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madrid software trainingsHybrid Mobile App Development Institute With Most Advanced Course Curriculum

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What is Hybrid Mobile App Development and Why it is in so much demand among companies?

You will agree that developing mobile apps depends on companies' needs and user requirements. While web-based and native apps have several advantages, hybrid mobile apps have gained traction. It is because of their flexibility across various platforms to develop applications that provide consistent performance regardless of the forum. Besides being cost-effective, hybrid apps make reserving a spot in the app store market slightly faster.

What is Hybrid Mobile App Development?

Hybrid app development is the method of developing a mobile app for multiple platforms, like Android and iOS, using a single code base. As a result, it drastically lowers development costs.

Hybrid app development adopts a method of writing the code once and running it everywhere to develop an app. It authorizes different plugins to enable direct access to the mobile device's features. Web techs, such as HTML5, CSS, and JavaScript, are the key to these apps while deploying a native container. Some famous examples of applications that surprise customers with their high performance, interface & functionality are Twitter, Instagram, Uber, Gmail, and others.

What is a Hybrid App?

A hybrid app is a software app that combines components of native and web apps. They are primarily web apps in a native app environment that connect with the mobile platform capabilities embedded in the app.

Why Should you Choose Hybrid Mobile App Development?

Hybrid apps utilize shared code to employ across multiple Android and iOS. It provides an ideal blend of native and web characteristics. It provides a lucrative opportunity for companies looking for an application that works on several devices to widen their business horizons. It lets you captivate customers no matter what platform they use. Whether you want an app to lure, be a game, or take photos, these apps offer many good options to leap into the mobility world.

Critical Advantages of Hybrid Apps So many companies go hybrid because hybrid mobile apps cost less than native apps. It takes less time to create hybrid apps, and it is easier to manage them.

Here are the five significant advantages of hybrid apps:

Easier To Scale on Another Platform

As hybrid apps use a single codebase, they can be employed across devices. For instance, when you develop them for Android, you can quickly launch them on iOS.

Only One Codebase to Manage

Unlike a native application, where you have to develop two apps, with a hybrid software building, you create only one app, so you only need to manage one database.

Faster Build Time

Since there is one database to manage, it takes less time to develop a hybrid app than a native one.

Low Development Cost

Hybrid app development cost is less than native apps. Because developers write one set of code, the initial costs and the maintenance costs are low. So, they are much more affordable than the native ones.

Offline Availability

Hybrid applications will work in an offline mode due to their native infrastructure. Users can still load the application and see the previously loaded data if they can't access real-time data.

Enhanced UX/UI

Hybrid applications combine the benefits of native and web apps and offer a continuous and flawless user experience across iOS and Android platforms. Besides, the lightweight hybrid application UI assists in loading content and graphics efficiently. The hybrid apps adapt to distinct device screens for easy data display and excellent data streaming. The impressive UI experience even improves the chances of the application's approval at the app store.

Wider Market Reach

They are perfect for targeting distinct platforms with one solution. In this competitive market, it becomes a golden and cost-effective option to reach out to a high number of users using other mobile platforms.

Easy to Maintain

Hybrid mobile app development offers simplified maintenance. For native apps, the app development teams need to roll out new versions for every platform with each update. A hybrid app removes versioning and makes app management as simple as updating a web page in real-time.

With many years of experience in hybrid app development training, Madrid Software is the best app development institute in Delhi, and it is the best institute for app development.

So, contact Madrid software if you are looking for a hybrid app development course in Delhi. The app development course fees are reasonable.

At Madrid Software, we believe in providing a full-fledged hybrid app development course in Delhi of your desire, where our industry experts have designed a top-notch curriculum just for you. Madrid Software is a smart choice for a Hybrid app development institute in Delhi.

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  •   Introduction To Data Science

    •   Python Concepts

    •   Analytics Concepts Of Python

    •   Numpy Package

    •   Introduction To Pandas

    •   Data Manipulation Using Pandas

    •   Pandas Package

    •   Data Munging With Pandas

    •   Data Visualization With Matplotlib

    •   Data Cleaning Techniques

    •   Predictive Modeling Concepts

    •   Machine Learning Concepts

    •   Statistics

    •   SQL

    •   Unsupervised Learning

    •   Supervised Learning

    •   principal component analysis

    •   Random Forest

    •   Support Vector Machine

    •   Tableau

    •   Case Studies

    •   Capstone Project

Case Studies



YouTube: Analyse and Predict Top Trending Videos for Each Category.
YouTube using Machine Learning based predictive modelling techniques to identify the top trending videos for a particular location based on the results achieved through analyzing the no. of likes, subscription and text mining the key words in user comments and no. of shares over internet.

Tesla Driver Less Cars: Artificial intelligence.
The current AI technologies in Tesla cars are based on unsupervised machine learning which impart decision making capabilities in driver less cars using chips and sensors. It aims to enable cars to navigate through freeways and even traffic on its own.

Zomato: Pick Best Restaurants of the City.
Zomato using predictive modelling machine learning techniques to identify the best resturant in metropolitan cities by analysing the key performance indicators like customer like, mapping positive feedback through text mining, user feedback ratings and type of cuisines served at the resturant.

Netflix: Machine Learning Project on Recommendation System .
Netflix Recommendation systems collect customer data and auto analyze this data to generate customized recommendations for the customers. These systems rely on both implicit and explicit data and based on the pattern present in the data the system provides recommendation to user.



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Data Science Interview Q & A


1.What is logistic regression in Data science?

Logistic regression measures the relationship between the dependent variable (our label of what we want to predict) and one or more independent variables (our features) by estimating probability using its underlying logistic function (sigmoid). Logistic Regression is also called as the logit model. It is a method to forecast the binary outcome from a linear combination of predictor variables.

2.Differentiate between univariate, bivariate, and multivariate analysis ?

  • Univariate - Univariate data contains only one variable. The purpose of the univariate analysis is to describe the data and find patterns that exist within it.

  • Bivariate - Bivariate data involves two different variables. The analysis of this type of data deals with causes and relationships and the analysis is done to determine the relationship between the two variables.

  • Multi-variate - Multivariate data involves three or more variables, it is categorized under multivariate. It is similar to a bivariate but contains more than one dependent variable.

3.How does data cleaning play a vital role in the analysis?

Dirty data often leads to the incorrect inside, which can damage the prospect of any organization. For example, if you want to run a targeted marketing campaign. However, our data incorrectly tell you that a specific product will be in-demand with your target audience; the campaign will fail.

4.What is the difference between supervised and unsupervised machine learning?

Supervised machine learning – It used unknown and labeled data. It has a feedback mechanism. The most commonly used supervised ML algorithms are decision trees, logistic regression, and support vector machines.

Unsupervised machine learning – It doesn’t require labeled data. Unlike supervised machine learning, it has no feedback mechanism. k-means clustering, hierarchical clustering, and apriori algorithm are the most commonly used unsupervised algorithms.

5.Explain the Decision Tree algorithm in detail?

A decision tree is a popular supervised machine learning algorithm. It is mainly used for Regression and Classification. It allows breaks down a dataset into smaller subsets. The decision tree can able to handle both categorical and numerical data.

6.What do you understand by the term recommender systems? Where are they used?

A subclass of information filtering systems that are meant to predict the preferences or ratings that a user would give to a product are recommender systems. Recommender systems are widely used in movies, news, research articles, products, social tags, music, etc. It helps you to predict the preferences or ratings which users likely to give to a product.

7.What is the p-value? What is its importance?

When you conduct a hypothesis test in statistics, a p-value allows you to determine the strength of your results. It is a numerical number between 0 and 1. Based on the value it will help you to denote the strength of the specific result.

p-value typically ≤ 0.05 shows strong evidence against the null hypothesis; so you reject the null hypothesis.

p-value typically > 0.05 shows weak evidence against the null hypothesis, so you accept the null hypothesis.

p-value at cutoff 0.05, this is considered to be marginal, meaning it could go either way.

8.We want to predict the probability of death from heart disease based on three risk factors: age, gender, and blood cholesterol level. What is the most appropriate algorithm for this case?

Choose the correct option:

  • Logistic Regression
  • Linear Regression
  • K-means clustering
  • Apriori algorithm

  • The most appropriate algorithm for this case is A, logistic regression.

9.Below are the eight actual values of the target variable in a train file. Find out the entropy of the target variable.

[0, 0, 0, 1, 1, 1, 1, 1]
Choose the correct answer.

  • -(5/8 log(5/8) + 3/8 log(3/8))
  • 5/8 log(5/8) + 3/8 log(3/8)
  • 3/8 log(5/8) + 5/8 log(3/8)
  • 5/8 log(3/8) – 3/8 log(5/8)

The target variable, in this case, is 1.
The formula for calculating the entropy is:
Putting p=5 and n=8, we get
Entropy = A = -(5/8 log(5/8) + 3/8 log(3/8))

10.Why do you want to be a data scientist?

The answer may vary from person to person. The aim is, to be honest, and polite. You may answer this like this. “I have a passion for working for data-driven, innovative companies. Your firm uses advanced technology to address everyday problems for consumers and businesses alike, which I admire. I also enjoy solving issues using an analytical approach and am passionate about incorporating technology into my work.”

FAQ


1. Who Should Do a Data Science Course?

Beginners and working professionals, both are eligible to do pg program in data science. To become a data scientist, you could earn a Bachelor's degree in Computer science, Social sciences, Physical sciences, and Statistics. You need to know programming languages like Python, Perl, C/C++, SQL, and Java.

2.What Are The Most Valuable Skill For a Data Science Professional ?

The most valuable skills for data science professionals are as follows:

  • Probability & Statistics
  • Multivariate Calculus & Linear Algebra
  • Programming, Packages, and Software
  • Data Wrangling
  • Database Management
  • Data Visualization
  • Machine Learning / Deep Learning
  • Cloud Computing
  • Microsoft Excel
  • DevOps

3. Is This Courses Useful For Non-Tt Professional

Any person with a structural thought process, good logical thinking skills, conviction towards learning new tools, and with a good business perspective can get into the field of data sciences. It’s not exceptional coders or highly knowledgeable people that are required.

4. What Is The Average salary Of a Data Scientist

The salary depends upon the company you are entering. The average data scientist’s salary is ₹698,412. As your experience and skills grow, your earnings rise dramatically as senior-level data scientists around more than ₹1,700,000 a year in India!

5. What Are The Top Algorithms That Every Data Science Professional Must Know

The top algorithms are:

  • Decision Tree
  • Logistic Regression
  • Linear Regression
  • SVM (Support Vector Machine) ...
  • Naive Bayes
  • KNN
  • K-Means Clustering
  • Random Forest
  • Dimensionality Reduction Algorithms
  • Neural Network

6. How Much Math In Statistics Is Used In Data Science

Math and Statistics for Data Science are essential because these disciples form the basic foundation of all the Machine Learning Algorithms. But, practical data science doesn't require very much math at all. It only requires skill in using the right tools. In statistics, you should know about probability distributions, statistical significance, hypothesis testing, and regression.

7. Which Programming Language Is Most Widely Used For Data Science

Python is the most popular and widely used data science programming language in the world today. It is an open-source, easy-to-use language that has been around. This general-purpose and vibrant language is innately object-oriented. It also ropes numerous paradigms, from functional to structured and procedural programming.

8.What Are The Top Companies In India To Work For After Completing Data Science Course

Many companies in India recruit Data Science professionals from entry-level to higher positions. Some of the top recruiters of Data Science and Big Data professionals in India for which you can work after completing the data science course are Equifax, Accenture, Amazon, Deloitte, LinkedIn, MuSigma, Flipkart, IBM, Citrix, Myntra, Juniper Network, etc

9. Do We Get Placement Support After Completing The Course

Yes, Madrid Software Trainings provide 100% placement support after the course and don’t throw at the deep end!

10.Do We Get Online Training Also In Data Science From Madrid Software Trainings

Yes, Madrid Software Trainings also provides online training for data science.

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