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Master in Data Science Course Training in Delhi


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Master in Data Science Course in Delhi
  • Upgrade Your Skills For a High Paying Corporate Job
  • Learn From Industry Leaders
  • Develop Real World Projects
  • Work on Multiple Case Studies

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Upgrade Your Skill In Master in Data Science Course

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Master in Data Science Course in Delhi


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Master in Data Science Institute in Delhi
Master in Data Science Institute in Delhi

Master in Data Science Institute in Delhi India's No-1 Master in Data Science Course Institute With Most Advanced Course Curriculum

Data Science Masters Training in DelhiAcademic Partner - Hewlett Packard



What Is Master in Data Science Course & Why It Is The Most Demanding Skill Now a Days?

  • Master’s Program in Data Science is an interdisciplinary approach that is an amalgamation of the power of programming with python and R language along with machine learning and statistics to produce actionable business insights by working on the raw data. The job may sound easy but needs a lot of skill set to harness the data which may be in a crude form and not a structured form. It requires mastering of Data Science tools and techniques for a Data Scientist, along with an out of the box thinking along with to master the job.

Advantages of learning Master’s Program in Data Science

Now that you have a fair idea of what data science is, let’s look at some advantages of taking up data science as a career option’

  • Data science is the most demanded job. It holds a sea of opportunities for you in 2020 and for years to come.

  • Scarcity of data scientist makes data science full of open positions and less saturated as compared with other job sectors.

  • Data science is the most lucrative career option as suggested by Glasdoor, according to which Data Scientists average pay scale is $116,100 annually.

  • Data science is famous for its versatility in numerous applications across various sectors that provides you to work in your desired sector.

  • Data scientists hold an important position in any company due to their ability to make smart business decisions with their skills.

  • Data Science will help you master a problem-solving attitude that will benefit you in your personal growth and not just in your career

Choosing from the Best Master’s Program in Data Science Course in Delhi

By now if you are convinced that this is the right career option for you, it is important for you to take the next step that is to enrol into the pg program in data science course.

If you hail from Delhi and dream of Chandni Chowk to China or America with a high paid job in a reputed MNC, you need to look for a full-fledged Master’s Program in Data Science Course in Delhi. There are numerous institutes offering data science training that can leave you confused.

Consider a few checks before choosing the best institute for data science that can clear your confusions such as;

  • Placement Reviews
  • Matched Budget
  • Practical Sessions Provided
  • Experienced Mentors
  • Alumni Reviews
  • Testimonials

Considering all the checks mentioned above, you can carefully shortlist from the data science institute in Delhi or from anywhere. Just in case you are in Delhi, we highly recommend you Madrid Software Trainings that ticks off everything mentioned in the checklist above and offers you the best data science course in Delhi with an in-depth theoretical and practical knowledge of all the latest data science tools and techniques. Madrid Software Trainings offer you industry-based specialized curriculum to gain the right skillset considering the best practices of current industry and the future trends to keep you a step ahead of others in your career in this field.

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Course Outline

Best Master Data Science course in Delhi

Our Master in Data Science Course Is Designed By Industry Experts That Gives The Candidate an Edge In The Market



  •   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

    •   Unsupervised Learning

    •   Supervised Learning

    •   principal component analysis

    •   Random Forest

    •   Support Vector Machine

    •   Data Analytics with R

    •   Regression in R Language

    •   Case Studies

    •   Capstone Projects




Job Profile And Salaries Of Master in Data Science

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09-Dec-2024 		Weekend
07-Dec-2024
  • 100% Classroom Training by Our Top Ranked Faculty
  • Course Curriculum Design by Industry Experts
  • Real Time Assignments Case Study & Projects
  • Got Better Salary Hike and Promotion
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  • Mock tests and Mock interview
  • Dedicated placement coordinator assigned to every
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Data Science Certification course in Delhi



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Master in Data Science Course 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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Data Science Certification course in Delhi

Data Science Certification course in Delhi
Master in Data Science Course in delhi



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