A Beginner's Manual to Data Science & Data Analytics

By Kimberly Cook |Email | Apr 30, 2018 | 13902 Views

Data is everywhere. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. Data has been a crucial part of our lives. 
So, let's know the basics of this field & the basic differences between this field. 
1. What are they? 
Data Science:
Data Science means mining large amounts of structured & unstructured data to identify patterns. It includes a combination of programming, statistical skills, machine learning, and algorithms. 
Big Data:
Big Data refers to humongous volumes of data. It includes capturing data, data storage, data sharing, data querying. 
Data Analytics:
Data Analytics helps to process and perform statistical analysis of data & discover how data can be used to draw conclusions and solve problems. 
2. What they do?
Data Scientist:
Data Scientist predicts the future based on past patterns. They help to explore and examine data from multiple disconnected sources. Also, they facilitate to develop new analytical methods and machine learning models. 
Big Data Professionals:
Big Data Professionals are the ones who are able to analyze system bottlenecks. They can build large scale data processing systems & also architect highly scalable distributed systems. 
Data Analysts:
Data Analysts are professionals who acquire, process and summarize data. They help to package data for insights. Also, they help to design & create data reports using various reporting tools. 
3. Where is it used?
Data Science is used in search engines, financial services & e-commerce. Big Data is used in financial services, communications & retail. Data Analytics is used in Healthcare, travel & IT industry. 
4. What are the key skills required ?
Data Science : 
- Programming skills like SAS, R, Python. 
- Statistical and mathematical skills
- Storytelling & data visualization
- Hadoop & SQL skills
- Machine Learning
Big data:
- Programming languages like Java, Scala
- NoSQL Databases like MongoDB, CassandraDB
- Frameworks like Apache Hadoop
- Excellent grasp of distributed systems
Data Analytics:
- Programming Skills like SAS, R and Python
- Statistical and Mathematical Skills
- Data wrangling skills
- Data visualization skills

Source: HOB