In broad terms, Data Science can be defined as a scientific field that uses scientific methods to extract knowledge and insights from structured and unstructured data and apply knowledge and actionable insights from data across a broad range of application domains, according to Wikipedia.
Data science is a field of study that utilizes cutting-edge tools and techniques to uncover hidden patterns and trends, thereby generating valuable insights that can be used to make more informed business decisions. It also encompasses predictive analytics, in which data scientists employ a variety of machine learning or statistical algorithms. (https://towardsdatascience.com/why-is-statistics-important-in-data-science-machine-learning-and-analytics-92b4a410f686)
In 2023/2024, the roadmap to becoming a data scientist can be confusing as this desirous career has myriad ways and contents. A unique, well-curated, and straightforward path to becoming a data scientist is an invaluable gift to a beginner in the field of data science.
The value of data in the 21st century cannot be overemphasized, as there are compelling reasons for businesses to be more efficient in the world with technological innovations that generate inconceivable data every second. Organizations and individuals constantly quest for data-driven decision-making as the world transitions to the digital economy, and transformation is commonplace.
The current requirement for employment includes competencies in a data science-related field, and this article focuses on the pathway to upskill to a data science-related role.
Statistics is critical for Data Scientists
Understanding the basics of statistics will help any newbie to get comfortable on the path to a data science career.
Data manipulation
Data scientists can clean and organize large data sets using Excel, R, SAS, Stata, Power Query M, Apache Spark, and other systems.
Critical thinking
Using linear regression, data scientists identify and model relationships between dependent and independent variables. Data scientists choose procedures with underlying assumptions that are considered during implementation. If beliefs are violated or incorrectly selected, the results will be incorrect.
Organization
Data scientists are constantly inundated with data from various sources and project opportunities. Expertise in statistical functions enables data scientists to work effectively within budget and time constraints. Routine processes also contribute to data security protection.
Problem-solving
In addition to pure computations and fundamental data analysis, data scientists use applied statistics to relate abstract discoveries to real-world problems. Additionally, data scientists utilize predictive analytics to plan future actions. All of this necessitates careful consideration and rational and innovative problem-solving strategies.
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