Complete Data Science Tutorial
Автор: Informatics for all
Загружено: 2026-01-20
Просмотров: 14
Mohammad Mobashir defined data science as an interdisciplinary field with high global demand and job opportunities, including freelance work. Mohammad Mobashir highlighted career prospects with high salaries in developed countries and a projected 31% growth in data science positions over the next decade. Mohammad Mobashir differentiated data science from business intelligence, discussed the advantages and disadvantages of data science, and outlined its applications and essential tools.
Introduction to Data Science Mohammad Mobashir defined data science as an interdisciplinary field combining statistics, computer science, and domain knowledge to extract insights from data. They highlighted its high demand and job opportunities globally, including freelance work. Data science involves various stages such as data collection, cleaning, analysis, visualization, and modeling (00:00:00) (00:04:34).
Career Prospects and Market Growth in Data Science Mohammad Mobashir detailed the high salaries in developed countries for data analysis jobs, with minimum annual salaries around $80,000 to $106,000. They also presented projections from the Bureau of Labor Statistics, indicating a 31% growth in data science positions over the next decade, with roles increasing to 2.7 million in the US market (00:02:38). Common job titles in this field include data analyst, data scientist, business analyst, and data engineer (00:04:34).
Distinguishing Data Science from Business Intelligence Mohammad Mobashir differentiated data science from business intelligence by explaining that data science utilizes both structured and unstructured data, incorporates machine learning, graph analysis, and natural language processing, and focuses on present and future predictions, unlike business intelligence which primarily deals with structured data and focuses on past and present analyses (00:04:34). They outlined a six-phase data science lifecycle: problem identification, data collection, data processing, data analysis, data modeling, and model development (00:06:14).
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