Data mining is a procedure of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use. Data mining is the analysis step of the “knowledge discovery in databases” process or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.
The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data; in contrast, data mining uses machine learning and statistical models to uncover covert or hidden patterns in a large volume of data. At heart, data analytics is all about solving problems. By learning data mining, you can gain problem-solving skills, analytical skills, and much more.
Following are some of the best online data mining courses available to you:
1. Data Mining with Weka – FutureLearn
The course Data Mining with Weka can help you discover practical data mining and learn to mine your data using the widespread Weka workbench. This course’s duration is five weeks with three hours of weekly learning, and the whole course can be taken online. You can learn it at your own pace with extra benefits from $49. This course is part of the Applied Data Mining program, which will help you to become a data mining expert through a few short courses. Learn how to mine your own data because today’s world generates more data than ever before. Through this course, you will be able to turn it into useful information as it introduces you to applied data mining using the Weka workbench. This course is for anyone who deals in data, and it involves no computer programming, but you still need some experience with using computers for everyday tasks. Also, high school maths will be more than enough to understand data mining, and you will need an understanding of some basic statistics concepts like variances and means.
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2. Data Mining Specialization – Coursera
Data Mining Specialization is taught by the University of Illinois at Urbana-Champaign. The skills you will gain are Data Mining, Data Visualization Software, Data Clustering Algorithms, Text Mining, Data Visualization (DataViz), Tableau Software, Data Virtualization, Information Retrieval (IR), Document Retrieval, Machine Learning, Recommender Systems, and Probabilistic Models. The Data Mining Specialization teaches techniques for both structured data and unstructured data. Specific course topics include text retrieval, pattern discovery, clustering, text mining and analytics, and data visualization. The main mission of this project is to solve real-world data mining challenges using a restaurant review data set from Yelp. There are 6 Courses in this Specialization: Text Mining and Analytics, Data Visualization, Text Retrieval and Search Engines, Pattern Discovery in Data Mining, Cluster Analysis in Data Mining, and Data Mining Project.
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3. Intro to Analytic Thinking, Data Science, and Data Mining – Coursera
Intro to Analytic Thinking, Data Science, and Data Mining course will begin with an assessment of the field and occupation of data science with an emphasis on the skills and ethical considerations required when working with data. You will review the types of business problems data science can solve and discuss the application of the process to data mining efforts. A brief overview of Predictive, Descriptive, and Prescriptive Analytics will be provided, and you will conclude the course with an activity to learn more about the tools and resources you might find in a data science toolkit. You will learn the knowledge and abilities needed to work in the profession, how data science is used to solve business problems and the benefits of using the cross-industry standard process for data mining (CRISP-DM). The skills you will gain are Environmental Data Analysis, Data Documentation, Geophysical Data, and Data Mining.
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4. Fundamentals of Data Mining – University of California San Diego
Fundamentals of Data Mining course can help you with the ever-increasing volume of research and industry data collected on a daily basis. Veteran data scientists are needed to process and filter the data, detect new anomalies or patterns within the data, and gain in-depth insight from the data. This course provides students with a basis in basic data mining, data analysis, and predictive modelling algorithms and concepts. Using practical exercises, students can learn data analysis and machine learning techniques for knowledge and model creation through a process of inference or learning from examples. In this course, you can gain practical experience through hands-on data mining projects. Statistics for Data Analytics or something with similar working knowledge is required. Skills regarding Linear Algebra for Machine Learning is also recommended but not required. You can test your level of statistical knowledge by taking the online Self-Assessment quiz.
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5. Data Mining for Advanced Analytics – University of California San Diego
Data Mining for Advanced Analytics Program is a specialized certification course offered by UCSD. It provides you with the skills for predictive data models to make data-driven decisions in any industry. Modern databases contain massive amounts of data and within this data lies important information that can only be successfully analyzed using data mining. Data mining tools/techniques can predict future trends and behaviours, allowing individuals and organizations to make active, knowledge-driven decisions. Newly updated with additional data sets and additional algorithms, in this program, you will use real-life data sets from numerous industries to complete projects. You will learn to plan and perform all the steps of data processing and modelling and the predictive/descriptive model. Targeted elective courses will allow you to learn further techniques, tools, and languages.
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6. Mining Massive Datasets – edX
Mining Massive Datasets course is based on the text Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, and Jeff Ullman, who by coincidence are also the instructors for the course. This course is estimated to take seven weeks for completion, with five to ten hours invested every week. It is a self-paced course that means you can progress at your own speed. Also, there is a free optional upgrade available in this course. You will learn PageRank and Web-link analysis, Frequentitemset analysis, MapReduce systems and algorithms, Locality-sensitive hashing, Algorithms for data streams, Clustering, Computational advertising, Recommendation systems, Social-network graphs, Dimensionality reduction, and Machine-learning algorithms. The course is intended for graduates and advanced undergraduates in Computer Science. At a minimum, you should have had courses in Data structures, Multivariable calculus, Algorithms, Database systems, Linear algebra, and Statistics.
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7. Data mining with Rattle – Udemy
Data mining with Rattle course can help you learn about terms like data mining, evaluating data mining model, rattle, machine learning, classification, all about data, and data mining tool. This course contains twelve sections with twenty-two lectures that are almost two hours in total length. All you need to get started is a computer and the basics of data mining. In this course, you will learn about a Graphical User Interfacenamed Rattle that is an interactive tool for data mining.Data mining is the analysis of data and using software techniques to find patterns and regularities in sets of data. The computer is responsible for locating the patterns by identifying the underlying features in the data.
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8. Data Mining for Business in Python 2021 – Udemy
Data Mining for Business in Python 2021course has nine data mining algorithms for Data Science, Machine Learning and Explainable Artificial Intelligence. Also, it has eighteen case studies and can help you learn terms like Survival Analysis, Association Rule Learning, Random Forest, LIME, SHAP, Cox Proportional Hazard Regression, CHAID, Cluster Analysis – Gaussian Mixture Model, Data Mining, Principal Component Analysis, XGBoost, and Manifold Learning. The only things you require to start learning is knowledge of statistics – linear and logistic regression and basic python. In the age of neverendingspreadsheets, it is easy to feel overwhelmed with so much data, and this is where data mining can be extremely useful to swiftly analyze, find patterns, and deliver an outcome to you. The Data Mining value added is that you stop the number crunching and pivot table creation, leaving time to come with actionable plans based on the insights.
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9. Data Mining and Analysis – Stanford Online
Data mining is a potent tool used to discover relationships and patterns in data. Learn how to apply data mining fundamentals to the dissection of data sets and explore, analyze and leverage data. You can learn how to turn it into valuable, actionable information for your company.Due to the limited space in this course, interested students should enrol as soon as possible.You need to know the following for this course: Introductory courses in statistics or probability (STATS60 or equivalent), computer programming (CS105 or equivalent), linear algebra (MATH51 or equivalent), and a Bachelor’s degree with a UG GPA of 3.3 or better.Topics of this course includeDecision trees, Association rules, Clustering, Case-based methods, and Data visualization.
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10. Data Mining – The Ohio State University College of Engineering
Data Mining is one of the non-credit courses in the CPDA program, and it can be taken individually or as one of the other courses required to receive the CPDA certificate of completion.Data Mining is the second course in the sequence of the CPDA program. After learning how to analyze data statistically, students learn how to sort through large datasets to identify trends, patterns, and relationships and discover insights previously unknown, and leverage them in business operations. The course is delivered in 100% distance-learning format and includes instructional material equivalent to a one-semester credit hour class. This course covers data mining basics and algorithms. Students will develop an appreciation for data transformation and preparation, an understanding of the data requirements for the different algorithms and learn when it is appropriate to use which algorithm. This is a project-based course where students solve their business problem through a data mining methodology. Specific topics also include Classification, Distance/Similarity Measurement; Anomaly Detection; and Association, Clustering, and Pattern Algorithms.
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