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Tag: Best Data Science Courses with placement in India

Techtrek In 2022: Here’s How Data Scientists Successfully Adopt The Data-Driven Culture

Posted on by Imarticus Learning

Techtrek In 2022: Here’s How Data Scientists Successfully Adopt The Data-Driven Culture

Given the enormous volumes of data created today, data science is an essential factor in many sectors and is one of the most debated issues in IT. Since data science has become increasingly popular, businesses have begun to use it to expand their operations and improve consumer happiness. 

The field of study known as data science works with enormous amounts of data using cutting-edge tools and methods to uncover hidden patterns, glean valuable information, and make business choices. Data science creates prediction models using sophisticated machine learning techniques. This post will explain how data scientists successfully adapt to the data-driven culture.

The Benefits of a Data-Driven Culture

Having a data-driven culture that propels the company forward has several benefits. 

Organizations are encouraged to use reliable data when making choices by a data-driven culture. For any organization, interpreting data and exercising critical thinking are essential. However, a data-driven culture aids businesses in determining when to and when not to make decisions based on data.

It is accomplished by continuously monitoring performance and plays a significant part in continuous improvement inside the organization. Additionally, it aids in the advancement of analytically-based business choices. Organizations may move away from utilizing data only to generate reports and measure their development with the aid of a data-driven culture. 

The Challenges of Implementing A Data-Driven Culture

Despite the increased investment, many businesses find it challenging to keep their data strategy moving forward. According to a recent report, 72% of significant companies have failed to establish a data-driven culture.

Although many organizations invest more and more resources in their data strategy, successful plan execution gets frequently hampered by large organizations’ sheer size and complexity.

It’s crucial to ensure that data and analytics are available in a tangible, intelligible manner and can immediately connect with the operations they perform and their choices to improve analytics adoption.

Teams that want to succeed should frequently allow end-users some liberty by putting the proper technologies in place to provide self-service analytics that considers local complexity.

Future developments in IoT, AI, big data analytics, blockchain, and quantum computing will be incredibly significant.

Explore a data science career with Imarticus Learning

Students may start in data science and machine learning with this certificate program. Students will master the fundamentals of data science and machine learning through this curriculum, developed in partnership with iHUB DivyaSampark @IIT Roorkee, and acquire the knowledge and abilities they need to apply these concepts to problems in the real world.

Course Benefits For Learners:

  • This five-month program, developed by renowned IIT faculty members, will instruct students in using Python to comprehend data mining and machine learning methodologies.
  • The data science certification course will be live via online sessions with India’s best educators.
  • With the help of this data science online training, students will be able to develop a solid foundation in data science.

Contact us through chat support, or drive to our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon. 

The Perfect Guide To Understanding The Data Science Career Path

Posted on by Imarticus Learning

The Perfect Guide To Understanding The Data Science Career Path

India is rising and shining brightly when it comes to adopting new and emerging technology. Almost every significant industry segment recruits data science specialists to help them gain meaningful insights from massive data. The analytics sector has seen considerable demand for highly qualified employees who understand business and technology. Organizations nowadays are always on the hunt for specialists who can cover this ever-growing talent gap.

What is data science?

To develop insights from data, data scientists mix probability, statistics, and machine learning with domain expertise. These insights range from forecasting outcomes and trends to displaying linkages and patterns.

Data science impacts practically everything we encounter, from tailored playlists to shopping recommendations to illness diagnoses. Data scientists are also pioneers in developing self-driving vehicles, chatbots, and our data-driven environment.

A Career Path of Data Science Professional

Many data scientists have difficulty locating data science profiles and determining whether their skills match the job description. Because this is a relatively new industry, most organizations are imaginative regarding titles and career routes. This is also since these titles have no apparent precedent. Data science careers are expanding rapidly, with numerous vacancies across all industries. 

How do you begin a career in data science?

    1. Get educated – A good data scientist must have a solid math and statistics foundation. Getting extra education is essential if you’re not strong in these areas.
    2. Start playing with data – Use publicly available data sets to practice your data wrangling, analysis, and visualization skills.
    3. Get involved in the community – The data science community is vibrant and supportive. There are many online forums where you can ask questions and get advice from experienced data scientists. Community involvement will help you learn and grow in your career.
    4. Find a mentor – A mentor can be a great asset in your data science journey. Find someone experienced in the field who can help guide you as you learn and grow in your career.
    5. Keep learning – As a data scientist, you must continuously learn new techniques and tools. Many excellent resources are available, such as online courses, books, and blog posts.

Explore a data science career with Imarticus Learning

Students may start in data science and machine learning with this certificate program. Students will master the fundamentals of data science and machine learning through this curriculum, developed in partnership with iHUB DivyaSampark @IIT Roorkee, and acquire the knowledge and abilities they need to apply these concepts to problems in the real world.

Course Benefits For Learners:

  • This five-month program, developed by renowned IIT faculty members, will instruct students in using Python to comprehend data mining and machine learning methodologies.
  • The data science course will be live via online sessions with India’s best educators.
  • With the help of this data science online training, students will be able to develop a solid foundation in data science.

5 NLP techniques every data scientist should know

Posted on by Imarticus Learning

Have you ever wanted to master NLP? If so, I have five techniques that will change your life! In the last few decades, computers able to understand and process natural language. As a result, many new applications can leverage this technology for more accurate processing of text data.

One of these is Natural Language Processing (NLP). NLP has become an essential part of our lives as it allows us to talk with machines in a way they understand. This blog post will discuss five NLP techniques every data scientist should know. 

1) Tokenization: 

  • A technique that breaks up sentences into individual words or word tokens. 
  • It is the first step in text processing as it gives us a way to deal with each word individually. 
  • Tokenization is either done by splitting up an input string into words or groups of the word. Depending on the application, you might choose one over the other. 
  • For example, splitting words would be the best approach to find new misspelled versions of a known word. 

2) Stemming: 

  • Stemming is a method that reduces words to their root. It allows us to deal with variations of a comment by using its root form instead. 
  • For example, “running,” “runs,” and “ran” would all be reduced to the stem word “run.” Stemming algorithms share the same purpose: to remove the grammatical additions of words to get their root form. 
  • It allows for automatic text simplification, which is essential when condensing the input data into a single searchable string.

3) Lemmatization: 

  • Lemmatization is a process that reduces inflected words to their base or dictionary form. 
  • For example, reduction of “walked,” “walking,” and “walk” to the root word walk.
  • Lemmatization is stemming done right. Stemming reduces words to their root forms, but it does not take into account morphological rules. On the other hand, Lemmatization builds up word knowledge, which allows for base or uninflected word matching.

4) Keywords Extraction: 

  • This process finds the most important words when applied to text, phrases, or sentences. 
  • Keywords extraction means finding essential words in a given sentence, and this gets done by using TF-IDF (Term Frequency-Inverse Document Frequency).

5) Sentimental Analysis: 

  • Sentiment analysis is a text mining technique that has applications in many fields. 
  • It can also be helpful when building chatbots as word sentiment can give us an idea of what the user is saying. 
  • Sentimental Analysis helps identify emotional, social, or opinionated aspects within written language.

Explore and Learn Data Science with Imarticus Learning

Our Data Science course details include Capstone Initiatives, real-world business projects, relevant case studies, and mentorship from industry leaders who matter to help students become experienced Data Scientists.

Some course USP:

  • This data science course in India aid the students in learning job-relevant skills.
  • Impress employers & showcase skills with the certification of data science endorsed by India’s most prestigious academic collaborations.
  • World-Class Academic Professors to learn from through live online sessions and discussions.

Contact us through the chat support system or visit Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon training centers.

How to Excel in Data Science?

Posted on by Imarticus Learning

Data science has been growing and has infiltrated everyday life, even if sometimes we are not aware of it. To excel in this discipline that is becoming so popular there are several things you can do, and you should know that the first of these is not necessarily to learn data science although it is of course on the list. It is very common that when you go to a website you will be recommended products that might also be of interest to you. 

Or when you search about something, the search engine completes the sentence for you or makes a suggestion. All this is driven by data science, but do you know what data science is? Do you understand what it means? Do you know where data science is applied? Understanding this is the first step to becoming an excellent data scientist followed of course by a good data science certification course. 

What Is Data Science?

Data science is first and foremost the discipline of making data useful. Above all data science has become a new approach to problem-solving and strategizing. Although the computing power of today’s computers and data centers is an element without which Data Science would not have much scope, we are dealing with a discipline where other areas of knowledge converge that cannot always be clearly defined.

Several subfields include mining large amounts of information, making decisions based on limited information, and using patterns to automate tasks. Each subfield encompasses a science or technology, and it is important to understand the differences, 

 

  • Analytics

 

Analytics allows for the analysis of all types of data in real-time, historical, unstructured, etc. Above all, it is the process of examining data sets to find trends, hidden patterns, correlations, and conclude the information extracted. It is now possible to analyze data and get answers almost immediately, which is not possible with traditional solutions.

 

  • Statistics

 

Statistics is mainly concerned with putting data in order and analyzing it to obtain predictions and forecasts about specific phenomena. It is made up of methods, procedures, and formulas that enable relevant conclusions to be drawn. Its main objective is to improve the understanding of information. 

 

  • Artificial Intelligence

 

Thanks to machine learning, artificial intelligence can process massive amounts of data, which we as humans could never do. It also refines models through algorithms and predictive analytics, allowing machines to perform activities that we can consider intelligent on their own.

In short, data science employs a variety of technologies and methods to process and analyze data. The important thing to become excellent at data science is to find a good data science certification course. By finding a course that fully exploits your capabilities and develops your skills you can become an excellent data scientist.

Why Study Data Science?

If you learn data science you will make you part of the changing world. You will develop skills in computer science, programming, statistics and learn how to analyze and use the information to solve problems and develop strategies. There are many sources of learning but not all of them will suit your needs and those of the market. Our data science courses are designed by industry experts so you will learn real-world applications to generate useful solutions.  

The program Post Graduate Program In Data Analytics & Machine Learning is designed for those looking to build their career in data science especially for recent graduates and early career professionals. The Data Science courses will go a long way to ensuring that you become an architect of your future. Seeing the increasing demand for the application of this discipline, it is logical to expect an increase in the demand for data science professionals.

Why Python for Data Science is Easy to Learn for Beginners?

Posted on by Imarticus Learning

Why Python for Data Science is Easy to Learn for Beginners?

Python is one of the most popular programming languages for data science. Python offers many advantages that make it easy for beginners, including its user-friendly syntax and powerful libraries. In this blog post, we’ll explore why Python is an excellent choice if you’re new to data science and want a language that’s both fun and effective for getting started.

Why is it easy to learn Python?

  1. Python’s Simple, Clear Syntax

If you’re new to programming or coming from a different language, learning how to code in Python can be straightforward. It has a clear syntax that is often more readable than other programming languages, making it easy to understand. Moreover, Python does not have any mandatory declarations or keywords, which means it doesn’t require a lot of boilerplate.

  1. Python’s Powerful Libraries for Data Science

Python has several libraries for data science, including NumPy, Pandas, and Scikit-Learn. These libraries make it easy to work with datasets, do scientific calculations, and build machine learning models. In addition to these libraries, Python has an extensive collection of modules for specific purposes. Many data science projects are built with libraries included in the standard distribution of Python, making it even easier to work with them.

  1. Community Support for Data Science in Python

The vast majority of courses on data science use Python as the teaching language. It means that if you’re new to data science, there are more resources available for learning python than for other languages.

Though many beginners find it easy to learn Python programming basics, data science often requires a deeper understanding of Python’s application in practice. For example, you need to know which algorithm will work best for the problem at hand with machine learning projects. Python has an active community of users who continually contribute to its success by contributing to open source projects and data science-related blog posts.

Is it possible to learn python training for beginners?

Absolutely! While working in Python requires more effort than in other languages, many online resources help you get started with data science in Python.

There are also plenty of books, courses, and tutorials available that will guide you through topics such as machine learning algorithms, visualization tools, and statistical concepts. As you can see, there are lots of reasons why Python is an excellent choice for beginners.

Learn Data Science with Imarticus Learning

A postgraduate program is for corporate experts to help students master real-world Data Science applications from the ground up and construct strong models to provide business insights and forecasts. The program is for graduates and early-career professionals (0-5 years) who want to grow their careers in Data Science, the most in-demand job skill. With this program’s job assurance guarantee, students may take a significant step forward in their careers.

Some course USP:

  • This Data science courses with placement assurance aid the students to learn job-relevant skills that prepare them for an exciting career.
  • Impress employers & showcase skills with a certification endorsed by India’s most prestigious academic collaborations.
  • World-Class Academic Professors to learn from through live online sessions and discussions. It will help students understand the 360-degree practical learning implementation with assignments.

Contact us through the live chat support system or schedule a visit to training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon.

Tools Data Scientists Use to Make Precise Predictions

Posted on by Imarticus Learning

It is no secret that the accuracy of predictions in the business world can make or break a company. Data scientists create these accurate predictions to help businesses understand what will happen and prepare for it. It’s not easy, but data science has many tools that can make this process easier. In this blog post, we’ll explore some of those tools and how they work!

Tools data scientists use to make precise predictions:

Predictive analytics algorithms help data scientists predict future events and behaviors by using existing data. These tools build mathematical models that capture the connection between demographics, location, time of day, etc., and measurements such as the number of web visits or revenue.

One type of algorithm is a decision tree, a set of rules used to classify things. For example, if the weather is sunny and warm, there’s an 80 percent chance it will be hot outside. Still, if the weather is rainy or cool, there’s only a 30 percent chance it will be hot outside. A data scientist can use this information to determine an appropriate temperature for an office during a particular weather pattern.

Another type of algorithm is a random forest based on the same idea as decision trees but performs better in some cases. Random forests use when data scientists want to make accurate predictions with many different variables. The randomized process behind the tool helps ensure that each prediction is different from the last one.

Artificial neural networks (ANNs) are machine learning algorithms inspired by the neurons in our brains. They let computers complete tasks like recognizing images, handwriting recognition, and other forms of pattern recognition that machines can use to make predictions.

Support vector machines (SVMs) are another machine learning algorithm. These designs are for computer vision, which is the science of how computers can detect, receive, and process images. In a support vector machine model, there’s one variable being predicted from many different inputs. The goal of SVMs is to find a hyperplane that best separates the input data into two distinct sets.

Decision trees, random forests, ANNs, and SVMs are examples of algorithms that can make accurate predictions. These tools work well with large datasets; however, they require careful preparation and data feeding (known as “feature engineering”).

Explore and learn Data Science with Imarticus Learning

Learn the fundamentals of data analytics and machine learning and the most in-demand data science tools and methods to become job-ready. Learn Python, SQL, Data Analytics, Machine Learning, and Data Visualization using Tableau. This PG program is for industry professionals to help students master real-world Data Science applications from the ground up. Therefore construct strong models to provide meaningful business insights and forecasts.

Some course USP:

  • Data science courses in India aid the students in learning job-relevant skills that prepare them for an exciting data scientist career.
  • Impress employers & showcase skills with a certification endorsed by India’s most prestigious academic collaborations.
  • World-Class Academic Professors to learn from through live online sessions and discussions. It will help students understand the 360-degree practical learning implementation with assignments.

Contact us through the live chat support system or schedule a visit to Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon training centers.

How Data Scientists Make Data-Driven Decisions using Logistic Regression

Posted on by Imarticus Learning

A data scientist is a person who uses statistics and information technology to analyze data, identify patterns, and generate insights. They use sophisticated algorithms for these purposes. This blog post will cover logistic regression and how to apply it to your business problems effectively!

What is Logistic Regression?

It is a machine learning algorithm that means that the results are learned from a training set and used to make valuable predictions about unseen data. In the case of regression, those predictions are probabilities.

Some Challenges of using Logistic Regression?

The regression and other algorithms can be challenging to interpret and may only provide a probability between 0 and 1.

For instance, if we feed in a set of data for people who have been diagnosed with cancer, the algorithm will learn which variables are most important for predicting that diagnosis.

However, it will give us an output representing the probability that a patient has cancer. This number does not necessarily mean that the person has or doesn’t have cancer — it is simply the probability we can use to make an informed decision.

How is data science used to make a data-driven decision?

One of the most significant impacts data science has today can be seen in its use as a tool for business decision-making.

Predictive modeling and regression are two popular techniques that many companies have adopted across all industries because they empower businesses to make more accurate decisions, resulting in greater efficiency.

Logistic does this by taking historical data and learning which variables are most helpful in making predictions.

The future of Data Science?

  • The future of data science lies in developing new techniques that can build on these existing methods while overcoming their limitations.
  • It also depends on working effectively with vast volumes of data from various sources, such as sensors, images, and video.
  • One of the most talented research areas is the development of techniques that can learn on their own – without being fed historical data to train them first.

Explore Data Science with Imarticus Learning

Corporate experts gave input for this postgraduate program to help students master actual Data Science applications. Be it grounding up or constructing solid models to provide meaningful business insights and forecasts. This course helps you learn all. The program is for early-career workers (0-5 years) who dream of growing and building data science careers.

With this program’s employment guarantee, the student may take a significant step forward in their career. After satisfactorily finishing the program, students assure interview chances.

Some course USP:

  • Data science courses in India aid the students in learning job-relevant skills that prepare them for an exciting data scientist career.
  • Impress employers & showcase skills with a certification endorsed by India’s most prestigious academic collaborations.
  • World-Class Academic Professors to learn from through live online sessions and discussions.
  • The program helps students understand the 360-degree practical learning implementation with assignments.

Contact us through the live chat support system or schedule a visit to Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon training centers.

Why Linear Regression is Important for Data Scientists & How to Learn It?

Posted on by Imarticus Learning

Linear regression is a powerful predictive modeling technique that enables the statistical analysis of continuous variables. It is the most popular technique for estimating relationships between inputs and outputs.

This post discusses linear regression, how to use it in data science, and why you need to know about it as a professional data scientist.  Now let’s dive into the topic!

What is Linear Regression?

We start this section by defining linear regression. Here, in simple words, it is an approach to estimate the relationship between the input and output. It simplifies the modeling process and produces more interpretable results. When you need to make predictions on new data, Linear discriminant analysis is a better option for making predictions on new data points (i.e., test set) because of its solid statistical foundation and mathematical proofs of performance guarantees.

Why is Linear Regression Essential for Data science?

For a Data Scientist, it is essential to know and understand the concept of linear regression and how to use it. This section provides some reasons why it is critical for data scientists:

When you don’t know which variables are important: In many real-world problems, no one tells you which input variable(s) affect the output variable. In cases where you have access to historical data, it is possible to find the relationship(s) between input and output variables (i.e., linear regression).

When your model needs linearity assumption: Incorporating nonlinearities in the prediction function requires complex modeling techniques like applying polynomial transformations or neural networks.

How can we use linear regression?

Here are some common scenarios where we use in the industry.

  • You can predict the price of a house/cars/robots etc., indicating loan eligibility for an individual based on his salary. How many items will you sell tomorrow? What time of the day am I likely to buy something?
    Estimating Expected Weight of a baby based on mother’s weight during pregnancy, Estimating the passengers who will purchase tickets for an airline, etc.
  • Now you can solve all these real-world problems with linear regression!
  • Linear regression is a beautiful yet straightforward statistical technique to estimate the relationship between input and output variables. In other words, it helps you to find a function that best explains the relationship between input and output variables.

Input features = house size, car speed, age of a person, flight duration, etc

Output variable = price of a house/car/flight ticket etc

Explore Data Science career with Imarticus Learning

Students can master the fundamentals of data analytics and machine learning and the most in-demand data science tools and methodologies. With Tableau, you can learn Python, SQL, Data Analytics, Machine Learning, and Data Visualization. With this program’s job assurance guarantee, students may take a significant step forward in their career.

Some course USP:

  • This Data science courses with placement assurance aid the students to learn job-relevant skills that prepare them for an exciting career.
  • Impress employers & showcase skills with a certification endorsed by India’s most prestigious academic collaborations.
  • World-Class Academic Professors to learn from through live online sessions and discussions. It will help students understand the 360-degree practical learning implementation with assignments.

Contact us through the live chat support system or schedule a visit to training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon.

Understanding Linear Discriminant Analysis in Python for Data Science

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When we are working with more than two classes in data, LDA or Linear Discriminant Analysis is the best classification technique we can use. This model provides very important benefits to data mining, data retrieval, analytics, and Data Science in general such as the reduction of variables in a multi-dimensional dataset.

This is very useful for minimizing the variance between the means of the classes while maximizing the distances between the same. LDA removes excess variables while retaining most of the necessary data. This is extremely crucial for Applied Machine learning and various Data Science applications such as complex predictive systems.

What is Linear Discriminant Analysis?

LDA is a linear classification technique that allows us to fundamentally reduce the dimensions inside a dataset while also retaining most of the crucial data and utilizing important information from each of the classes. Multi-dimensional data contains multiple features that have a correlation with other features. Using dimensionality reduction, one can easily plot multidimensional data into two or three dimensions.

This also helps make data more cognizable for non-technical team members while still being highly informative (with more relevant details). LDA estimates the probabilities of new sets of inputs belonging to each class and then makes predictions accordingly.

Classes with the highest probability of having new sets of inputs are identified as the output class for making these predictions. The LDA model uses Bayes Theorem for estimating these probabilities from classes and data belonging to these classes.

LDA allows unnecessary features that are “dependent”, to be removed from the dataset when converting the dataset and reducing its dimensions. LDA is also very closely related to regression analysis and analysis of variance. This is due to all of their core objectives of trying to express individual dependent variables as linear combinations of other measurements or features.

However, Linear Discriminant Analysis uses a categorical dependent variable and continuous independent variables. Unlike different regression methods and other classification methods, LDA assumes that independent variables are distributed normally. For example, logistic regression is only useful when working with classification problems that have two classes.

How is LDA used in Python?

Using LDA is quite easy, it uses statistical properties that are predicted from the given data using various distribution methods such as multivariate Gaussian (when there are multiple variables). Then these statistical properties are used by the LDA model for making predictions. In order to effectively use the LDA model or to use Python for Data Science, one must first employ various libraries such as pandas, matplotlib, and numpy.

First, you must import a dataset such as the ones available in the UCI Machine Learning repository. You can also use scikit-learn to import a library more easily. Then, a data frame must be created that contains both the classes and the features.

Once that is done, the LDA model can be put into action, which will compute and calculate within the classes and class scatter matrices. Then, new matrixes will be created and new features will be collected. This is how a successful LDA model can be run in Python to obtain LDA components.

Conclusion

Linear Discriminant Analysis is one of the most simple and effective methods for classification and due to it being so preferred, there were many variations such as Quadratic Discriminant Analysis, Flexible Discriminant Analysis, Regularized Discriminant Analysis, and Multiple Discriminant Analysis. However, these are all known as LDA now. In order to learn Python for Data Science, a reputed PG Analytics program is recommended.

A Complete Guide to Data Science, Artificial Intelligence and Machine Learning

Posted on by Imarticus Learning

Data science often referred to as the ‘oil of the 21st century can be simply defined as the subject dealing with the collection, storage, analysis, deployment, and prediction of data. It collects the clean information from the raw data of the user and uses it for actionable insights. It is also used in predicting certain events in the future. Scientists define it as another form of statistics and YES! IT IS.:-

best data science courses in India

Data science vs AI vs Machine Learning

Data science obviously has the upper hand when compared with artificial intelligence and machine learning. Indeed machine learning and AI is a subset of data science.

After all data science, machine learning, and AI are associated with each other to build the technology.

 By 2013, the total data created was 2.7 zettabytes which 9x times more than it was collected in the previous 92,000 years of humankind combined. And is 90% of entire world data has been created in just 2 years. YEP! That’s amazing.

And it is still growing at a rapid pace. By 2020, the total data created was 44 zettabytes and it is projected to a rise of 175 zettabytes at the dawn of 2025.

Processes in Data science:-

  1. Understanding Business problem
  2. Data Acquisition
  3. Data preparation
  4. Exploratory data analysis
  5. Data modeling
  6. Visualization and communication
  7. Deploy and maintenance

Potential of data science:-

The power of data science is beyond our vision. We use it in our day-to-day life. It made our lives easier. Data science is being currently being used by many companies like Google, Instagram, Apple, etc. Whatever we browse, we watch is everything monitored from second to second.

Some of these determine its potential:-

  1. Genomic data provides a deeper understanding of genetic issues.
  2. Logistics companies like DHL and FedEx have discovered the best time and route to the ship.
  3. Used to predict the employee artition and understand the variables that influence employee turnover.
  4. Airline companies can now easily predict flight delays and notify passengers.

best data science courses with placement in India

Applications of Data science:-

 Data science plays a major role in many fields of the world like health, finance, Entertainment, cyber security, social networking, weather forecasting, etc.

  • Apps like Instagram Facebook YouTube collect the data from which we are interested and designs a user-friendly profile with recommendations popping up.
  • Data science is also used in detecting earthquakes’ location and magnitude by Seismograph.
  • Often used in cyber security and crime-related issues because data science has every single information of a person like his address, phone number, salary, what type of device he uses, etc.
  • Entertainment sites like Netflix and Prime video analyze the information from the videos which we have watched recently and creates our recommendations.

You might wonder which company has the most data. And the prize goes to google.

Because Google’s entire business is based on data science. Google uses apps like Google Maps to show us the best route in the traffic is an example often build by data science.

Companies like Apple supports the user’s privacy and does not allow the companies to go through our personal information using data science.

A tool like VPN helps in disguising or diverting our IP address from ISP and third parties.

Another segment to know under data science is hacking.

Hacking is done by hackers who are unauthorized users who break into one’s system and steal or destroy their personal information.

Hacking can be prevented by installing anti-software and keeping it up-to-date.

Another way of preventing hacking is setting up two-factor authentication.

Artificial intelligence like Siri, Alexa, etc are designed for user assistance and can be referred to as user-friendly software.

best data science courses with placement in IndiaFuture outlook:-

In the future foresight for sure, Dada signs will rise rapidly and will make our lives much easier with the better implementation of technology in the upcoming generations. For sure we can see the golden ages of artificial intelligence in the upcoming era.

We will be able to get the use of robots for better development. But how much ever it grows it must be always embedded in the limits because if it overtakes the human race it will be the end of Mankind. But it is difficult to equal the level of human intelligence.

Case Study:- Instagram algorithm

The main objective of the Instagram algorithm is to keep its users online for as much time as possible. Its algorithm works like popping the ads that users might be interested in. Now you might wonder how can Instagram know about its user’s interests.

Instagram algorithm stores the set of information of each user separately like how much time a user spends on a post or a real or what type of post he likes frequently or what type of ads the user visits.

So it analyzes from all these statistics and organizes the homepage and search engine of one account to hold them online for most of the time. It might seem surprising and tactical but at the end of the day, it’s all business.

The ones who are interested in data science is a very good field of the subject to opt for.

One can opt for data engineering at the graduation level. They would have a very good scope of becoming a data scientist or a data engineer. And The mean average salary is around $90,000 to 120,000 $.

And that’s it in today’s blog. Hope you had an informative day.

Hasta la vista.

Article Credit – 

“This blog was written and submitted by Ruthvik Rao, Hyderabad as a part of Imarticus National Blogging Contest. All views and opinions expressed within this article are the personal opinions of the author.

Disclaimer:

The facts and opinions appearing in the article do not reflect the views of Imarticus Learning and Imarticus Learning does not assume any responsibility or liability for the same.