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.
Mohammad Mobashir covered fundamental concepts in data science, including essential coding languages (R, Python) and tools like Hadoop, SQL, and SAS. Mohammad Mobashir discussed diverse applications of data science, such as fraud detection, healthcare diagnostics, and internet search, and explained key algorithms in supervised (classification, regression) and unsupervised (clustering) learning, along with linear regression. Mohammad Mobashir also addressed career entry requirements and clarified the distinction between free YouTube tutorials and paid, certificated Zoom classes.
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.
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. Common job titles in this field include data analyst, data scientist, business analyst, and data engineer.
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. They outlined a six-phase data science lifecycle: problem identification, data collection, data processing, data analysis, data modeling, and model development.
Advantages and Disadvantages of Data Science Mohammad Mobashir discussed the pros of data science, such as it being a fast-growing, versatile, and highly demanded field with abundant, well-paid, and prestigious roles that lead to better decision-making and predictive analysis. However, they also noted disadvantages, including its ambiguous nature, the near impossibility of mastering it, the significant domain knowledge required, the potential for unexpected results from arbitrary data, and issues related to data privacy, cost, time, and bias.
Applications and Tools in Data Science Mohammad Mobashir outlined diverse applications of data science across various sectors like healthcare, finance, retail, transportation, entertainment, and e-commerce. They also listed key tools and programming languages essential for data science, such as SQL, Spark, R, Python, MATLAB, and libraries like pandas and NumPy, emphasizing that a basic understanding of these tools, along with foundational mathematics and statistics, is sufficient to enter the field. Mohammad Mobashir concluded by highlighting the importance of basic statistical methods and practical application knowledge for aspiring data scientists.
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