Defining Data Science and Big data

[naveen]

Data science and big data are often used interchangeably, but they are distinct concepts with overlapping elements. Here's a breakdown to help you understand the key differences:


Data Science:

• Concept: A field of study that encompasses the entire process of extracting insights and knowledge from data. This includes collecting, cleaning, analyzing, interpreting, and visualizing data.
  
• Focus: Extracting valuable information from data to solve problems, make informed decisions, and support strategic objectives.
  
• Skills required: Statistics, mathematics, programming, machine learning, data visualization, communication, problem-solving skills.
  
• Tools: R, Python, SQL, data visualization tools (Tableau, Power BI), machine learning libraries (Scikit-learn, TensorFlow)

Big Data:

• Concept: Refers to large and complex datasets that are difficult to process with traditional methods due to their volume, velocity, variety, and veracity.
  
• Focus: Efficiently storing, managing, and processing massive datasets to enable data analysis and insights.
  
• Skills required: Programming (Java, Python), distributed computing frameworks (Hadoop, Spark), database management, data engineering.
  
• Tools: Hadoop ecosystem (HDFS, MapReduce, Spark), NoSQL databases, cloud computing platforms

Relationship:

• Big data is a subset of data science: The tools and techniques used in big data are often applied in data science projects involving large datasets.
  
• Data science relies on big data: For many data science applications, the ability to handle and analyze big data is crucial.

Here's an analogy:

• Think of data science as a chef: They gather ingredients (data), prepare them (cleaning and preprocessing), cook them (analysis), and present the dish (insights and visualizations).
  
• Big data is the pantry: It provides the chef with a vast array of ingredients in various forms (structured, unstructured) and sizes (small, large).

Scope of Data Science

• Data Scientist.
• Machine Learning Scientist.
• Data Analyst.
• Business Analyst.
• Machine Learning Engineer.
• Data Engineer.
• Data Architect.
• Database Administrator.
• Data Scientist.
• Machine Learning Engineer.

Scope of Big Data Engineer.

• Data Architect.
• Data Modeler.
• Data Scientist.
• Database Developer.
• Database Manager.
• Database Administrator.
• Database Analyst.
• Business Intelligence Analyst.

Skills Needed to Become a Data Science Professional

1. Probability and Statistics.
2. Programming Languages and Software.
3. Machine & Deep Learning.
4. Calculus and Linear Algebra.
5. Data Mining.
6. Data Cleansing.
7. Data Wrangling.
8. Natural Language Processing (NLP).
9. Database Management.
10. Data Visualisation.
11. Cloud Computing.
12. Communication Skills.
13. Statistics.

Skills Needed to Become a Big Data Professional

1. Programming Languages.
2. Machine Learning.
3. Data Mining.
4. Predictive Analysis.
5. Quantitative Analysis.
6. Data Visualisation.
7. Apache Spark.
8. Apache Hadoop.
9. NoSQL.
10. Problem-Solving Skills.

Which is the Better Option?

The interconnection of big data and data science only makes your choice easier. In fact, big data is a subset of data science.

In my opinion, both of them are quite fulfilling career options and offer great job opportunities