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Coursera is a well known and popular MOOC teaching platform that partners with top universities and organizations to offer online courses.
A typical course at Coursera includes pre recorded video lectures, multi-choice quizzes, auto-graded and peer reviewed assignments, community discussion forum and a sharable electronic course completion certificate.
You can study the course materials for free.
But you have to pay if you want course certification and peer graded assignments.
Some Coursera courses facilitate onetime payment fee that lasts for 180 days.
If you enrol to courses that are part of a specialization, you have to opt for monthly subscription fee to have access to the courses grades and certificate.
Coursera hosts around a good number of courses, specializations, certificate programs and master’s degree in the field of data science.
All these courses are curated and taught by professors from world’s best universities.
What is the difference between the data science coursera and data mining course coursera?
Coursera has two separate specialization courses on data science and data mining and both caters to a different audience in the field of data analysis.
Coursera Data Science Specialization:
Offered by John Hopkins University, it is more of introducing yourself to the field of data science. It talks about data collection, production of data science products and implementing these data science process in R. It is a 10 course program that aims to teach you data analysis in R. Since it use R, this specialization will be an added advantage for someone coming from a stats background
Coursera Data Mining Specialization:
Offered by Illinois University, it focuses mainly on the data mining techniques such as text mining, pattern recognition, cluster analysis, etc that data scientist’s use. There are total 6 courses that will give you an in-depth knowledge into the field of data mining for predicting data patterns.
You can call data mining a subset of the data science field where you are looking to discover patterns from a large datasets that you are working on.
So if you are planning to start a career as a data scientist, you need to have understanding in data mining techniques as well.
The best option could be to study JHU data science specialization and then opting for Coursera data mining Illinois to get even deeper insights.
What are the best courses related to data mining in coursera?
Below are our best picks of Coursera Data mining courses in terms of course reviews and feedback.
Using Databases with Python
Offered by the University of Michigan, this is part of the 5 course series of Python for Everybody Specialization.
You can audit the course material for free or add a verified certificate for $79.
The course use SQlite3 as its database for designing data models and focuses on how to retrieve, process and visualize data with Python.
Course Ratings: 4.8+ from 10,988+ students
Key Learning’s from the Course:
- Explain the basics of Object Oriented Python
- Use the create, read, update and delete operations to manage databases
- Understand how data is stored across multiple tables in a database
- Utilize the Google Maps API for data visualization
Who is this course best suited? Excellent Coursera data mining Python course if you want to learn how to connect Python with database
Skills Gained from the course: Python Programming, Database (DBMS), SQlite and SQL
Course Reviews:
Machine Learning: Clustering & Retrieval
This is the last course of the popular machine learning specialization offered by University of Washington.
The course uses two popular data mining technique (Clustering and retrieval) to group unlabeled data and retrieve items of similar interests with case studies.
Course Ratings: 4.6+ from 1578+ students
Key Learning’s from the Course:
- Create a document retrieval system using k-nearest neighbours
- Identify various similarity metrics for text data
- How to use KD –trees to reduce computations
- How to use locality sensitive hashing
- Supervised Vs Unsupervised learning tasks
- Know cluster documents using k-means
- Use MapReduce to parallelize k-means
- Use Mixtures models to examine probabilistic clustering approach
- How to fit a Gaussian model using EM (Expectation Maximization)
- Learn how to use LDA (latent Dirichlet allocation) to perform mixed membership modeling
- How to draw inferences using Gibbs sampler
- How to implement the above techniques in Python
Who is this course best suited? If you want to learn in depth on cluster analysis techniques with respect to data mining at Coursera
Skills Gained from the course: Data Clustering Algorithms, K-Means Clustering, Machine Learning and K-D Tree
Course Reviews:
Capstone: Retrieving, Processing and Visualizing Data with Python
The Coursera data mining capstone course shows learners how to build applications to retrieve, process and visualize data using Python.
This is the final course of the 5 course series of Python specialization offered by University of Michigan.
Course Ratings: 4.6+ from 3,930+ students
Key Learning’s from the Course:
- Create email data visualizations
- Make use of unicode characters and strings
- Select and process the data of your choice
- Understand the basics of building a search engine
Who is this course best suited? Learn data modelling, pagerank algorithm, web scrapping, email data parsing and dataviz with capstone projects.
Skills Gained from the course: Data analysis, Python programming, DBMS and DataViz
Course Reviews:
Data Analysis with Python
This massive data mining Coursera course is part of IBM data science professional certificate specialization.
It teaches you how to analyse data in Python starting from the Python basics and exploring different data sets with Python standard libraries.
Course Ratings: 4.6+ from 1,677+
Key Learning’s from the Course:
- Learn how to import datasets
- How to use Pandas to load, manipulate, analyse and visualise datasets
- How to perform data wrangling and data summarization
- Build machine learning regression models using Scikit-learn library
Who is this course best suited? For beginners wanting to master data analysis in Python
Skills Gained from the course: Predictive Modelling, Python Programming, Data Analysis, DataViz and Model Selection
Course Reviews:
Data Visualization with Python
This exclusive data visualization course from IBM teaches you effective tools and techniques to extract information, better understand data and make effective decisions out of a large dataset.
Course Ratings: 4.6+ from 1,079+ students
Key Learning’s from the Course:
- Introduction to Data visualization tools such as Matplotlib and its basic features
- Learn about area plots, histograms, bar charts, pie charts, box plots, etc with Matplotlib
- Advance visualization tools such as waffle charts and word clouds and how to create them
- How to use Seaborn to generate regression plots
- How to use Folium to visualise geospatial data, create choropleth maps, etc
Who is this course best suited? If you are looking to learn graphical techniques used in data science using Python packages.
Skills Gained from the course: Python Programming, Data virtualization, DataViz and Matplotlib
Course Reviews:
Process Mining: Data Science in Action
Course Ratings: 4.7+ from 481+ students
Key Learning’s from the Course:
- Understanding of business process intelligence techniques
- The role of Big Data and the relation between process mining techniques and other data analysis techniques
- Learn how to apply basic process discovery techniques
- How to apply basic conformance checking techniques to compare event logs and process models
- Data understanding with respect to process mining project
- How to conduct process mining projects in a structured manner
Who is this course best suited? If you want to lean key analysis techniques used in process mining
Skills Gained from the course: Petri Net, Process Modelling, Process Mining and Data Mining
Course Reviews:
Do you recommend any other Coursera Data Mining courses worth enrolling into? Let us know in the comments.
Happy Learning!
