10 Must-Read Big Data Articles for Data Scientists in 2021

10 Must-Read Big Data Articles for Data Scientists in 2021

Data scientists are in high demand as companies across all industries collect massive amounts of data. Effective data management and analysis are essential for businesses to make informed decisions and stay ahead of the competition. As a data scientist, it’s important to stay up-to-date with the latest developments in the field to maintain a competitive edge. Here, we’ve compiled a list of 10 must-read big data articles that data scientists should read in 2021.

1. The Top 10 Big Data Trends for 2021

This article by Bernard Marr outlines the top 10 trends in big data for 2021, including artificial intelligence (AI) and machine learning (ML), the Internet of Things (IoT), and edge computing. It’s a valuable read for data scientists as it provides insights into the latest trends in the field and shows how they’re expected to evolve.

2. Understanding Big Data: The Five Vs

This article by Bernard Marr explains the Five Vs of Big Data – Volume, Velocity, Variety, Veracity, and Value – illustrating the challenges and opportunities presented by each. Data scientists should read this article as it provides a foundational understanding of big data and its characteristics.

3. Top Machine Learning Algorithms for Data Scientists to Know

This article by Sebastian Raschka explains the top machine learning algorithms for data scientists. It explores the differences between supervised and unsupervised learning and categories of machine learning algorithms such as decision trees, linear regression, and neural networks. Data scientists will benefit from this article as it provides a detailed overview of the most important algorithms in the field.

4. Advantages and Disadvantages of Big Data

This article by Priyanka Thakare highlights the pros and cons of big data. On the one hand, big data provides incredible insights and opportunities for businesses to grow. However, there are also potential ethical and privacy concerns associated with its use. Data scientists should read this article to better understand the implications of working with big data.

5. Big Data Analytics in Healthcare: A Review

This article by Ramesh Raliya et al. examines the impact of big data analytics on healthcare. It covers the benefits of using big data in healthcare, including improved disease diagnosis and analysis, as well as potential concerns like data privacy and security. Data scientists working in healthcare will gain valuable insights from this article.

6. How Big Data Is Revolutionizing eCommerce

This article by Irfan Ahmad explains how big data is transforming eCommerce. It covers the impact of big data on sales, marketing, and customer service. Data scientists working in eCommerce will benefit from this article as it provides valuable insights into the role of big data in the industry.

7. The Role of Big Data in Sports Analytics

This article by Sajal Pyakurel explores the role of big data in sports analytics. It covers how teams are using data to make game-changing decisions, improve player performance, and even predict injuries. Data scientists working in sports analytics will find this article useful for understanding the latest trends in the field.

8. Predictive Analytics: What Is It and How Does It Work?

This article by Joseph Lee provides an in-depth overview of predictive analytics. It explores how machine learning algorithms can be used to forecast future outcomes based on past data. Data scientists should read this article to better understand the fundamentals of predictive analytics and how it can help businesses make data-driven decisions.

9. Big Data and Climate Change: How Data Science Is Helping to Tackle Global Warming

This article by Jennifer Lonoff Schiff explains the role of big data in addressing climate change. It covers how data scientists are using big data to understand the impact of human activity on the environment and develop solutions to mitigate it. Data scientists interested in contributing to global sustainability efforts will find this article valuable.

10. Ethics and Governance in the Age of Data Science

This article by Salil Vadhan explores the ethical and governance issues associated with big data. It covers important issues, including privacy, accountability, and transparency. Data scientists should read this article to better understand ethical considerations in the data science industry and inform responsible data management practices.

Conclusion

In conclusion, these 10 articles provide valuable insights into the latest trends in big data and data science. From machine learning algorithms to ethical considerations, data scientists should read these articles to stay up-to-date with the latest developments in the field. By doing so, they’ll be better equipped to use big data to drive business insights and results.

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