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<p><a title=" Magellan Roadmate Update" href="http://magellanroadmateupdatee.com">http://magellanroadmateupdatee.com</a>Data science industry is growing exponentially, entering every possible industry and sector. Therefore, there is a huge demand for data scientists and analysts too in the market for different tasks that are to be performed in a data science cycle.<br />Everybody knows that data science is all about gathering data and finding meaning from it which can help a business in making quick and informed decisions. However, the entire process of data science is not that simple as it involves many components, tasks, roles, technologies, methodologies, etc.<br />Therefore, to understand this cycle, one needs to break the framework into small steps and then try to realize what skills are required and how each step helps in competing for a big data project.<br />Steps involved in the big data framework are discussed below.<br />1. Obtain data<br />Collecting data is the very first step that is to be taken by data scientists. Data can be collected from anywhere, it can be a business’s own data source or it can be from external sources. The most important skill required to collect and manage databases is MySQL. One needs to use programs like Python and R to find data from sources and feed them to big data programs. One can use web API’s to crawl data from social media websites or use Kaggle to download data files. This step also requires a data string especially if the amount of data is large by using Hadoop,<br />Spark, Flink, etc.<br />2. Clean data<br />If the data is unclean and filled with inconsistencies, then the end result will be improper and<br />inconsistent too. Therefore, it is important to scrub the data and filter out anomalies from it.<br />Scrubbing the data involves deleting extra values, filling missing values and replace the incorrect<br />ones. One also needs to consolidate the data files into a single repository so that working on<br />them becomes easier. Some of the very important programs and technologies that one needs to<br />make use of are SAS enterprise miner, Spark, etc.<br />3. Explore data<br />Examining the data before using the data for machine learning is very important, as it is crucial to find a data science question before searching for answers and solutions. Different data is explored differently to find variables and patterns from the data so that one can define a certain correlation among them. There are several programs that one need to be expert in like Python’s Matplotlib, Numpy, Scipy, etc. and R&#39;s GGplot2. One should also have a thorough knowledge of<br />statistical methodologies.<br />4. Model data<br />This is the stage where machine learning skills are utilized to use different algorithms according to the data sets. This step helps in decreasing the multidimensionality of the data and use only those which are needed. This helps in predicting models using predictive regressions and linear regressions. Overall, it will help in forecasting and predicting the end results of a decision. Skills<br />required are Python and R. Also, one should be well aware of MAE and RMSE.<br />5. Interpreting data<br />This step is crucial to make non-technical personnel understand the predicted results in simple terms. One needs to present the result in accordance with the questions that were initially asked so that the findings can be turned into actionable results. One needs to have both business acumen as well as good communication and presentation skills for this stage.<br />Resource box<br />Data science is not that simple as most of the definitions seem to depict it. However, it is not impossible to understand and become a skilled <a href="https://www.excelr.com/data-science-course-training-in-bangalore/">data </a>scientist. One needs to be trained and gather practical knowledge in this field which can be attained by the data science certification courses by Excelr.</p>
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