This is informational bot application developed for education related to give information about data science. ##This is web based informational bot application has been developed for the accomplishment of future ready talent intership programme launchede by microsoft,futureskill prime,future ready talent,github.
##project link:https://sites.google.com/view/nikhila-chatbot
##project title: Datascience
##project description : I have created a informational "azure chat bot" using Html and Qn maker,web app bot.it helps the user to find information about datascience and its history and about services.IN this website i provide all information data science.It's look a professional bot trying to solve the real world problem like in these days we are searching for good and genuine website,apps etc.for information here and there .so i makea try to give every details of datascience.My project that helps both learners and explorers like for learners and I provide every detail about datascience and for all explorers i gave website link and everthing there in the bot to know more to create deploy etc. ##Datascience: Data science is the study of the extraction of knowledge from data. It uses various techniques from many fields, including signal processing, mathematics, probability, machine learning, computer programming, statistics, data engineering, pattern matching, and data visualization, with the goal of extracting useful knowledge from the data. With computer systems able to handle more data, big data is an important aspect of data science.
A person that does data science is called a data scientist. Data scientists solve complicated data problems using mathematics, statistics and computer science, although very good skill in these subjects are not required.[1] However, a data scientist is most likely to be an expert in only one or two of these disciplines, meaning that cross disciplinary teams can be a key component of data science.
Good data scientists are able to apply their skills to achieve many kinds of purposes. Their skills and competencies vary widely.
##The Data Science Lifecycle Now that you know what is data science, next up let us focus on the data science lifecycle. Data science’s lifecycle consists of five distinct stages, each with its own tasks:
Capture: Data Acquisition, Data Entry, Signal Reception, Data Extraction. This stage involves gathering raw structured and unstructured data. Maintain: Data Warehousing, Data Cleansing, Data Staging, Data Processing, Data Architecture. This stage covers taking the raw data and putting it in a form that can be used.
Process: Data Mining, Clustering/Classification, Data Modeling, Data Summarization. Data scientists take the prepared data and examine its patterns, ranges, and biases to determine how useful it will be in predictive analysis.
Analyze: Exploratory/Confirmatory, Predictive Analysis, Regression, Text Mining, Qualitative Analysis. Here is the real meat of the lifecycle. This stage involves performing the various analyses on the data. Communicate: Data Reporting, Data Visualization, Business Intelligence, Decision Making. In this final step, analysts prepare the analyses in easily readable forms such as charts, graphs, and reports.
##Prerequisites for Data Science: Here are some of the technical concepts you should know about before starting to learn what is data science.
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Machine Learning Machine learning is the backbone of data science. Data Scientists need to have a solid grasp of ML in addition to basic knowledge of statistics.
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Modeling Mathematical models enable you to make quick calculations and predictions based on what you already know about the data. Modeling is also a part of Machine Learning and involves identifying which algorithm is the most suitable to solve a given problem and how to train these models.
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Statistics Statistics are at the core of data science. A sturdy handle on statistics can help you extract more intelligence and obtain more meaningful results.
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Programming Some level of programming is required to execute a successful data science project. The most common programming languages are Python, and R. Python is especially popular because it’s easy to learn, and it supports multiple libraries for data science and ML.
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Databases A capable data scientist needs to understand how databases work, how to manage them, and how to extract data from them.
Use of Data Science Data science may detect patterns in seemingly unstructured or unconnected data, allowing conclusions and predictions to be made.
Tech businesses that acquire user data can utilise strategies to transform that data into valuable or profitable information.
Data Science has also made inroads into the transportation industry, such as with driverless cars. It is simple to lower the number of accidents with the use of driverless cars. For example, with driverless cars, training data is supplied to the algorithm, and the data is examined using data Science approaches, such as the speed limit on the highway, busy streets, etc.
Data Science applications provide a better level of therapeutic customisation through genetics and genomics research.
