Data Science
First, let’s see what Data Science is?. Data Science is a combination of several tools, algorithms, and machine learning principles aiming to discover hidden patterns from the raw data. How is this different from what statisticians have been doing for years? Most of the data earlier was structured and are of very small in amount, which can be easily analyses or converted into final reports, by not putting too much efforts. Unlike the earlier pattern, today the information is much bulkier than any time before, because of so many new companies, startups and the evolution of data science. The information is way more and is mostly discovered in very different forms like- documents, files, images, text, videos, etc. Before reaching to the final level there are many short term levels which needs to be tackled and data structuring is one of them. Today we get unstructured data and this shout out for the need of Data Science or Data Scientists. This data is generated from different sources like financial logs, text files, multimedia forms, sensors, and instruments. Simple BI tools are not capable of processing this huge volume and variety of data. This is why we need more multifaceted and advanced analytical tools and algorithms for processing, analyzing and drawing meaningful insights out of it. Let’s talk about some more aspects of Data Science. It’s helpful when we recognize the clear-cut necessities of the customers from the existing data like- the browsing history, purchase history, age and income, geographical location, address etc. Undoubtedly we will be able to serve them with better and precise product, yes we had this data earlier too, but now with the vast amount and variety of data, you can train models more effectively and recommend the product to your customers with more precision. It will bring more business to your organization. In another scenario to understand the role of Data Science in decision making. Let’s suppose our car has suddenly become intelligent. How? The self-driving cars collect information from sensors, including radars, cameras and lasers to create a map of its surroundings. Based on this data, it takes judgements when to speed up, when to speed down, when to overhaul, where to take a turn – building use of progressive machine learning algorithms. Data Science can be used in predictive analytics. Here we can talk about weather forecasting. Intricate information from aircrafts, ships, satellites etc. can be together and analyzed to build models. These models will not only estimate the climate but also benefit in predicting the manifestation of any natural catastrophes. It will help to take appropriate precautionary measures beforehand and save many treasurable lives. Data Science has pushed its boundaries from the times, when people were not even aware of this industries and today it has proven the value this field is adding to almost all the sectors. Source Link- Data Science Course Data Science Certification Data Science Training Data Science Course in Pune Data science Certification in Pune.
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WHAT IS DATA FORECASTING?
Forecasting is a process of making predictions on future based on past and present data and most commonly by analysis of trends. Risk and Uncertainty are central to forecasting and prediction; it is generally considered good practice to indicate the degree of uncertainty attaching to forecasts. In any case, the data must be up to date in order for the forecast to be as accurate as possible. In some cases the data used to predict the variable of interest is itself forecasted. METHODS OF DATA FORECASTING. The appropriate forecasting methods depend largely on what data is available. If there is no data available, or if the data available are not relevant to the forecasts, then qualitative forecasting methods must be used. These methods are not purely guesswork—there are well-developed structured approaches to obtaining good forecasts without using historical data. Quantitative forecasting can be applied when two conditions are satisfied:
TIME SERIES OF DATA FORECASTING Time does play a key role in normal machine learning datasets. Predictions are made for new data when the actual outcome may not be known until some future date. The future is being predicted, but all prior observations are almost always treated equally. Perhaps with some very minor temporal dynamics to overcome the idea of “concept drift” such as only using the last year of observations rather than all data available. Data Forecasting is based on Time period. So, to analyze the data we need to have specific time intervals. Examples of time series data include:
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