In today’s generation data is something you will find everywhere. It is growing at a rapid rate, data is the thing which is doubling every two years and changing our day to day life and the way of living. About 1.7 megabytes of new information will be created every second for every human being on the planet, which makes it extremely important to know the basics of the field at least. After all, here is where our future lies.
Here we will differentiate between the Data Science, Big Data and Data Analytics, based on how it is, what it is, where to use, the skill you need to know and the salary in each field.
Let’s first start with understanding what these concepts are.
What is Data Science?
Data Science is the combination of statistics, mathematics, programming, problem-solving, capturing data in ingenious ways, the ability to look at things differently, and the activity of cleansing, preparing, and aligning the data. It deals with both unstructured data and structured data also the data science is related to data cleansing, preparation, and analysis.
Read Here, Top 12 Most Preferred Data Science Careers
Applications of Data Science:
1: Internet Search
Search engines use data science for delivering the best result for search queries in a fraction of second.
2: Digital Advertisement
This is the only reason for digital ads getting higher CTR than a traditional advertisement.
3: Recommender Systems
A lot of companies use this system to promote their products and suggestions by the user’s demands and relevance of information. The recommendations are based on the user’s previous search results.
Courses For Data science:
|Course 1||Data Science Certification Training - R Programming|
Data Science with Python
|Course 3||Machine Learning|
|Course 4||Tableau Desktop 10 Qualified Associate Training|
|Course 5||Big Data Hadoop and Spark Developer|
|Course 6||Data Science Capstone|
Skills Required to Become a Data Scientist
1: Education: 88% have a Master’s Degree, and 46% have PhDs
2: In-depth knowledge of SAS or R: For Data Science, R is generally preferred.
3: Python coding: Python is the most common coding language that is used in data science, along with Java, Perl, C/C++.
4: Hadoop platform: Although not always a requirement, knowing the Haddon platform is still preferred for the field. Having a bit of experience in Hive or Pig is also a huge selling point.
5: SQL database/coding: Though NoSQL and Hadoop have become a significant part of the Data Science background, it is still preferred if you can write and execute complex queries in SQL.
6: Working with unstructured data: It is essential that a Data Scientist can work with unstructured data, be it on social media, video feeds, or audio.
Data Scientist Salary
According to Glassdoor, the average salary of a Data Scientist Salary in India
7.7 lakh, However in the USA the salary is $108,224 per year.
What is Big Data?
Big data is a field that treats ways to analyze, systematically extract information from, or otherwise, deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Also, Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it's not the amount of data that's important.
Applications of Big Data
1: Big Data For Financial Services
Banks use big data for their financial services like Credit card companies, retail banks, private wealth management advisories, insurance firms, venture funds, and institutional investment. The common problem among them all is the massive amounts of multi-structured data living in multiple disparate systems, which can be solved by big data
Big data is used in several ways like:-
i- Customer analytics
ii- Compliance analytics
iii- Fraud analytics
iv- Operational analytics
2: Big Data in communication
The top priority for the telecommunication service providers in the Gaining new subscribers, retaining customers and expanding within the current subscriber. The solutions to these challenges lie in the ability to combine and analyze the masses of customer-generated data and machine-generated data that is being created every day.
3: Big Data for Retail
This requires the ability to analyze all the disparate data sources that companies deal with every day, including the weblogs, customer transaction data, social media, store-branded credit card data, and loyalty program data. Brick and Mortar or an online e-tailer, the answer to staying the game and being competitive understands the customer better to serve them.
Courses for Big Data:
|Course 1||Introduction to Big Data|
|Course 2||Big Data Modeling and Management Systems|
|Course 3||Big Data Integration and Processing|
|Course 4||Machine Learning With Big Data|
|Course 5||Graph Analytics for Big Data|
|Course 6||Big Data - Capstone Project|
Skills Required to Become a Big Data Specialist
1: Analytical skills: The ability to be able to make sense of the piles of data that you get. With analytical skills, you will be able to determine which data is relevant to your solution, more like problem-solving.
2: Creativity: You need to have the ability to create new methods to gather, interpret, and analyze a data strategy. This is an extremely suitable skill to possess.
3: Mathematics and statistical skills: Good, old-fashioned “number crunching.” This is extremely necessary, be it in data science, data analytics, or big data.
4: Computer science: Computers are the workhorses behind every data strategy. Programmers will have a constant need to come up with algorithms to process data into insights.
5: Business skills: Big Data professionals will need to have an understanding of the business objectives that are in place, as well as the underlying processes that drive the growth of the business as well as its profit.
Big Data Specialist Salary
According to Glassdoor, the average salary of a Big Data Specialist in India is 8.5 lakhs, However, the salary for the same in the USA is $106,784 per year.
What is Data Analytics?
Data analytics is the science of analyzing raw data in order to make conclusions about that information. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption.
Applications of Data Analytics
The main challenge for hospitals with cost pressures tightens is to treat as many patients as they can efficiently, keeping in mind the improvement of the quality of care. Instrument and machine data are being used increasingly to track as well as optimize patient flow, treatment, and equipment used in the hospitals. It is estimated that there will be a 1% efficiency gain that could yield more than $63 billion in global healthcare savings.
Data analytics can optimize the buying experience through mobile/ weblog and social media data analysis. Travel sights can gain insights into the customer’s desires and preferences. Products can be up-sold by correlating the current sales to the subsequent browsing increase browse-to-buy conversions via customized packages and offers. Personalized travel recommendations can also be delivered by data analytics based on social media data.
Data Analytics helps in collecting data to optimize and spend within as well as across games. Game companies gain insight into the dislikes, the relationships, and the likes of the users.
4: Energy Management
Most firms are using data analytics for energy management, including smart-grid management, energy optimization, energy distribution, and building automation in utility companies. The application here is centered on the controlling and monitoring of network devices, dispatch crews, and manage service outages. Utilities are given the ability to integrate millions of data points in the network performance and lets the engineers use the analytics to monitor the network.
Courses for Data Analytics:
|Course 1|| Data Analytics for Lean Six Sigma|
|Course 2||Introduction to Data Analytics for Business|
|Course 3||Beginner Statistics for Data Analytics |
|Course 4|| Complete Data Analysis Course with Pandas & NumPy: Python|
|Course 5||Beginner’s Guide to Data & Data Analytics by SF Data School|
|Course 6||Advanced Business Analytics Specialization|
|Course 7||R Level 1 - Data Analytics with R|
|Course 8|| Health Information Literacy for Data Analytics Specialization|
|Course 9 ||Data Analytics: SQL for newbs, beginners, and marketers|
|Course 10|| Social Media Data Analytics|
Read Here, Data Analysis: The Trending Career Option
Skills Required to Become a Data Analyst
1: Programming skills: Knowing programming languages are R and Python are extremely important for any data analyst.
2: Statistical skills and mathematics: Descriptive and inferential statistics and experimental designs are a must for data scientists.
3:Machine learning skills
4: Data wrangling skills: The ability to map raw data and convert it into another format that allows for more convenient consumption of the data.
5: Communication and Data Visualization skills
6: Data Intuition: a professional needs to be able to think like a data analyst.
Data Analyst Salary
According to Glassdoor, the average salary for a Data Analyst Salary in India is
4.1 L. However, the salary in the USA for the same is $61,473 per year.
The salary increases as per the knowledge and expertise you bring to the table.
Also, Read Here Data Analyst: Career Path, Skills, Qualifications, and Responsibilities