To define data mining, it is to extract valid information from enormous datasets and then turn it to useful patterns for further use cases. Data mining involves many data-relevant elements, such as data processing, data management, machine learning, statistics and database systems.
We have articles named 80 Best Data Science Books That Worth Reading and 88 Resources & Tools to Become a Data Scientist to help you better understand data science. In this article, we will mainly focus on data mining and 10 must-have skills you need to have.
Algorithm & Statistics Skills
Data Structure & Algorithms Knowledge
Data structures involve many things, such as arrays, stack, trees, queues, linked list, hash table, set and so on. Sorting, searching, dynamic programming, recursion are common Algorithms.
Master in data structures and algorithms can help you generate more insightful algorithmic solutions when dealing with huge datasets. It is super useful in the field of data mining.
Machine Learning Algorithm
Machine learning, one of the most significant sections of data mining, creates a mathematical model of sample data so that predictions/decisions can be made without programming explicitly. Besides, deep learning belongs to a bigger machine learning family of methods.
Data mining sometimes shares the same method as machine learning, and they overlap to a great extent.
NLP (Natural Language Processing)
As a part of computer science and artificial intelligence, NLP (Natural Language Processing) is designed to help computers understand, interpret and manipulate human language. NLP is commonly used in many fields like text analysis, word segmentation, automatic summarization, syntax and semantic analysis. If you need to deal with a big amount of text, then NLP is a must.
Basic Statistics Knowledge
Statistics basics are also vital to data miners. Data mining is not just about programming or computer science. It is significant for a data expert to have a basic knowledge of statistics (Probability, Probability Distribution, Correlation, Regression, Linear Algebra, Stochastic Process…,etc) since it can help you get more insights from data, especially how to identify questions, make an accurate conclusion, quantify the accuracy of your findings.
Computer Science Skills
R, Python, C++, Java, Matlab, SQL, SAS, Shell, etc are common programming languages requirements in the data mining field. You can’t deny that data mining relies on coding to a great extent. It’s hard to decide which language is the best for data mining since each language has its own pros and cons. Peter Glesson has raised 4 spectra to evaluate programming languages: Specificity, Generality, Productivity, and Performance, and they can be seen as pairs of axes (Specificity – Generality, Performance – Productivity). Each language will fall into somewhere in the map based on its characteristics. According to KD Nuggets’s research, R and Python are the most common-used programming languages for data science.
There are relational and non-relational databases. You need to have a good command of relational databases, such as Oracle and SQL, to manage and deal with enormous datasets. Or you may need to know non-relational databases. The major types of non-relational databases are Column: Cassandra, HBase; Document: MongoDB, CouchDB; Key value: Redis, Dynamo.
Operating System Background for Linux
Linux has commonly used in data science-related fields. It is much more stable and efficient in processing huge datasets than other operating systems. It will be an advantage if you have knowledge about Linux and can deploy a Spark distributed system on Linux.
Big Data Processing Frameworks
Big data processing frameworks can be classified into 3 categories: batch-only, stream-only and hybrid. You may be familiar with some well-known frameworks such as Hadoop, Storm, Samza, Spark, Flink. Normally, those processing frameworks compute over the data, extracting valid information and insights from gigantic datasets.
Hadoop and Spark are recognized as the most implemented processing frameworks. Hadoop is good for those not time-sensitive batch workloads, and it is less expensive than others to implement. Yet, Spark, a hybrid framework, provides higher speed for batch professing and micro-batch processing for streams.
Communication & Interpretation Skills
For a good data miner, apart from proficient data analyzing skills, the ability to communicate and interpret the results and outcomes is also necessary. Many audiences may not have a technical background, so it is necessary that you can explain the results in an understandable way and give your audience insights.
Relevant Work/Project Experience
According to David Robinson, the Chief Data Scientist at DataCamp, when he was asked how to start his first job in the field of data science, he mentioned that the most effective way for him was doing public work. He wrote blogs and did many open-source projects during his Ph.D., and all of these practices contributed to his proficiency in data science skills. Therefore, we can say that relevant project experience is essential too since it can help sharpen your skills, and put what you have learned into practice.
If you want to gain experience in data mining, here are 12 Most Popular Data science Programs Platforms for you to find out the best projects.
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