Data science vs Big Data vs Data Analytics

Data Science

It is a field of analyzing the large amount of raw data. It categorizes the raw data into useful patterns. The patterns are used to form more concrete set of questions. The answers for these questions give the correlation between the data sets. This correlation and the patterns will be useful in solving more problems.

 

The Data science uses the predictive analysis. It also uses the statistics and the AI for data analysis. The queries framed in data science is used to make the form. It is also useful in groups. Groups are also known as the clusters. This clustering is further used to identify the business analysis. Data Science will take unordered data. It will convert it in to the ordered format. The ordered format is very useful for further analysis.

 

The Data Science is a potential field. It is used to analyze the huge amount of the data. Finally, it will find useful answers from the large amount of the data. It is used to predict the search results based on the input. It recommends the users about the most visited pages. Data science will also recommend the products.

 

Big Data

Big data is used for gathering and storing large amount of information. It contains both structured as well as unstructured data. There are three Vs in big data. They are Volume, Velocity, Variety. Volume stands for data. Velocity stands for the speed of data. Variety stands for the number of types of the data.

 

In big data, we can handle any data from any sources. It will analyze the given data to find the answer. The Answer makes possible for the cost saving as well as the time reduction. New product development is feasible with the big data. Understand the market conditions is easy with big data. It will control the online reputation.

 

Big data Spark is an open source framework. It is from the Apache Software Foundation. Big data spark is a computing engine. Using this, we can process and analyze the large amount of real time data. Mainly inter-connected platforms system uses this.

 

Big data spark processes data very quickly. Transferring details from computer’s hard disk is possible using this spark. Faster electronic memory will store the transferred data. It also works with the cluster as well as process the data in parallel.

 

Data Analytics

It is the science of raw data used to give meaningful info and results. The results we derived are from the existing data. It is simply said to be the data analytics. It is implementing algorithmic process of extracting the raw data.

 

Many companies use this Data analytics process. This process will enable them to take effective decision. They also verify and disprove the old models or theory. Data analytics is one of the most powerful tools. The result will be from the fact known to the researchers.

 

It is the process of understand and devising the effective pattern. The patterns may be of recorded data using math as well as statistics. The Machine learning Technique as well as predictive modeling uses this.

 

Data Science vs Big Data vs Data Analytics in Tools and Technologies Perspective

The data-analytics tools are used to achieve our goals. Some of the popular tools are Python, SAS, R as well as Hadoop. Tableau Microsoft and ClickView are also popular tools used. Following are tools as well as technologies which relates to these three terms.

 

Big Data Tools

Hadoop

Hadoop is an open source frame work. It is a java based frame work. It is responsible for running application as well as storing data. Using Cluster of commodities hardware, we can able to store these data. It will allow expansive storage of the varied range of data. Hadoop will concentrate on financial management as well as operation.

 

Hadoop is one of the best big data tools. They are highly scalable. It is flexible to store the big data. It makes computer to perform fast. They have high tolerance against the hardware malfunctions to protect data.

 

NoSQL

NoSQL is one of the most important big data tools. It will handle both structured as well as unstructured data. The scope as well as applications will differentiate NoSQL from SQL. It doesn’t not used any technology to store unstructured data. They have common values in each set of rows. NoSQL will work effectively to store large amount of data. There are number of open source NoSQL data base use to analysis data.

 

HIVE

Apache Hive is the best distributed data management tool for Hadoop. Hive has its own query language. It is like the SQL. HiveSQL is the query language of the Hive. It is also known as HSQL. Hive query language will run on the top of the Hadoop architecture. Data mining as well as data management uses this.

 

Data Analytics Tools and Languages

R

R is an open source programming language as well as software environment. It will facilitate the graphics as well as the statistical computing. The Data miners will use the R programming language. The Statisticians will use this R to develop software for statistical analysis. The Data analysis will also use this.

 

The Social media sites will use R. Manufacturing, predictive model for the automotive uses the R. The R is famous in data visualization in the journalism. We can use R in the finance & banking as well as Drugs & food manufacturing. To generate a report, we will use the R programming.

 

Tableau

Tableau is an open source tool for the data analytics. It is used to connect the data source. It is also used to create the dashboards, visualizations, maps, etc., These all will create with the help of the real time updates presented on the web. Tableau is used to create such data. These Data can share with the client. This is possible with the help of the social media. It is one of the best software for visualization. It is also considered as the best software for data analysis.

 

Spark

Apache Spark is one of the best data processing engines. It is used to run the application in the Hadoop cluster. This process will be very fast. Its speed will be 100 time faster in memory as well as 10 time faster in the disk. It is very popular in the development of machine learning model. The data pipeline development is also using this Spark. It makes the data analysis process very easy. Mlib, the spark library provides several machine learning algorithms. The greater number of the data science techniques use this.

 

Data Science Tools and Languages

SAS

SAS is one of the best software which suits the data management. The business intelligence as well as advanced analytics uses SAS. The SAS will suit for the purpose of the predictive analytics. Multivariate analysis uses this SAS. SAS has two models used by the developers.

 

PYTHON

Python is an open source programming language. It is an object oriented, interpreted high level programming language. The development of the rapid application is possible with Python. It will work as a scripting language. It will able to connect all the existing component together. This is because of its dynamic binding as well as its type. The Data mugging is possible, and it will create web-based product.

 

SQL

SQL is one of the best programming languages for the data scientist. It is used to store as well retrieve the data. It is mainly used to handle the large database with huge amount of data. SQL has fast process time which will help in reducing the turnaround time.

 

Data science vs Big Data vs Data Analytics in Application Perspective

Applications for the Big Data

Communication

Telecom companies widely uses the big data. New subscribers as well as to retain the old one uses this big data. For spreading their base with existing customers, they use big data. It is used to compare the continuous generated data. Big data involves in solving the related issues.

 

Retail

Big data has the capacity to analyze the data. Customer transactions data as well as the weblogs uses Big data. Store-branded credit card details and loyalty program data uses this. Big data play a major role in the field of the social media.

 

Financial

All financial as well as account companies utilizes the big data. Big data plays a major role in the Banking sector. It helps them to resolve the issues fast. Fraud as well as customer analysis is few functions of big data. Operation & complains analysis is another function of them.

 

Education

All the education institution utilizes the big data. They utilize big data to handle the huge amount of the data. The big data has the number of applications. All the domains use this.

 

Applications for the Data Science

Digital Advertisement

Data science plays a major role in the digital marketing. It drives the CTR rate of the digital ads. It compares the ads with the old conventional ads.

 

Internet Search

Data science is the backbone for the search engines. It propels the search engine bots to crawl through the diverse content on internet. This will carry out once we hit the search key on the search engine.

 

Recommended System

Data science enhanced the user experiment. Companies promote a huge amount of the products. It will give us the suggestions while we brows on the internet.

 

Image/Speech Recognition

Image and speech recognition provide an enhanced user experience. It provides bar code scanning facility in our mobile. We can tag our friends. It will work on the model of speech to text conversion.

 

Applications for the Data Analytics

Gaming

All Gaming companies will use the data analysis. It will get an insight of our likes, dislikes as well as the relationship. It is possible by gathering the data for spending and optimizing.

 

Travel

Data analytics helps companies to influence & optimize our purchase. It is possible by analyzing social media, mobile and weblogs. It will help us in our travel patterns. Travel recommends like the shortest roots are also customized by this. It will depend on our data which will exist on our social network.

 

Energy Management

Data Analytics play a major role in the energy management. They can manage the smart-grids, distributed as well as optimize the energy. They can manage the build of automation for the utility companies. It will focus on the monitoring of the network devices. It will also focus on managing the network of the device.

 

Data science vs Big Data vs Data Analytics in Technical Skill Perspective

The following table will give the details for the Technical skills.

 

S.No	DATA SCIENTIST	BIG DATA PROFESSIONAL	DATA ANALYST
1	SQL/ Database coding	Business skills	SQL/ Database coding
2	SAS/R Coding	Data Visualization	Spread Sheet Knowledge
3	Machine learning	SQL/ Database coding	Programming Skill
4	Deep Learning principles	Familiarity with MATLAB	Scripting and Statistical skill
5	In deep knowledge of programming	Creativity	Reporting with the data visualization software
6	Statistical and Analytical Skills	Technologies like HADOOP, Spark, Hive etc.,	Data warehousing
7	Data Mining Activities	Working with unstructured data	HADOOP Based Analytics
8	Co-relations	General purpose language	Adobe and google analytics

 

Data Science vs Big Data vs Data Analytics in Trends Perspective

Following shows the trendiest feature of all the three fields.

 

• Big data is very trend in the Robots.
• Data Analysis is very trendy in machine learning.
• Data Science is very trendy in the Smart app.

Data Science vs Big Data vs Data Analytics in Salary Perspective

These professionals are in the same field but differ with the salary range. Among them the Data Scientist will get the highest pay. Then the big data team will pay next.

 

Conclusion

All three have some common tools as well language. They all have some common skills as well as specializations. They must undergo some special certified courses. Using this comparison, they can become an expert in their field.