In the ever-evolving landscape of data science, staying ahead of the curve is essential for professionals and enthusiasts alike. The year 2023 promises to be a milestone for data mining education, with a plethora of courses designed to equip you with the knowledge and skills needed to navigate the intricate world of data extraction, analysis, and interpretation.
In this comprehensive blog, we’ll look into 4 top-notch data mining courses offered in 2023, catering to individuals with varying levels of expertise and interests.
Level: Beginner
Duration: Approx 21 hours
Fee: Paid but check for offers as it can be as low as ₹449 or ₹750/month
What you’ll learn: Data Mining in Tableau, Data wrangling, Data visualization, SQL programming, Data modeling and Machine learning, Communication
Description:
In this course, you'll gain firsthand experience in the challenges regularly faced by Data Scientists. You’ll get to know and solve issues like data corruption, anomalies, and irregularities.
This course offers a comprehensive overview of the Data Science journey. By the time you finish, you'll have learned:
1. Effective methods for cleaning and preparing data for analysis.
2. How to create basic data visualizations.
3. Data modeling techniques.
4. Curve-fitting strategies for your data.
5. Techniques for presenting your findings that will captivate your audience.
Additionally, you will develop a solid understanding of essential tools, including:
SQL, SSIS, Tableau, Gretl
Who this course for:
Level: Intermediate level
Duration: 3 month (10 hours/week)
Fee: Free to audit, only upgrade for certificates with financial aid available
What you’ll learn: Data Clustering Algorithms, Text Mining, Data Visualization (DataViz), Data Mining
Description: This Data Mining Specialization consists of 6 courses which covers a range of data mining techniques applicable to structured data with well-defined schemas and unstructured data in natural language text format. It delves into various course subjects, such as uncovering patterns, clustering, text retrieval, text mining and analytics, and data visualization. As the final project, the Capstone entails tackling authentic data mining issues using a dataset comprised of restaurant reviews from Yelp.
Here are the 6 courses in this specializations:
2.Text Retrieval and Search Engines
4.Pattern Discovery in Data Mining
5.Cluster Analysis in Data Mining
Who this course for: Anyone interested in the fields of Data Science and AI however one should note that this course is labeled as intermediate level and does not require prior experience. Thus, even beginners can easily take up this course.
Level: Intermediate
Duration: 6 weeks(6-10 hours/week)
Fee: Free to audit
What you’ll learn: clustering techniques, classification techniques, frequent pattern mining and data warehouse techniques etc
Description: In recent times, data mining has gained prominence as a significant area of research and practical applications. Its primary objective is to extract valuable and intriguing insights from vast data repositories like databases and the internet. Data mining amalgamates methodologies from the domains of databases, statistics, and artificial intelligence (AI) to achieve this goal.
Who this course for: This course expects you to know algorithms to get started as you’ll be working with various algorithms like clustering, classification and for frequent pattern mining knowledge of Apriori algorithm, ECLAT algorithm, and FP-growth algorithm would be great help to you.
Level: Advanced
Duration: Approx. 7 weeks(5-10 hours/week)
Fee: Free to audit
What you’ll learn: 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, Machine-learning algorithms
Description: The course draws its foundation from the book "Mining of Massive Datasets" authored by Jure Leskovec, Anand Rajaraman, and Jeff Ullman, who interestingly happen to be the instructors for the course as well.
The key subjects explored in this course encompass various areas such as MapReduce systems and algorithms, Locality-sensitive hashing, Techniques for handling data streams, PageRank and Web-link analysis, Frequent itemset analysis, Clustering, Computational advertising, Recommendation systems, Social-network graphs, Dimensionality reduction, and Machine-learning algorithms.
Who this course for: The course targets graduate students and advanced undergraduates specializing in Computer Science. As prerequisites, it is expected that participants possess a foundational understanding of topics such as Data structures, Algorithms, Database systems, Linear algebra, Multivariable calculus, and Statistics.
In conclusion these courses will cover the fundamentals as well as advanced data mining concepts to get you started in this field. I would highly emphasize practicing as you go along the course on platform like Kaggle to hone these skills. Till then happy learning!
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