What is Data Science?
Data science is a multidisciplinary field that uses scientific procedures, systems, algorithms, and methodologies to extract knowledge and insights from both structured and unstructured data. To address complicated issues and arrive at data-driven judgements, it integrates data analysis, machine learning, statistics, and computer science.
"The sexy job in the next ten years will be statisticians."
Difference between Data Science and Statistics
| Data Science | Statistics |
| Includes statistics but also incorporates data engineering, machine learning, data mining, and data visualization. | Focuses on collecting, analyzing, interpreting, presenting, and organizing data. |
| Uses a wider range of tools and techniques, including programming languages like Python and R, big data technologies such as Hadoop and Spark, and machine learning libraries like TensorFlow and Scikit-learn. | Relies on mathematical theories and models, often using tools like R or SAS. |
| Aims to extract actionable insights and create predictive models to solve business problems. | Aims to make inferences about a population based on a sample. |
What Makes Someone a Data Scientist?
"A data scientist is that unique blend of skills that can both unlock the insights of data and tell a fantastic story via the data."
Defining Data Science
What data scientists do is data science. It calls for the capacity to solve problems using data and effectively communicate insights. It is not constrained by the volume of data or particular tools; rather, it concentrates on drawing valuable conclusions and adding value from data.
Data science is an ever-evolving and diverse field that integrates computer science, statistics, and domain-specific expertise. It is motivated by inquisitiveness, technical proficiency, and the capacity to clearly state insights derived from data analysis. The fundamental idea of data science is still using data to solve issues and inform decisions, even though the definition of the field is still developing.