THE 10 DATA MINING TECHNIQUES DATA SCIENTISTS NEED FOR THEIR TOOLBOX
Data Mining – At their core, data scientists have a math and statistics background. Out of this math background, they’re creating advanced analytics. On the extreme end of this applied math, they’re creating machine learning models and artificial intelligence. Just like their software engineering counterparts, data scientists will have to interact with the business side. This includes understanding the domain enough to make insights.
Data scientists are often tasked with analyzing data to help the business, and this requires a level of business acumen. Finally, their results need to be given to the business in an understandable fashion. This requires the ability to verbally and visually communicate complex results and observations in a way that the business can understand and act on them.
Thus, it’ll be extremely valuable for any aspiring data scientists to learn data mining — the process where one structures the raw data and formulate or recognize the various patterns in the data through the mathematical and computational algorithms. This helps to generate new information and unlock various insights.
Here is a simple list of reasons on why you should study data mining?
- There is a heavy demand for deep analytical talent at the moment in the tech industry.
- You can gain a valuable skill if you want to jump into Data Science / Big Data / Predictive Analytics.
- Given lots of data, you’ll be able to discover patterns and models that are valid, useful, unexpected, and understandable.
- You can find human-interpretable patterns that describe the data (Descriptive), or
- Use some variables to predict unknown or future values of other variables (Predictive).
- You can activate your knowledge in CS theory, Machine Learning, and Databases.
- Last but not least, you’ll learn a lot about algorithms, computing architectures, data scalability, and automation for handling massive datasets.
In my last semester in college, I did an independent study on Big Data. The class covers extensive materials in a book titled Mining of Massive Datasets by Leskovec, Rajaraman, and Ullman. We discussed a lot of important algorithms and systems in Big Data such as MapReduce, Social Graph, Clustering…. This experience deepened my interest in the Data Mining academic field and convinced me to specialize further in it. Recently, I took again Stanford CS246’s Mining of Massive Datasets, which covered that book and featured lectures from the authors. Now being exposed to that content twice, I want to share the 10 mining techniques from the book that I believe any data scientists should learn to be more effective while handling big datasets.
This article originally appeared on builtin.com To read the full article, click here.
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