Data Science - 5 Data Science and Business Analytics:

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 Data Science and Business Analytics:


I. Programming Background:


Universal Knowledge: Almost everyone possesses at least a basic programming background.

Diverse Proficiency: Ranges from individuals with minimal experience to Masters of Science in Computer Science and MBA students with technical backgrounds.

Importance of Computational Thinking: Emphasis on the ability to think computationally as a crucial skill.

II. Evolution of Data Science:


Hot Topics: Data science and business analytics have gained significant prominence in the last four to five years.

New Tools and Approaches: Emergence of new tools and approaches to handle large volumes of data that traditional techniques couldn't manage.

Increasing Awareness: Companies and employers now understand the importance of data science in various fields.

Growing Demand: Significant growth in enrollment for data-related courses, indicating increasing demand and awareness.

III. Shifting Perceptions and Career Paths:


Parental Influence: Shift in parental advice from traditional career paths to recommending STEM classes and careers in data science and analytics.

Changing Landscape: Recognition that fields like data science and analytics offer lucrative and promising career opportunities.

IV. Definition of Big Data:


Personal Definition: Data that is large enough and has sufficient volume and velocity, making it unmanageable with traditional database systems.

Historical Origin: Originated from Google's challenge in storing all web pages globally, leading to the development of approaches like Hadoop.

Expanding Scope: Encompasses not only storage challenges but also the development of new analytical and statistical techniques for handling large datasets.

V. Challenges and Innovations:


Statistical Perspective: Some view big data as data that cannot fit on a thumb drive.

Innovation by Google: Google's attempt to solve the page rank algorithm problem initiated the development of technologies like Hadoop.

Analytical Techniques: Continuous development of analytical and statistical techniques to handle exceptionally large datasets.

Deep Learning: Mention of the potential inclusion of deep learning in addressing big data challenges.

VI. Conclusion:


Dynamic Landscape: The landscape of data science and business analytics is evolving rapidly with increasing awareness, technological advancements, and a growing demand for skilled professionals.




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