allows machines to understand and improve automatically from their experience.  Looking forward to becoming a Data Scientist? Check out the Data Science Course and get certified today.

What are the Points in Which Artificial Intelligence Differs from Machine Learning?

Although artificial intelligence and machine learning may seem the same, they are two entirely different concepts. The primary points of difference consist of the following:

  • Knowledge Acquisition

Artificial intelligence employs a system in which knowledge gets acquired. In addition to that, the concepts and ideas get applied and used for different and distinct purposes. 

On the other hand, machine learning merely refers to the system of acquiring and learning new ideas, skills, and concepts. The acquired knowledge conventionally does not get applied. 

  • Aim or Objective

Artificial intelligence aims to increase the possibility and chance of ultimate success. It does not target the accuracy aspect and element while doing so. Overall, it simulates and enacts cases and instances of natural intelligence. It gives it the ability to solve complex or complicated problems.  Also, check this Data Science Online Training in Hyderabad to start a career in Data Science.

The primary goal or objective of machine learning is the exact opposite. It works to increase accuracy. Conversely, it does not consider the success part. It allows machine learning to encompass a learning system from various data. It provides the machines with the capability of performing specific tasks. Furthermore, it aims to maximize their efficiency. 

  • Purpose or Application

AI entails a decision-making system where the machines mimic human cognitive skills to solve different and distinct problems. It seeks the optimal solution for any case. Moreover, it leads to wisdom and intelligence ultimately.  Become a Data Scientist with 360DigiTMG Data Science Colleges in Pune. Get trained by the alumni from IIT, IIM, and ISB.

On the other hand, machine learning entails a system that enables the machine to learn new information from pre-existing data. It involves the creation of self-learning and understanding algorithms. However, machine learning does not strive to seek any solution. Overall, it results in knowledge acquisition. 

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Data Science Training,

Data Science Training, Data Science – Machine Learning With Python

by Data Science

How is Python Valuable for Machine Learning and Data Science?

Python refers to a high-level, open-sourced, and interpreted programming language. It offers a suitable and desirable functionality to handle statistics, scientific functions, and mathematics. In addition to that, data science training it provides a constructive and favorable approach for all sorts and kinds of object-oriented programming. Overall, Python is an easy-to-learn language that almost all people can understand and grasp effortlessly and quickly. 

Python offers a comprehensive set of beneficial libraries to handle and operate the various applications related to data science. On top of that, it has an undemanding use. It can get owed to the unsophisticated syntax it applies. It makes it easier to read the language and adapt to it. It stands true even for those who do not have an engineering background. Looking forward to becoming a Data Scientist? Check out the Data Scientist Course and get certified today.

In other words, an individual can learn Python at any time. Also, they can use it in any application. For that reason, the language can get used in various research and scientific communities. On top of that, it is popular among numerous people all across the globe. 

Python is suited to quick prototyping actions. In addition to that, it is exceedingly versatile and productive. It can get owed to the scientific packages and Application Programming Interface (API) with which it remains associated. It makes it possible to employ the required changes in the language and upgrade its frameworks and systems. Furthermore, Python allows for an effortless data cleaning that proves highly advantageous for machine learning applications.  Also, check this Data Science Certification Course in Bangalore to start a career in Data Science.

 Python offers the required level and extent of job and network security. On top of that, it makes the processes and tasks of algorithm creation for fraud detection effortless. Sentiment analysis and natural language processing (NLP) also receive a significant boost with the help of Python. It can get owed to the substantial library collections it provides. They help solve complicated and complex problems associated with businesses and companies. Moreover, it helps develop data applications and systems. Become a Data Scientist with 360DigiTMG Data Science in Chennai. Get trained by the alumni from IIT, IIM, and ISB.

What Does Python Relate to Machine Learning Entail?

Python covers a broad scope that allows it to touch upon numerous sectors, industries, and fields. That is why a colossal number of people remain interested and invested in it. Thus, several courses teach Python related to machine learning and data science. In general cases, such lessons entail the following:

  • Translation of everyday and common business questions and various complex problems into simple machine learning applications and tasks to make data-driven assessments and decisions
  • Training of a classifier using various algorithm techniques from different libraries
  • Discovery underlying customer behaviors and building a Recommendation Engine depending on the patterns
  • Re-segmentation customer market for an improved and enhanced alignment of services and products according to customer needs and preferences
  • Development of predictive revenue models and other numeric parameters and variables
  • Investigation of flows and relationships between business entities and the relevant target audience or customer base
  • Use of Python Pandas to explore, visualize, and analyze data from various sources for trends and patterns associated with the business.

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360DigiTMG – Data Analytics, Data Science Course Training Hyderabad

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Phone: 1800-212-654321
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Data science in today's world

Data science in today’s world

by Data Science

It is a universal truth that big data is running the world. Almost all business companies have become data-centric, which means they use it to simplify their consistent methods. There is a lot of importance to data analysis and data processing. This data processing and analysis is where data science stands.

It is a stream where knowledge is gained from enormous data bytes using scientific procedures, approaches, algorithms, and structure. Data statistics, mining, analysis of predictions, etc., are various branches of data science. It uses many subjects like mathematics, computer science, statistics, information sciences, and technologies like machine learning, data engineering, pattern recognition, signal processing, etc.

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Reasons behind the increasing importance of data science

Companies can use the data according to their sector or field they are operating in. It is a compilation of many subjects, so it is vital for various industries. The following are some reasons why companies are using it for their growth and development.

Understanding their customers and building better products

Clients are the core of a business and play a crucial role in a business’s success and failure. Data science enhances the companies to understand the client more in a modified way. Data science has techniques that can explore the market, compare the company with other competitive companies, read the history of customers, and finally make better suggestions. By considering and analyzing the recommendations, the firm will build better-quality products, which will further improve its rating. Also, check this Data Science Course in Hyderabad with placements to start a career in Data Science.

Solving problems and canceling the risks

Business intelligence has become a powerful field after collaborating with data science. With the increase in data availability, businesses are hiring data scientists to handle the data and ease the decision-making procedure. Data scientists are supposed to inspect and evaluate the nature of data, understand the condition and type of the problem, and then use the correct algorithm to solve it.

Predictions are always risky decisions to make. With it, many possibilities are predicted by putting the available data together through statistics and logics of data science. The firm can consider all the probabilities and make a suitable decision, thereby decreasing their chances of risk and damage. Statistical analysis is another term used for this process of detecting possibilities. Become a Data Scientist with 360DigiTMG Data Science course in Pune. Get trained by the alumni from IIT, IIM, and ISB.

Recruiting and training

Recruiting staff is always a tiring and lengthy process, but data science helps in easing the process. Companies can use screenings through the employees’ resumes and applications. The firm can filter the candidates by selecting the candidates who have the most required qualities for the company. With image recognition, converting the visual information on the resume into digital data will be possible.

It is also necessary to keep the working staff up-to-date and informed. Targeting every employee and analyzing their strengths and weaknesses will be effortless with it. Training the employees on their flaws and teaching them new cutting-edge technologies would be an asset to the company. Want to learn more about data science? Enroll in the Data Science Training in Chennai to do so.

Conclusion

Data is a huge asset where you can invest your time and money. It is also worth the investment. From protecting your company to knowing your customers personally, from building new innovative products to recognizing when and where to publicize the product, the data helps in every aspect.

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UNDERSTANDING COVID-19 WITH DATA SCIENCE

UNDERSTANDING COVID-19 WITH DATA SCIENCE

by Data Science

When we discuss COVID-19 with data science, the first thing that comes to mind is the role of health workers who lead from the front. Data scientists are working from home and not contributing to helping humanity out of this crisis during such times. But is this statement true? To answer this, we must know that wherever there is data, we need data scientists.

DATA AND COVID-19

We might or might not sense it, but data is everywhere. Everything we see, search, shop, or buy is a part of a large dataset. This dataset is being used to analyze various trends and behaviors. The COVID-19 pandemic is generating lots and lots of data. This data is being gathered and analyzed to understand its spread and develop a preventive mechanism or even a permanent solution. Also, this data is being used to study the entire pandemic’s lifecycle.  Looking forward to becoming a Data Scientist? Check out the Data Science Course with Placement and get certified today.

DETECTION

Many undergoing projects use Artificial Intelligence (AI), using deep learning and machine learning algorithms to analyze big data collected from thousands of sources to specialize in Infectious disease surveillance. Currently, there is no standard method for detecting COVID-19. Though CT is being used for the same purpose, it gives a similar result for COVID-19 and other kinds of respiratory diseases. But researchers do believe that AI can be used to upgrade chest imaging to give a much more clean and more reliable output to differentiate between the symptoms of COVID-19 and pneumonia. Also, check this Data Science Training and Placement in Bangalore to start a career in Data Science.

SPREAD

Along with detection, data analytics can also be used to study the spread of diseases. For example, BlueDot, a Toronto start-up, uses airline ticketing data to predict the spread from one city to another. In India, Arogya Setu mobile application uses location statistics to analyze the spread. It gives a warning to the user when they come in the range of an infected person. It also helps the local health authorities to detect the location of all the people at high risk.  Become a Data Scientist with 360DigiTMG Data Science Colleges in Pune. Get trained by the alumni from IIT, IIM, and ISB.

MANAGING THE SITUATION

After the outbreak, the public health authorities can use AI-driven applications to control the pandemic. As advised by every national and international health organization, social distancing is the most effective way to break the chain, but let’s not forget that human beings are social beings. Maintaining distance is hard in populated countries like India. These days, thermal and face detection security cameras are installed in public places to warn whenever there is less than a 6-foot distance between people. Robots tend to patients when there are high chances of getting infected. Drones are used in containment zones to supply daily necessities such as groceries, toiletries, masks, etc. Want to learn more about data science? Enroll in the Best Data Science Training in Hyderabad to do so.

RECOVERY

The last and most important step in recovery. Recovery is not only required for the pandemic but also for economics. Both medical and economic recovery is based on data science. For medical recovery, a vaccine is required that can be made only when we have an adequate amount of data about the disease. Data about the disease includes its symptoms, what age group it has affected the most, the recovery rate, the fatality rate, the recovery time, etc. For economic recovery, information is required to understand what class of society was affected the most, how many people were affected, etc. Data science can help the decision-making body take the necessary actions.

CONCLUSION

Data science plays a key role in understanding and managing the situation during the COVID-19 pandemic. Though the situation came as a surprise, we have handled it with much more ease than any other past pandemic with the help of data science. 

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Components Of Data Science Field

Important Areas And Components Of Data Science Field

by Data Science

When it comes to data scientists, half the tech professionals are eager to learn it. Why shouldn’t they be excited? After all, a data scientist is regarded as the sexiest job title of current times. Some say that data science is all about playing with data, turning them into understandable numbers and graphs. But, it is not that easy when one goes into detail. The data science field has various components, which makes it more complex. These components complete data science as a field of study. They need to work together to make a data science project a success. Here are areas of data science that every aspiring data scientist need to focus on:

 

Database management

Without data, there cannot be any data science. As the data and its structure are changing rapidly, the way to collect them, store them, and maintain them should keep updating as well. This is exactly what makes database management imperative to every business. Database management is useful in marinating data and ensuring that the data experts get the required data easily, without much hassle. Are you looking to become a Data Scientist? Go through 360DigiTMG’s PG Diploma in Data Science and Artificial Intelligence!

Data mining

Data mining is all about finding patterns hidden in the data. These patterns will help the business to make strategic decisions and understand the market better. Under this, the data is mined and given structures that will help in finding meaningful insights. These meaningful insights can be used to make decisions, curb risk and solve various problems. Also, check these Data Science Course fees in Chennai to start a career in Data Science

Machine learning

This is another component of data science, which is helping largely in predictive analytics. In machine learning, the algorithms are trained using past data. Now, these algorithms have become useful in predicting the outcomes of various scenarios. Thus one can say that machine learning has become a major part of data science and data analytics.

Data visualization

Simply finding insights from the data structures is never enough. Those who are not data scientists are simply unaware of how to understand these insights. Therefore, to make the data more understandable, data visualizations are used. This makes it easier to communicate the insights to the higher management.

Deep learning

Deep learning is another area that falls in the data science field. Deep learning is all about creating predictive models and is highly useful in predictive analytics. Neural networks are created, which work like the human brain. Data is fed into the neural network. Which then uses the same to predict the results of different situations. Become a Data Scientist with 360DigiTMG Data Science Training in Bangalore. Get trained by the alumni from IIT, IIM, and ISB.

Business intelligence

Data science is capable of helping management to make decisions. Using business intelligence tools, the management can track trends and recognize patterns in the business’s data. This can help in making better decisions with more informed knowledge of the industry and market.

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Cloud computing

Lastly, cloud computing stands to be one of the most required parts of data science these days. As the amount of data collected and used by businesses increases, the infrastructure required to handle such amounts is becoming ineffective. This is where cloud computing comes in. Using cloud applications; businesses can store and manage data easily without investing in heavy infrastructure.

As data science is prevalent in all industries, the number of job opportunities is growing rapidly. Even today, there is a significant gap between the demand and supply of manpower in data science. This is the right time to be part of the Data Science Training to get complete theoretical and practical knowledge and industrial exposure for a brighter future. 

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