How to get Free and Paid Data Mining Courses Online

How to get Free and Paid Data Mining Courses Online

Data Mining Courses Online: This Specialization will allow you to apply the learned algorithms and techniques for data mining from the previous courses in the Specialization, including Pattern Discovery, Clustering, Text Retrieval, Text Mining, and Visualization, to solve interesting real-world data mining challenges. Specifically, the design of the Project emphasizes:

[su_list icon=”icon: renren” icon_color=”#870b0b” indent=”12″]Simulating the workflow of a data miner in a real job setting Integrating different mining techniques covered in multiple individual courses Experimenting with different ways to solve a problem to deepen your understanding of techniques Allowing you to propose and explore your ideas creatively.[/su_list]

Learn Data Mining with free online courses and MOOCs from the University of Illinois at
Urbana-Champaign, Stanford University, Eindhoven University of Technology, Yonsei
University and other top universities around the world.
Best Free and Paid Data Mining Courses Online
Here are some best free and paid data mining courses that will help you add to your
qualifications and certificates to make you stand out and more valuable.
Data Mining Certification by Illinois (Coursera)
Data mining has become a front runner in tackling challenges earlier assumed to be
unapproachable and has become one of the most in-demand careers. In this specialization, you
will step by step look into key topics like text retrieval, pattern recognition, analytics, and
visualization. You will get the opportunity to work with both structured and unstructured data.
With over 23,000 students and glowing reviews, it is safe to say that this series of programs is a
crowd favourite.
  Get a brief overview of the technical jargon and fundamental methodologies.
 lore concepts used in search engines and their applications.
 tice with assignments that deal with huge scales of information.
 earn about clustering algorithms and evaluation techniques.
 rate your acquired knowledge to finish the real-life based capstone project.

 Data Mining Courses (Udemy)
On this platform, you will find over 35 courses and tutorials dedicated to the area of data mining.
Use languages like Python and R to scrape, mine and analyze datasets and apply ML-based
algorithms. You can take the quiz present on the website to determine the programs that are
following your learning goals and experience level.
 inner level classes require no prior knowledge of the topics covered.
 follow along the installation guide to set up the necessary software and tools.
 rate and structure data from different sources.
 form statistical analysis draws an inference, and understand how variables are related.
 lore topics like natural language processing, text mining, sentiment analysis and more.
 ures + Exercises + Downloadable resources + Full lifetime access

 Programs and Certifications on Data Mining (edX)
edX show you how to upgrade your existing set of skills in working with mining tools, and
techniques. If you are starting from scratch then take the lectures focusing on the fundamental
terminologies and basic methodologies before moving on to predictive analytics, statistical and
probabilistic modelling. Whereas in the advanced programs you will work with complex mining
algorithms and utilize them to solve challenges faced in real-life scenarios.

  • Work with R, Hadoop, Spark, Java, and Python.
  • Over regression analysis and statistics thoroughly to follow along without any problems.
  • Professional certificates, micro masters, and individual courses.
  • Share the insights gained from the processing and evaluation performed.
  • Get answers to your queries by reaching out to the mentors.

 Online Data Mining Courses (Harvard University)

The huge demand for talent in data mining, this top academic institution offers two programs focusing on required industry knowledge and the science behind it. In the classes under the topic of wrangling, you will go through the elaborate process of importing datasets from different sources, cleansing and preparing them for further analysis. The other course focuses on the non- technical and business aspects of this field such as how you can meet customer expectations and
forecast market trends.

lore the top methods preferred by scientists and industry leaders.
form web scraping, use the package and work with R.
Get recommendations for additional resources to supplement your learning.
Earn academic credit by finishing the graded assignments and exams with a score above
the cut-off.

Data Mining and Analysis (Stanford Online)

Foundations of programming, statistics, probability, linear algebra and are looking for a training that will help you to build upon that knowledge then do check out this certification by Stanford University. Leverage the power of the principles and techniques discussed to deal with large amounts of data and gain meaningful insight relevant to business goals. Get the statistical package for R, Excel software and you are good to go.

  • Discuss decision trees, methods for handling case-based problems.
  • hands-on assignments available for practice and helps you to identify your weak areas.
  • Sent your insights and observations using visualizations by focusing on the key
    parameters.
  • The instructor shares tips and advice acquired from their experience.
  • additional resources are suggested.
  • Earn academic credit by completing the coursework and exams.

Mining Massive Datasets (Stanford University via edX)

The text Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, and Jeff Ullman, who by coincidence are also the instructors for the course. The book is published by Cambridge Univ. Press, but by arrangement with the publisher, you can download a free copy here. The material in this on-line course closely matches the content of the Stanford course CS246. The major topics covered include

MapReduce systems and algorithms, Locality-sensitive hashing, Algorithms for data streams, PageRank and Web-link analysis, Frequent itemset analysis, Clustering, Computational advertising, Recommendation systems, Social-network graphs, Dimensionality reduction, and Machine-learning algorithms.

Text Mining and Analytics (the University of Illinois at Urbana-Champaign via Coursera)

The major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort.

Analysis of text data requires an understanding of natural language text, which is known to be a difficult task for computers. However, several statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and
their potential applications.

Pattern Discovery in Data Mining (the University of Illinois at Urbana-Champaign via
Coursera)

Data mining along with basic methodologies and applications is very useful. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining.

We will also introduce methods for data-driven phrase mining and some interesting applications of pattern discovery.

This course provides you with the opportunity to learn skills and content to practice and engage in scalable pattern discovery methods on massive transactional data, discuss pattern evaluation measures, and study methods for mining diverse kinds of patterns, sequential patterns, and sub-graph patterns.

More Data Mining with Weka (University of Waikato via Independent)

Data Mining with Weka and provides a deeper account of data mining tools and techniques. Again the emphasis is on principles and practical data mining using Weka, rather than mathematical theory or advanced details of particular algorithms.

Students will work with multimillion-instance datasets, classify text, experiment with clustering, association rules, neural
networks, and much more.

Advanced-Data Mining with Weka (University of Waikato via Independent)

This course follows on from Data Mining with Weka and More Data Mining with Weka. It provides a deeper account of specialized data mining tools and techniques.

Again the emphasis is on principles and practical data mining using Weka, rather than mathematical theory or advanced details of particular algorithms.

Students will analyse time-series data, mine data streams, use Weka to access other data mining packages including the popular R statistical computing language, script Weka in Python, and deploy it within a cluster computing framework.

The course also includes case studies of applications such as classifying tweets, functional MRI data,
image classification, and signal peptide prediction. Data Mining Capstone (the University of Illinois at Urbana-Champaign via Coursera) The Capstone Project is to analyze and mine a large Yelp review data set to discover useful knowledge to help people make decisions in dining.

The project will include the following
outputs:
Opinion visualization: explore and visualize the review content to understand what people have
said in those reviews.
Cuisine map construction: mine the data set to understand the landscape of different types of
cuisines and their similarities.
Discovery of popular dishes for a cuisine: mine the data set to discover the common/popular
dishes of a particular cuisine.
Recommendation of restaurants to help people decide where to dine: mine the data set to rank
restaurants for a specific dish and predict the hygiene condition of a restaurant.
From the perspective of users, a cuisine map can help them understand what cuisines are there
and see the big picture of all kinds of cuisines and their relations.

Data Mining (Indian Institute of Technology, Kharagpur and NPTEL via Swayam)

Data mining is a study of algorithms for finding patterns in large data sets. It is an integral part of modern industry, where data from its operations and customers are mined for gaining business insight. It is also important in modern scientific endeavours.

Data mining is an interdisciplinary topic involving, databases, machine learning and algorithms. The course will cover the fundamentals of data mining.

It will explain the basic algorithms like data preprocessing, association rules, classification, clustering, sequence mining and visualization. It will also

explain implementations in open-source software. Finally, case studies on industrial problems will be demonstrated. Data Mining: Theories and Algorithms for Tackling Big Data (Tsinghua University via edX) aspiring data scientists find it surprisingly difficult to locate an overview that blends clarity, technical depth and breadth with enough amusement to make big data analytics engaging. This
course does just that.
Each module starts with an interesting real-world example that gives rise to the specific research
question of interest. Students are then presented with a general idea of how to tackle this problem along with some intuitive and straightforward approaches.

Finally, several representative algorithms are introduced along with concrete examples that show how they function in practice. While theoretical analysis sometimes overcomplicates things for students, where it’s applied to help them better understand the key features of the techniques.

Business analytics and data mining Modeling using R (Indian Institute of Technology
Roorkee and NPTEL via Swayam) The objective of this course is to impart knowledge on the use of data mining techniques for deriving business intelligence to achieve organizational goals. Use of R (statistical computing CSS – MOOCs Proposal software) to build, assess, and compare models based on real datasets
and cases with an easy-to-follow learning curve.

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