HR Training Course Training in Delhi

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.

• 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.

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.

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.

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.

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.

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.

Choose the correct option:

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

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

[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 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.”

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.

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

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.

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!

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

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.

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.

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

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

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