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Evolution of Data

As the need for big and large-scale data increases day by day, We will need a large data storage. Therefore, the focus was on establishing a framework to store data.

What is Data Science?

Data science is a combination of various mechanisms and algorithms to uncover various patterns and models. It is about technical methods to collect information and knowledge from data of various forms, whether organized or unorganized.


Need for Data Science

We primarily use data Science to make decisions and predictions.

  • Builds intelligence and potentiality in machines.
  • Recommending the right product to the right consumer at the right time. This is the purpose of Collecting Data such as age, sex, location, preferences, and purchasing history. Gathering the right Data will make the organizations facilitate customer segmentation and help to draw a pattern.
  • Collects data from the environment through the sensors and make decisions, accordingly.
  • Speculates deceptive transactions before processing them.
  • Patterns built based on this method helps in increasing the convention rate.


The life cycle of Data Science


It involves procurement of raw data from all sources, whether internally or externally in relation to the business. We could collect data from social media, census datasets, web servers, etc.


Data Preparation

The need for pre-planning, Preprocessing and research is crucial in data. The required data could be irregular or unreliable as of incorrect data format, abrupt values, and blank values. This will help to establish a relationship between various variables.


Model Planning

Techniques, methods, and procedures are determined to establish a relationship between variables. We can apply exploratory Data Analytics (EDA) using statistical formulas and visualization techniques as histograms and line graphs to get a better view. Several tools used are R, SAS/ACCESS, etc.


Model Building

It develops datasets for experiments and training. We use various learning techniques like classification and corporation to build the model. Various tools like R, Python, WEKA, SPCS Modeler.



Final reports, analysis, code, and technical records will be a very crucial stage to measure the general performance.


Communicate Results

The important and key discoveries are communicated to the stakeholders, and they explain models to the medical authorities. It determines results, whether it is a success or a failure.



Data Scientist

Is a person who primarily studies are Mathematics, Information system, Business and Computer Science involving Statistical research, Data processing, and machine learning.

The data scientist will create methods for complex and large data sets for modeling and research work. Predictive models are built using machine learning algorithms, and the data is verified and processed for analysis.




Business Intelligence vs. Data Science

Often, Data Science is confused with Business Intelligence.

Business Intelligence tests the impact of certain events in the near future and analysis the previous data for insight. The data sources are structured using SQL and Data Warehouse, and the approach involves statistics and visualization, focusing on past and present.


Data Science answers to open-ended questions as what and how events occur. Data sources are both structured and unstructured, using logs, SQL, cloud data, etc. The approach used is Statistics, Machine learning, Graph analysis, and Programming (NPL) and the focus is on the present and future. Data science courses will teach you everything you need to know.


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