What is Data Science

Data Science is the perfect combination of business, analytical, and programming skills which allows bringing out meaningful insights from unstructured and raw data. Data Science is a study that deals with representation, extraction, and identification of data resources useful information which can be used for various business purposes.

 

The requirement to bring out the useful insights is mandatory for the businesses to stand top among the crowd as numerous facts are getting generated each minute. Data Engineers set up the data storage and database to facilitate the process of data wrangling, data mining and other related operations.

What is Data Science?

Data Science is getting evolved as an in-demand and promising career path for professionals. Successful data scientist professionals understand today that they should have the traditional skills of data mining, analyzing an extensive collection of data and programming skills. Data scientists should master the complete data science life cycle spectrum and posses the flexibility to uncover their organization’s useful intelligence.

 

Data Science is used primarily to make predictions and decisions by using prescriptive Analytics, Predictive casual analytics, and Machine Learning. Let’s check them out in depth below.

 

Prescriptive Analytics: If you need a model that can alter the dynamic parameters and hold the intelligence of executing their own decision, then undoubtedly Prescriptive Analytics would be the right choice. It is one of the new fields, and it deals with providing advice. It suggests a prescribed range associated outcomes and specified actions. One accurate example of Prescriptive analytics is Google’s self-driving car.

 

Predictive Casual Analytics: If you need one model that is capable of predicting any future event possibilities, you can prefer to choose Predictive Casual Analytics.

 

Machine Learning – Making predictions: If you have the finance company’s transactional data and you need to develop a model to determine and predict the future trend, then you can choose Machine Learning algorithms. It falls under the supervised learning paradigm.

 

Machine Learning – Pattern Discovery: If you do not hold the parameters depending on which you are going to make the predictions, then it is essential to find out the hidden dataset patterns to make meaningful predictions. Clustering is one of the conventional algorithm used for pattern discovery.

Data Science vs. Data Analytics: What’s the Difference?

Data Science: An Overview

Data Science is the science and art of bringing out the actionable insights from unstructured data. We have already discussed data science in depth. Data science is something to choose when you are dealing with a large amount of data.

 

  • Mining an extensive collection of unstructured and structured data to recognize patterns.
  • It includes a combination of statistical, programming and machine learning algorithm skills.
  • It’s about uncovering the data findings using various tools, techniques, and process.
  • Data Scientists works based on the business needs, market requirements, etc.

Data Analytics: An Overview

Data Analytics is otherwise derived as data analysis and is just similar to data science but in a more concerte way. The primary purpose of data analytics is to generate data insights by connecting trends and patterns with enterprise goals. Comparing organization hypothesis against the data asset is the main use case of data analytics, and the practice should be focused on strategy and business.

 

  • Data Analytics deals less with Machine Learning, Artificial Intelligence, Predictive Modeling, etc.
  • Data Analytics makes use of the SQL and other basic query expressions to dice and slice data.
  • Wrangle data which are smaller or localized in footprint.
  • Data Analysts are not responsible for deploying machine learning tools or building statistical models.
  • Data Analysts does not have much freedom as data scientists, and they are less involved in the data work culture.

 

Factors Data Science Data Analytics
Primary Goal Asks perfect questions regarding the business and also finds the right solutions. Mining and Analysing your business data.
Data Volume Extensive range of data. A Limited collection of data.
Diverse Tasks Preparation analysis, data cleansing to acquire insights. Aggregating, Data Querying to discover a pattern
Substantial Expertise Required Not Mandatory
Non-Technical Required Not Mandatory
Focus Data are Pre-processed Data are processed.
Purpose Obtaining insights from raw or unstructured data. Acquiring insights from processed data.
Bandwidth Liberal and more freedom in Practice and Scope. Less freedom is available in Practice and scope.
Data Types Structured and Unstructured Data Structured Data.
Advantages Data scientists examine and explore data from different sources. Data Analyst explores data from a single source like CRM.

 

Although there is a vast difference in both Data Science and Data Analytics, both are essential parts for the future data and work. Data Analytics takes the right direction from the data scientists. Both Data Science Vs. Data Analytics need to be embraced by various organizations so that they can lead a successful way to bring a change in the technological facts and also can understand the data reliably.

Data Science Algorithms:

There are three distinctive algorithms used in Data Science namely Machine Learning Algorithms, Optimization algorithms for estimating parameters that include the Least Squares, Stochastic Gradient Descent, Newton’s Method.
Data Preparation, Process and Munging Algorithms. While considering the Machine Learning Algorithms, many algorithms can be used by data Scientists namely.

 

  • Linear Regression
  • Logistic Regression
  • Linear Discriminant Analysis
  • Classification and Regression Trees
  • Naive Bayes
  • K-Nearest Neighbors
  • Learning Vector Quantization.
  • Support Vector Machines
  • Bagging and Random Forest
  • Boosting and AdaBoost

The Lifecycle of Data Science:

There are six phases of lifecycle namely Discovery, Data Preparation, Model Planning, Model Building, Operationalize and Communicating results.

 

Discovery: It’s essential to explore and understand the requirements, specification, budgets, priorities and more before starting a project. You should be capable of asking the right questions. You also need to formulate the Initial hypotheses to test by framing the problems in your business.

 

Data Preparation: You need an analytical sandbox, where you can execute the analytics of your project for the complete duration. You need to preprocess, explore and condition different data from data priority to modeling. Apart from that you also need to perform ETLT (Extract, Transform, Load and Transform) to acquire the sandbox data.

 

Model Planning: In this phase, you can determine the techniques and methods to draw the variables and its relationships. You will also apply EDA (Exploratory Data Analytics) with the help of different visualization tools and statistical formulas. Some of the standard tools for model planning includes SQL Analysis Services, R and SAS/ACCESS.

 

  • SQL Analysis Services is capable of performing in-database analytics with the help of basic predictive models and data mining functions.
  • R has a complete modeling capability set and offers a good environment for developing different interpretive models.
  • SAS/ACCESS is used for accessing Hadoop data and is used to create reusable and repeatable model flow diagrams.

 

Model Building: You develop different datasets for testing and training. Various tools available for model Building includes SAS Enterprise Miner, SPCS Modeler, Alpine Miner, WEKA, MatLab and Statistica. You will analyze different learning techniques like association, classification, and clustering to develop the model.

 

Operationalize: You deliver briefings, final reports, technical documents and coding in this phase.

 

Communicating Results: In this phase, you will recognize the significant findings and explore whether the project results are success or failure depending on the phase criteria.

 

It is not wrong to say that the future is in the hand of Data Scientists. Numerous data offers opportunities to bring out critical business decisions and results. It is expected soon to change the complete way everyone looks at the world flooded with data.

September 16, 2019
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