Data Science is the combination of mathematics, statistics, and software that applied in the business environment. Data Science is the blend of various algorithms and machine learning principles. Data Science is used for building predictive models, data analytics, data mining, and business analytics.

Data Science is the act of collecting, organizing, and understanding data for decision making. Data Science is a field that helps to extract the knowledge from structured and unstructured data. Data Science is used for maintenance, visualization, and extraction of data. R programming and Python are used for the implementation of Algorithms in Data Science.
The lifecycle of Data Science:

1. Discovery: The first step of the data science lifecycle is discovery. In this phase, the data scientists know business problems. The main concern is to collect the data that what are the problems that occurred.

2. Data Preparation: The second step of the data science lifecycle is data preparation. Data preparation is to analyses the data from capturing, extraction, and acquisition.

3. Planning of Model and buildings: The third step of the data science lifecycle is planning of models and buildings. This phase is for techniques and testing purposes. The common tools used for planning are R, SQL analysis, and SAS. The common building tools used as Weka, Mat lab, Statistic, and SPCS modeler.

4. Processing of data: The fourth step of the data science lifecycle is the processing of data. The processing of data is done through various processes like data mining, data summarization, clustering, and data modeling.

5. Analyzing of Data: The Fifth step of the data science lifecycle is analyzing data. The analysis of data is done through regression, qualitative analysis, predictive analysis, and exploratory.

6. Communicate: The last step of the data science lifecycle is communicating. The data is display in various places like Business Intelligence, data reporting, and data visualization. This final data is used for decision making.

Steps for Learning Data Science:

1. Knowledge of Statistics and Math: The main step is to the knowledge of statistics and math. It provides mathematical and statistical tools such as linear algebra, probability analysis, and sampling.

2. Programming knowledge: The other main step is to the knowledge of programming. It required programming knowledge such as Python, Artificial Intelligence, Business Intelligence, and Data Science.

3. Machine Learning: The other main step is basic and advanced knowledge of machine learning. Machine Learning is used various tools and techniques such as supervised learning, unsupervised learning, and reinforcement learning.

4. Understanding of data mining and visualization: The other main step is to understanding data mining and visualization. Data Mining and visualization is an important process for the exploration of data. This will help to make the decisions, cut costs, and increase the revenue.

5. Practical Experience: The other main step is to get practical experience. Data Science required theoretical as well as practical knowledge. Practical concepts will provide industry demonstration and actually solving the business problems with future perspective and predictions.

The demand for Data Science is growing rapidly in the IT sectors. The companies used data science for decision making and growth that help for better market share and leadership. Data Science is used in various sectors such as Healthcare, Finance, Transportation, Manufacturing, Banking, E-commerce, Automobiles, etc.
Data science Online training gives an understanding of how to build models that help in decision making.
Online Data Science Training is providing an understanding of the mining of structured and unstructured data that helps in productivity.

Data Science Online Course helps to add value to the operations of the organizations. Data Science Online Course helps to understand the mining of structured and unstructured data that helps in productivity, costs, identify new marketing opportunities, and competitive advantage in the organizations. It gives a deep understanding of data manipulation and data structure.

The job roles and opportunities after completing the data science online training is Data Scientists, Business Intelligence Analyst, Data Mining Engineer, Data Architect, Decision Scientists, Data Visualization Specialist, Statistician, Data Engineer, Business Intelligence Manager, etc.
Data Science is used in many companies such as Netflix, Porter, and Gamble, Amazon, Google, Facebook, etc.

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