Steps Involved In A Data Project Cycle
Data science is something everyone knows about or at the very least have heard about. Every industry and many business firms are using data science to understand the market demands and give the best product and services to their customers. This is why the demand for best data science courses are on a rise, making it one of the most popular courses to seek.
But before taking up any course, one should be familiar with what goes on in that line. Data science is a complex field which involves many steps and processes all embedded together in a pipeline. Data science course in bangalore project cycle has 5 processes in it and it will be discussed further in brief below.
This is the most straight forward step which involves collecting data from different sources and processing them. Data can be obtained from any source, whether internal to the company or from external sources. Processing of the data involves tools like MySQL, Python and R. One will come across many types of databases by Oracle, PostgreSQL and can also find data from the internet.
The most common type of data is in the form of files which can be downloaded using Kaggle or from corporate data sources. All these data will be found in different formats that one will have to change into a format that Python or R can understand.
This step is all about cleaning the data and scrubbing out all the garbage from it as it can tamper with the end result. If the data is irrelevant, the outcome will be irrelevant too. Scrubbing and pruning of the data involve changing the format of the files into a standardized format, filter the data of the data which are collected from the data which are commonly called locked files, also one will have to replace wrong data and fill in the gaps. All this organize the data for further processing.
This step involves understanding the data and framing a question out of it which will be answered at the end of the data cycle. Here one will have to understand what the data is trying to say, and identify the patterns hidden in it which are also called data visualization. In this step, one will have to compute statistics to find out about different variables and their correlations. In this step, one will have to be well acquainted with statistics and tools like Numpy, Matplotlib, Scipy or GGplot2.
This is the stage that most of the data science aspirants have heard about, that is, machine learning. In this stage, the data will have to be modeled in a way that only the variables and features which are of use are used. Modeling will involve classification, differentiation, regression, data clustering using hierarchical clustering or k-means.
Interpret the data
This is the most important and crucial step, that is, to interpret the data models and communicate the results. One should be able to generalize the data models so that it can be understood by any person who does not have a technological background. One needs to give actionable insight about the data which can be later turned into a prescriptive analysis, and one can decide what is to be done and what to avoid in their decision making.
All these steps mentioned above is not as easy as they sound. They require training and practice with practical projects which will help in honing one’s skill. For this reason, one needs to find the best data science training in bangalore like ours which can help understand data science from its very core.
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