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Introduction

Data science helps to study about data, which involves in developing methods to record, store, and analyze data to extract useful information effectively. The aim of data science is to gain meaningful insights and knowledge from both structured and unstructured data. The both structured and unstructured data are processed through analytical, programming, and business skills. Data is so important when it comes to business because it helps you improve and understand business process so, you can reduce wastage of money and time. Data science is a fastest growing industry over these years with scope of growing in the field of artificial intelligence and other areas. The Data Scientists are the highly paid professional in the IT industry. DJ Patil & Jeff hammerbacher are known as first data scientists in the year 2008. The key roles of data scientists are creating algorithms, testing, researching and building other tool.

The Process that a data scientists need to follow on everyday basis are:

  1. Business requirement
  2. Data Acquisition
  3. Data Preparation& cleaning
  4. Exploratory data Analysis
  5. Data Modelling
  6. Data Validation
  7. Deployment
  8. Optimization

Business requirements:

The first and foremost thing in data science is to analyze the business requirement or needs before getting the solution. The data scientists should know the exact problem that needs to get resolved also should know project thework with and what exactly the client want to get resolved. The two major tasks addressed in this stage

  • Define objectives: Brainstorm with all your stakeholders to understand and identify the business problems. Formulate questions that define the business goals that can be targeted by the data science methods.
  • Identify data sources:Find the relevant data that you need to answer the questions that define the project’s goals.

Data Acquisition:

Data scientific project starts with the identification of multiple data sources that could be–web server logs, social media information, online repository data such as US Census data sets, data transmitted from internet sources via APIs, web scraping or information that could be present in an excel or that could come from any other source. Data Acquisition includes the acquisition of data from all the inner and external sources recognized. The three major tasks addressed in this stage

  • Embed data into the analytical setting of the goal
  • Explore data to determine whether the quality of the data is sufficient to answer the query.
  • Set up a new or regularly refreshed data pipeline to score.

Data Preparation:

Data Preparation referred as data cleaning or data wrangling phase. The process’s aspect of data preparing is where most of your time will be. Cleaning the data can be more an art form than a science as you have to understand that if you have the right data to continue to a healthy model and know how to properly wash it so that it will not corrupt your model. There is an old saying, “garbage in, garbage out.” If you give bad data, your model won’t be very efficient.The five major tasks addressed in this stage

  • Discover is about finding the data best-suited for a specific purpose.
  • Detain is about collecting the data selected during discovery.
  • Distill is about refining the data collected during the detain phase of data preparation.
  • Document is about recording both business and technical metadata about discovered, detained and distilled data.
  • Deliver is about structuring distilled data into the format needed by the consuming process or user.

Data Modelling:

This is the key activity of a data science project requiring that the programs be written, run and refined to analyze and derive significant company ideas from data.This is where statistics and data analysis come in to generate a model that suits the data best. In order to find one with the greatest fit, you may need to attempt several models. To do this, it is often possible to go back to how the data was prepared. There are more methods to clean data that are lacking. Is the removal of the rows secure? Can we place in an average for it? There might even be a Better value for the missing ones, depending on the business. All of these can contribute to a much better model. The three major tasks addressed in this stage

  • Feature engineering: To promote model instruction, create information characteristics from the raw information.
  • Model training: Find the model that most correctly answers the query by comparing their metrics of success.
  • Determine the suitability of your model for production.

Data Validation:

Data validation is probably one of the most important techniques used by a data scientist, since there is always a need to validate the stability of the machine learning model-how well it would generalize to new data.The six major tasks addressed in this stage

  • Source system loop back verification
  • Ongoing source-to-source verification
  • Data-Issue tracking
  • Data certification
  • Statistics collection
  • Workflow management

Deployment:

This is where you share your information results. This is not restricted to a call API that utilizes your model. It could simply be documenting your results in an email, a shared document, or a presentation to a group of managers. While it’s simple to speak technically with your teammates, the key to this step is to relay what you discover in the information to a sales team or executives so that they can take action with them.The major task addressed in this stage

  • Operationalize the model: Deploy the model and pipeline to a manufacturing or production-like setting for application consumption

Optimization:

This is the final stage of any data science project that includes retraining the machine learning model in manufacturing whenever new sources of data come in or take the required measures to keep up with the results of the machine learning model.

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