Data is everywhere and a part of our daily lives in more ways than most realize. The amount of digital data that exists that we create is growing exponentially. According to estimates, global data creation will reach 180 zettabytes.
Therefore, there is a need for specialists who understand the fundamentals of Data Scientist, big data, and data analytics and can make comparisons such as Data Scientist vs. data analytics that help distinguish between different disciplines of data processing.
These three terms are often heard in the IT sector, and while their purposes share some similarities, they have some deep differences. This blog will give you a clear understanding of the meaning, application, and skills required to become a data scientist, prominent data specialist, or data analyst.
Before we dive deeper into the topic, let’s first understand what data is.
What is Data?
Data is a collection of facts and bits of information. In the real world, data is either structured or unstructured. This blog on Data Scientist vs. Data Analytics vs. Big Data will first comprehend the data types.
Structured data is data that has order and a well-defined structure. Because structured data is consistent and well-defined, it is easy to store and access. Also, searching for data is easy because we can use indexes to store structured data.
Another type is unstructured data. It is inconsistent because it has no structure, format, or sequence. Unstructured data is prone to errors when we index it. Understanding and manipulating unstructured data is, therefore, a difficult task. Interestingly, we always have more inconsistent unstructured data than structured data in the real world.
Why is data important?
Check out the stats below to see what’s going on in your daily data life:
Average daily –
People from around the world:
” Mail over 300 billion emails and send 500 million tweets
” Send over 65 billion messages via WhatsApp
” Perform 5.6 billion searches on Google
” Facebook generates almost 4 petabytes of data
And by 2025, 463 exabytes of data will be available worldwide!
Data is one of the most prominent assets of any company today. In fact, Forbes has long predicted this, stating, The overall data market is expected to nearly double. Revenue will grow from $69.6 billion in 2015 to $132.3 billion in 2020. From these statistics, we can infer how important data is and how it needs to be used for businesses.
What is Big data
Big data refers to massive data sets from various sources at high speed. Any data set with one characteristic can be called Big Data. It is also data with truth and value.
Big Data analyzes insights to help you make business moves. Some real-world examples of big data are as follows:
” Big Data is used to find out the buying habits of consumers.
” It can be used to monitor health through data from wearable devices.
” The transportation sector utilizes fuel optimization tools where big data is used.
” Used for predictive inventory arrangement.
” It can assist you with real-time data monitoring and cybersecurity logs.
What is Big Data Analytics?
Big data analytics uses specialized software or platforms to draw conclusions or find answers to distinct questions established on correlations or relationships between data sets from various systems.
It helps enterprises or institutions discover patterns and gain insights by using the strengths of IT, marketing, and Data Scientist skills.
What is Data Scientist
Data Scientist is dragging information and insights from structured or unstructured data established on interdisciplinary learning in mathematics, statistics, computer science, and machine learning.
Some real-life Applications where Data Scientist is used are:
” Data Scientist is of great help in identifying and predicting diseases.
” It can be used with personalized health recommendations.
” It is excellent for real-time optimization of shipping routes.
” Data Scientist can be utilized to automate digital ad placement.
Example:
Let’s take an example from the banking industry to explain the roles of big data professionals, data scientists, and data analysts:
Data Scientist will help banking
” Fraud detection and prevention
” Risk management
” Analysis of customer data
” Marketing and sales
” And chatbots and virtual driving assistants with artificial intelligence.
Big Data will help the banking sector with:
” To provide its customers with personalized banking solutions.
” Performance increase.
” Conducting practical analysis of customer feedback
” And with effective risk management.
Data Analyst will help the banking sector:
” Analyze the bank’s work systems,
” Information, procedures, and documents.
” Bank data analysts will consider the financial and managerial aspects of the bank, and therefore cost and time can be determined for each function.
” This role is also responsible for reviewing the monthly audit of cost savings.
Skills to Become a Data Scientist
Data Scientist is an interdisciplinary field of study. It requires different fields like programming, database, and machine learning. According to Forbes, “data scientist jobs are among the best in the IT industry.” The average earning for a data scientist is $120,000.
To become a Data Scientist, you need to acquire the skills below:
” Good understanding of Python and R programming languages
” Knowledge of mathematics, especially statistics and probability
” Knowledge of SQL database queries
” Knowledge of data mining
” Knowledge of working with data visualization tools
Skills to become a Big Data professional
Big Data is another broadly used technology in the industry. LinkedIn reports that the average salary for Big Data professionals in the United States is $115,689. In India, this salary is around ?725,000 for a fresher.
Here are some of the top skills you require to get into big data with decent earnings:
” Experience with Big Data Hadoop
” Good knowledge of Apache Spark
” Background of NoSQL databases such as MongoDB and Couchbase
” Understanding of quantitative and statistical analysis approach
” Excellent knowledge of SQL
” Good understanding of programming languages such as Python, C, C++, Java, and Scala
Skills to become a data analytics professional
Nowadays, data analysis has become an essential part of business processes. Organizations hire data analysts to perform fundamental data analysis. According to McKinsey, more than 10,000 jobs will be open for data analysts in 2021. The average data analyst salary in the US is also around $105,253. Below are the skills you should have if you want to become a data analyst:
” Programming experience in Python and R
” Knowledge of statistics and probability
” Data visualization and presentation skills
” Analytical skills
” Good knowledge of Microsoft Excel
” Understanding how to create dashboards and reports
Big data vs. Data Analytics vs. Data Scientist: Differences and Comparison
Big data
Big data is generated from various sources in massive volumes at high speed.
Skill sets
” Set up/maintain infrastructure
” Data preprocessing
” API development
” Data channel implementation and monitoring
Areas of use:
Financial services, retail, health, sports, performance optimization, communication, etc.
Tools:
” Kafka and Storm
” Cloud Computing
” AWS/Azure/GCP
Annual Salary Range (USD): 95000 to 165000
Data Analytics
Use specialized software and tools to analyze big data and make decisions.
Required skill sets:
” Analyze the data
” Data modeling
” Data engineering
” Storyboarding
” Business Analytics
” Business Intelligence
Areas of use:
Healthcare, travel, gaming, energy management, etc.
Tools:
” KNIME, SAS, Python
” Power BI/Table
Annual Salary Range (USD): 75000 to 130000
Data Scientist
Using multiple disciplines to extract information and interpretable insights from structured and unstructured data.
Required skill sets:
” Machine learning
” Exploratory analysis
” Data mining
” Visualization of modeling
” Software development
Areas of use:
An Internet search, search recommendations, digital ads, image/speech recognition, fraud, risk detection, etc.
Tools:
” Apache Spark/Hive
” Neural network frameworks
” R or Python
Annual salary range (USD): 100,000 to 185,000
Conclusion
This blog has discussed the minor and significant differences between Data Scientist Vs. Big Data vs. Data Analytics touches definition, application, skills, and position-specific salary concepts.
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