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Traditionally how business people and organizations use to generate business ? The simple answers that come across are hard work, how? By doing market research, visiting each retail shops in person, talking to them, asking about their requirements. And what was the status of people’s need was they use to take the questionaries’ personally to every door and make them understand their product or services? So clearly a lot of hard work one has to put in and all by human efforts. The current scenario is way dissimilar than this, there is so much information is available on the internet and x number of algorithms, tools, techniques, practices that have taken place and still evolving, all to generate accurate data and helping the world to be an immensely comfortable place to live. Today's post is about that. It's about the new, perhaps surprising ways that companies (and non-profits) are using machine learning to make smoother, faster, better products. 1.       Sealed mobil

Data is one of the greatest holdings any business has in present time. Data Science , data analytics, data mining, data engineering, etc., all work together on a single platform but perform very diverse and significant jobs in different scenarios. Many times people use these terms interchangeably but indeed there are huge differences among these models. A similar kind of uncertainty is there in the terms like- big data, data science and data analytics. Applicants often get confused and opt for different job role which does not match with their skillsets. Therefore, it is utmost important for you to know before moving ahead in a certain direction for better career. What is the Actuality? Big data and data science are not just some technical terminologies but are considerable theories contributing in variety of ways. While these terms are interlinked there is a structural difference between them. Big Data-   Big data is when we deal with mass data and of data of various type

Principal Component Analysis- Data Science Here we will talk about the simplified explanation of Principal Component Analysis , Which will be helpful to answers the basic confusions that data science aspirants go through while reading and those who are already doing well in PCA will recall the basics. The explanation is good for those also who don’t have a strong mathematical background. What Is Principal Component Analysis? PCA is a method to shorten or reducing the vastness of large data, it diminishes the outsized dataset into smaller chunks with loosing on much information as on large sets.   As we will understand that working with large data creates a lot of chaos, with PCA in Data Science small datasets can be made and important information can be saved. Reducing the number of variables from data will obviously cost little accuracy too, but the simplification is given little more preference there. The reason behind this practice is smaller datasets are easier to dea

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a part and parcel of Artificial Intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in a wide variety of applications, such as email filtering, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers.                       The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining i

Sentiment Analysis in Data Science Sentiment analysis is a concept of mining information and emotion from the text from different sources. Organizations take the help of sentiment analysis for taking the customer/user review and social sentiments about their brand, product or service while monitoring online conversations. On Social Media , we generally count the views and number of interactive text without getting a deeper understanding of high-value insight that is actually a point of concern. Behavioral economics and psychology show us that much of human decision-making is based in the world of emotion and cognitive bias, not logic," Peter observes. Companies crave to make use of that insightful information, so to determine the feeling of the user behind that expression. Sentiment Analysis of data science is designed and builds in such a way where the variety of elements can be provided to customers for experiences and form an opinion. Unlike many other aspects

What is Time Series Analysis-  It is a set of observation or data point s which are taken at a specified time period. It’s said that the most effective way of time series is to maintain the equal intervals of time to calculate the correct prediction. Business forecasting is a part of time series analysis, stock market works on the prediction model.   While determining the past experiences and scenarios one can invest in share and predict how things will turn up in the future. Also, a lot of retailers make bulk purchases on the basis of predicting the sales the will achieve in the forthcoming future. All this is part of business analysis, which is a part of almost all the domain irrelevant of their nature.   Other major terms are analyzing past behavior, future plans, and evaluation of current accomplishments. Past behavior is nothing but patterns that are being observed in the past, the season of sale, product preference, etc. all this comes under past behaviors. Future

  “R” only appears like a humorous name for a language until you realize that more than half the alphabet has been used up for one-letter programming language names. And when you learn that “R” is just an implementation of another language called “S”. R   is named partly after the first names of the first two R authors and partly as a play on the name of “S”.R is discovered by Ross Ihaka and Robert Gentleman and S stand for Statistical programming language. While we talk about Data Science there are few popular languages which are taught by every institute or training centers that are- Python and R programming language . These two are the default part of the Data Science Course and holds a big share. R has become the hot systematic programming tool of choice for data scientists in every industry from insurance to banking to marketing to pharmaceutical development etc. For data scientists , R bids a multitude of features making statistical analysis of large data sets sim

With the massive growth in  Data Science  and  Machine learning  there are two programming languages have emerged as the most favorable and suitable language for the data scientists, which is trying to help in their own different ways. Mostly the two are considered almost the same yet different let’s discuss how? R language  is best for statistician as it possesses an extensive catalog of the statistical and graphical method. Python is preferent for its simplicity and high performances, both are free to use and open sourced language and have been introduced to the world in the early 90s. Data Scientists and data analysts look forward to both the languages as they work pretty much the same but even then why some of them work with Python and some with R. R is a scripting language, with high flexibility with a vibrant resource bank, whereas Python is widely used object-oriented language, which is easy to learn and debug. Below are the comparison parameters between the two- Ease o