What is data science?

Data Science Course is an evolving technology in the twenty-first century and is the core of learning various other tools like machine learning, utilization of mathematics and statistics that process a huge amount of raw data to project a processed data. Data Science Training accumulated from various sources is being used for understanding the current trends and requirements in various industries like FMCG, pharmaceutical, entertainment and others. Data is considered to be the key player in fulfilling the requirement of companies involved in research and development, marketing and sales and purchases. In order to utilize and process the raw data, the need for data scientists have been growing at the fastest rate and this sector is estimated to boom in the recent year. The study of data science is itself vast in nature and therefore need of specialized mentoring is important.

Role of Data Scientist:

A data scientist role is to process the historic data and analyze the same to give an end result in the form of prediction and projected information that helps the organization to focus at the key area of development. Data analysts are the one who explores the available data and discovers the insights from it, also the analysts use various analytical tools like machine learning, Tableau, R, Python and various other software's to share the end result.

Types of predictions data analysts perform-

1. Predictive analytics -

This method is applied when one needs to predict the possibilities that will occur in the future. Analysts need to develop a model that predicts the future possibilities and provides a conclusion on the basis of which a company can make the decision accordingly.

2. Prescriptive analytics -

This method includes the use of artificial intelligence that helps the built model to analyze the situation and take the decision of its own. The process includes gathering of real-time data and processing the same to give an end result that helps to formulate a specific strategy. For example, the introduction of self-driving cars by Google will use this method to gather the data of nearby area with the help of road maps, surroundings, the road turns and street light information that will help the computers and other functions to operate in a specific condition.

3. Machine learning for pattern discovery -

This method uses the available raw data to process and makes the predictions based on different parameters. The technique uses various algorithm patterns and predefined labels available in the library to make the decision. The most commonly used pattern discovery technique being used is known as clustering.

Why Data Science to be used?

Currently, the data is mostly available in the unstructured form and need to be arranged in a structured manner in order to get the desired results. The job of a data analysts plays an important role in the structuring of data and sequencing them with the help of different tools like Tablue in order to form a structure and a model that helps further in the analysis of data.  As per the current trend of understanding the customer, the requirement has grown to a higher level and offering of precise product to the customer will help the organizations to sell their products with high accuracy. This is where the data science and the role of data scientist plays an important role.

Who is a data scientist

The term data scientist has been derived from two different words that are data and scientist, the data scientist is the one who visualizes and uses the facts and figures to form structured information from the field of science and other applications that includes mathematics and statistics.

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What does a data scientist do?

Data scientist processes the complex data and solves the problems that persist within an organization and a model. Data scientists are considered to be an expert in a certain field of science and are known to have expert knowledge in a certain domain. The role of a data scientist is to present a complex problem in a simple and useful manner, along with structuring of raw data into a simpler format.

The lifecycle of a Data Science

The data science lifecycle consists of six different phases that are the discovery phase, data preparation phase, model planning phase, model building phase, operational phase, and result phase.

 

Phase 1- Discovery Phase

The phase includes the understanding of the problems, available time frame, resources, technology, manpower, and budget. The complex problem is understood and a model is designed to get a required result. A hypothesis is considered and different tools are being considered that should be efficient to provide a result. 

 

Phase 2- Data Preparation Phase

The phase includes the gathering of data and formulating Data Science Training it in a structured form. This includes data mining and data cleaning with the help of statistical tools like “R”. Once the data is formed and stored, its time to use different variables and perform ETLT process that is extracted, transform, load and transform.

 

Phase 3- Model Planning Phase

In this stage, different methods and techniques are used to create a relationship between different variables.  Exploratory data analytics is applied using statistical tools like statistical formulae and visualization tools. The most common tools being used are R, SQL, and SAS.

 

Phase 4- Model Building Phase

Under this phase, a dataset is developed for testing purpose. This also includes an understanding of different tools being used and whether it will be sufficient to suffice the requirement of building a model. The tools used for model building are like WEKA, Matlab, Alpine Miner, Statistica, and others.

 

Phase 5- Operational Phase

The operational phase includes the delivery of the final Data Science Certification report that includes report briefing, code and technical documents. Even different projects are undertaken and models are run in order to get the desired results.

 

Phase 6- Result Phase

 This is an important stage and needs to be evaluated properly to find if the desired result has been obtained or not. Under this stage, key findings are identified and communicated to the stakeholders. The stage determines the success and failure of the model built.

Overall, the ratio of success has improved in the recent scenario and the success rate of models built has increased owing to the presence of helpful building tools and others statistical methods, however, the success behind the model lies in the hand of a data scientist who plays a critical role in the process.