DATA MINING TRAINING COURSE IN INDIA

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

Data mining involves the exploration and analysis of extensive raw data to uncover patterns and extract valuable insights. Businesses leverage data mining software to gain deeper insights into their customer base, aiding in the development of more efficient marketing strategies, boosting sales, and reducing costs.

Data mining involves uncovering anomalies, patterns, and correlations within extensive datasets to anticipate outcomes. Employing a diverse array of techniques, this information can be harnessed to enhance revenues, reduce costs, cultivate customer relationships, mitigate risks, and achieve various other benefits

STATISTICA Data Miner organizes the modeling screen into four primary stages of data mining: (1) acquiring data; (2) cleaning, preparing, and transforming data; (3) analyzing, modeling, classifying, and forecasting data; and (4) generating reports.

To be eligible for admission to the Postgraduate Program in Data Mining, candidates must possess a bachelor's degree from a recognized university in science, computer engineering, or a BTech degree.

Data mining specialists represent a specific expertise within the expansive realm of data science careers, which is currently one of the fastest-growing fields worldwide. According to the U.S. Bureau of Labor Statistics, there is a projected 36% growth in data science occupations anticipated through the year 2031.

Understanding data mining is commonly seen as a complex endeavor. Nevertheless, acquiring proficiency in this crucial field of data science is more manageable than it may initially seem.

Data specialists need statistical knowledge and some programming language knowledge to complete data mining techniques accurately. For instance, here are some examples of how companies have used R to answer their data questions.

Consequently, SQL Server is currently employed for conducting Data Mining tasks as it incorporates data necessary for making predictions. Nowadays, rather than relying on programming languages such as Python and R, you can utilize SQL for the collection, filtering, and analysis of data, fostering business growth.

In essence, the practice of data mining necessitates the amalgamation of methodologies from various fields, encompassing statistics, mathematics, machine learning, database technology, data visualization, pattern recognition, signal processing, information retrieval, and high-performance computing.

The collaboration between data mining and AI is pivotal in driving positive impacts on your data-related initiatives, consequently benefiting your organization as a whole.