About DATA MINING TRAINING

This course "Data Mining", is designed for candidates with or without programming skills, with basics of Data importing and Data mugging along with effective programming techniques. This also includes Python Data Science challenges kit, enabling the candidates to not only understand Python core concepts but also gain practical mastery over Data Mining with Python, which is very much in demand in Today's Data Science job opportunities.

DATA MINING TRAINING COURSES

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Description

Become an expert in this lucrative "Data Mining" field with DataMites's training program. This Data Mining program provides requisite knowledge on concepts of Data analysis using R language and also the techniques of Machine learning and Text Mining. These are the most premier and demanding concepts needed to analyse the huge volume of data from different perspectives to gain better business insights. By choosing this DataMite's "Data Mining" course, it allows you to set your complete focus on these specified topics, R language, Machine learning and Text Mining to gain a specialisation.

Your big career move is all set now, as this Data mining course focuses on specialisation of skill set involving the techniques of Machine learning, Text Mining using the R programming language. Opting this course would be a perfect idea to explore, analyse and leverage data in order to arrive at valuable information for your company.

After successful completion of this "Data Mining" course, you should have acquired following skills

  • A complete knowledge about the R tool starting from the scratch of installing the tool. Acquire a thorough knowledge of the tool, also gain a better insight of Data manipulation as well as Data visualisation using the tool.
  • Gained an in-depth knowledge on this artificial intelligence Machine learning, which is a much demanded one in all the companies for an effective and powerful analysing of their business data. Both the foundation and Expert level are covered in this program.
  • Have attained the knowledge of Text mining and the major techniques used for mining and analysing the text data to derive at useful knowledge for the company. Also would have learned the statistical approach handled on text data with no or minimum human effort.

Data Mining is a powerful tool that many companies want to adapt in order to increase the accuracy of results they arrive from their raw data. Since the companies are readily interested in applying these new technologies for their data analysis, there is already a steep growth of opportunities in this field. Though the data mining concept is not pretty new and has been there for decades, it is becoming popular because of increased amount of data, fast processing and reduced cost in storage. As a Data Analytics professional, after completing this course you would see an increased growth in your career.

  • Any professionals who are aspiring to make a career in Business analytics using R language in Text Mining and Machine learning can choose this course.
  • Even as a beginner, if you want to take a specialisation by learning R language along with text mining and machine learning then start right away with this course.
  • Even as a beginner, if you want to take a specialisation by learning R language along with text mining and machine learning then start right away with this course.

"Our search for world class career ended at DataMites" is what, quoted by all of our candidates. Only experts with real time experience and better understanding of concepts can teach the candidates well. True, we have world class faculties who can make you understand even the toughest concepts at ease. DataMite's certification that you acquire after completing this course has industry recognition and adds weightage to your resume.

Syllabus

Introduction to R:

  • Installing R
  • Installing R Studio
  • Creating Objects in R
  • Creating Arrays
  • Creating Data frames
  • Use of Structure
  • Dimensions
  • Loading CSv files, Foreign packages into R

Data Manipulation with R:

  • Loading vectors in R
  • Combining to vectors in R
  • Cleaning Data with R, Swapping Data, Sorting Data, Converting unstructured to structured data, usage of sub, gsub, regexpr, gregexpr, apply, lapply, sapply

Data Visualization with R:

  • Usage of Plot, lines, boxplot, stars, barplot, pie, hist, rug, sunflowerplot, various color of histograms, tabplot, ggplot2, maptools and extracting data from URLs

The following topics are covered in "Machine Leaning"

Foundation:

Machine Learning Introduction: Supervised and Unsupervised Learning

  • Linear Regression Theory
  • Linear Regression Programming with R
  • Working on Case Study

Multiple Linear Regression

  • Theory behind multiple linear regression
  • Multiple Linear Regression with R
  • Working on Case Study

Decision Tree:

  • Theory Behind Decision Tree
  • Decision Tree with R
  • Working on Case Study

Naive Bayes:

  • Theory behind Naïve Bayes classifiers
  • Naive Bayes Classifiers with R
  • Working on Case Study

Support Vector Machines:

  • Theory behind Support Vector Machines
  • Support vector machines with R
  • Improving the performance with Kernals
  • Working on Case Study

Association Rule:

  • Theory behind Association Rule
  • Working on Case Studies

Expert:

Neural Net:

  • Artificial Neural Network
  • Connection Weights in Neural Network
  • Generating Neural Network with R
  • Improving Neural Network Accuracy with Hidden Layers
  • Working on Case

Random Forest:

  • Theory behind Random Forest
  • Random Forest with R
  • Improving performance of Random Forest
  • Working on Case Study

Recommendation Engine:

  • Theory behind Recommendation Engines
  • Working on Case Study with R

Dimension Reduction:

  • Theory behind Recommendation Engine
  • Working on Case Studies

The topic covered in "Text Mining" are

Text Mining:

  • Introduction to Text Mining concepts
  • Sentiment Analysis with R/li>
  • Positive and Negative Word Cloud
  • Case study on Sentiment analysis

Advanced Regression

  • Theory Behind Advanced Regression
  • Advanced Regression with R
  • Working on Case Study

Web Analytics:

  • Theory behind Web Analytics
  • Working on Case Study

FAQ'S

Total course fee should be paid before 50% of the course completion. We also have EMI option tied up with bank. Check with coordinators.

No, Most of software are free and open source. The guidelines to setup software is a part of course.

Certified Data Scientist is delivered in both Classroom and Online mode. Classroom is provided in selected cities in India such as Bangalore, Hyderabad.

All the online sessions are recorded and shared so you can revise the missed session. For Classroom, speak to the coordinator to join the session in another batch.

We have a dedicated PAT (Placement Assistance team) to provide 100% support in finding your dream job.

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