Data is everywhere, Data are the characteristics or information, usually numerical, that are collected through observation. In other words, data are a set of values of qualitative or quantitative variables about one or more persons or objects, while a datum is a single value of a single variable. This context is about “Data Science vs Big Data”, let’s first understand the types of data.
Data Science VS Big Data
DATA SCIENCE
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
It 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 data.
BIG DATA
Big data is a term that applies to the growing availability of large datasets in information technology. Big Data is a collection of data that is huge in volume, yet growing exponentially with time. It is a data with so large size and complexity that none of traditional data management tools can store it or process it efficiently. Big data is also a data but with huge size.
“Immense volumes of data, both unstructured and structured, big data can inundate a business on a day-to-day basis. Big data is used to analyze insights, which can lead to better decisions and strategic business moves” – Buzzword.
Applications of Data Science
Data science is quite a challenging area due to the complexities involved in combining and applying different methods, algorithms, and complex programming techniques to perform intelligent analysis in large volumes of data. Hence, the field of data science has evolved from big data, or big data and data science are inseparable.
Big DATA- Big data classifies data into unstructured, semi-structured, and structured data.
- Unstructured data – social networks, emails, blogs, digital images, and contents
- Semi-structured data – XML files, text files, etc.
- Structured data – RDBMS, OLTP, transaction data, and other structured data formats.
Data Science VS Big Data
| Concept | BIG DATA | DATA SCIENCE |
| Meaning |
Revolves around the huge volumes of data which cannot be handled using the conventional data analysis method | Scientific approach of interpreting the data and retrieves the information from a given data set |
| Concept |
Descriptive Statistics | Descriptive Statistics, hypothesis testing and ANOVA |
| Basic Analytics |
Exploratory Data Analytics | Data Analysis, Manipulation and Visualization |
| Application Areas |
Telecommunication
Financial Service Health & Sports Research & Development Security & Law Enforcement |
Internet Search
Digital Advertisement Text-to-Speech recognition Risk-detection And other activities |
| Formation |
Data filtering
Preparation Analysis |
Internet Users/ traffic
Live feed Data generated from system logs |
| Approaches |
Used business to track their market presence which helps them to develop agility and gain a competitive advantage over others | Uses mathematics and statistic extensively along with programming skills to develop a model to test the hypothesis and make decisions in the business |
Key differences between Big Data and Data Science
Job Responsibilities-
Data Science- A Data Scientist explores and analyze the data and use various advanced machine learning algorithms to identify the occurrence of a particular event in the future. This involves identifying hidden patterns, unknown correlations, market trends and other useful business information.
Big Data- Big data professionals describe the structure and behaviour of a big data solution and how it can be delivered using big data technologies such as Hadoop, Spark, and Kafka etc. based on requirements.
Required Skillset
These skills are essential for making sense of data and determining which data is relevant to when creating reports and looking for solutions. To become a Data Scientist, you should have an excellent in these…
- analytical skills
- data management skills
- programming skills
- technical skills
- sound knowledge of database system
As a big data professional, it is necessary to develop proficiency, programming languages skills in statistics and mathematics are required.
- Data wrangling skills
- Data visualisation,
- Machine learning skills, and
- Communication skills.
Final Words
No matter which path you choose or whether you will become a Data Science or Big Data Professional. They both have their own shining fields. If you choose your career in data science and want to do Data Science Course in Noida to get Data Science Training in Noida, then you can join the Aptron Data Science Institute in Noida.
Or you have decided to become a big data professionally and have joined Big Data training in Noida then the same place is best for Big Data course in Noida. You can go with Aptron for every IT course.
Other Related courses-
Data Analytics Training in Noida