'Data scientists' term total reminiscent of a lone genius working alone, the esoteric formula applied to a large number of data to explore the useful insights. But this is only one step in the process of data analysis. Data analysis is not a goal in itself, the goal is to Air Max 2012 Black Red White New Nike Free Run 3 Shoes Women Navy Womens enable companies to make better decisions. Scientists build data products, it must be such that everyone in the organization to better use of data, so that each department, each level can make data-driven decisions. In the automatic collection, cleaning and analyzing data products, you can capture the data value chain, to provide information and forecasts for the implementation of a dashboard or report. With the emergence of new data and analysis can be automatically and Air Max 2012 Black Red White continuously run. Data scientists can continually improve the model according to the business, improve prediction accuracy. 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If nothing can be changed, no matter how much New Nike Free 5.0 V4 Shoes Black Red data collection and analysis can not have progress. In the project as early as possible to determine the goals, targets and operational instruments can indicate the direction for the project, to avoid meaningless data analysis. For example, the goal is to increase the degree of customer retention, which can be an indicator of the percentage of clients to update their subscriptions, business tools can be updated page design, timing and content of e-mail reminders and special promotions. 3. Data Collection: Data collection to try to cast a wide net. More data - especially data more different sources - enables data scientists to find a better correlation between the Nike Air Max data, to build a better model, to find more feasible ideas. Big Data economy means personal records are often useless, with each record available for analysis in order to provide real value. The company's website through their tests to closely track the user clicks and mouse movements, store the product by attaching RFID to track user's movement, coach athletes through additional sensors to track their course of action. 4. Data cleaning: data analysis is the first step to improve data quality. Data scientists to correct spelling mistakes, dealing with missing data and remove meaningless information. This is the data value chain is the most critical step. Junk data, even through the best analysis, also will produce incorrect results and misleading business itself. More than one company was surprised to find that a large part of their customers live in New Nike Tr Fit Running Shoes Black Blue Schenectady, New York, and the town's population of less than 70,000 people. However, Schenectady ZIP code is 12345, as customers are often reluctant to fill their real information in online forms, so the zip code will appear disproportionately in almost every customer's archive database. Direct analysis of these data will lead to wrong conclusions, unless the data analyst to take measures to validate and cleanse data. Of particular importance is that this step will scale performed because continuous data value chain requires incoming data will immediately be cleaned, and cleaning frequency is very high. This usually means that this procedure will be performed automatically, but that does not mean people can not participate. 5. Data Modeling: Data scientists build models related Womens Nike Kobe VIII data and business results, make recommendations on operational means to determine changes in the first step. Data scientists unique expertise is the key to business success, reflected in this step - related data, modeling, forecasting business results. Authentic Mens Basketball Shoes All Red Nike KD 7 Factory Outlet Data scientists must have a good background in statistics and machine learning to build a scientific, accurate models, avoiding the pitfalls meaningless correlation, and some models. These models rely on the available data, but for the future forecast is useless. But only statistical background is not enough data scientists also need a good understanding of the business, so that they can judge whether the results of the mathematical model makes sense, and whether it is relevant. 6. Training a data science team: data scientists are notoriously difficult to employ names, so it is best to build their own data science team, so the team who have advanced degrees in statistical terms people focused on data modeling and prediction, and Other people - qualified infrastructure engineers, software developers and ETL experts - to build the necessary infrastructure for data collection, data pipeline and data products, so that the resulting data can be output from the model, and the form of reports and forms in business the display. These teams usually use a similar large-scale Hadoop data analysis platform automated data collection and analysis, and operation of the entire process as a product. 7. optimization and repeated: data value chain is a repeatable process, capable of business and data value chain to generate a continuous improvement itself. Based on results of the model, the business will make changes according to the driving means, data science team will evaluate the results. Based on the results, the companies can decide on the next plan, and data science team continued data collection, data cleaning and data modeling. Repeat this process business faster development direction will be corrected sooner, the sooner to get the data value. Ideally, after several iterations, the model will produce accurate forecasts, the business will achieve the desired goal, the Womens Nike Kobe VIII resulting data will be used to monitor the value chain and reporting, while everyone in the team will begin to address the next business challenge.data analysis 7 key steps