Practical advice for analysis of large, complex data sets

Practical advice for analysis of large, complex data sets

1/11/2017

link

https://www.unofficialgoogledatascience.com/2016/10/practical-advice-for-analysis-of-large.html

summary

This blog post provides practical advice for analyzing large datasets. It discusses the challenges that arise when working with big data, such as the need for efficient storage and processing, and the importance of careful data cleaning. The author emphasizes the importance of having a clear research question and hypothesis, as well as the need to choose appropriate statistical techniques and modeling approaches. The post also highlights the significance of data visualization and the use of diagnostic tools to evaluate model performance. Overall, it offers valuable insights and tips for conducting effective analysis on large datasets.

tags

data analysis ꞏ big data ꞏ data science ꞏ data visualization ꞏ large datasets ꞏ data management ꞏ data cleaning ꞏ data preprocessing ꞏ data exploration ꞏ exploratory data analysis ꞏ statistical analysis ꞏ machine learning ꞏ predictive modeling ꞏ data mining ꞏ data engineering ꞏ data wrangling ꞏ data extraction ꞏ data transformation ꞏ data interpretation ꞏ data manipulation ꞏ data analytics ꞏ data-driven decision making ꞏ data modeling ꞏ data integration ꞏ data architecture ꞏ data storage ꞏ data querying ꞏ data infrastructure ꞏ data pipeline ꞏ data processing ꞏ data quality ꞏ data validation ꞏ data aggregation ꞏ data normalization ꞏ data scaling ꞏ data sampling ꞏ data storytelling ꞏ data insights ꞏ data presentation ꞏ data exploration techniques ꞏ data analysis tools ꞏ data analysis methodologies