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Why DataMites Infographic


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"


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


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





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.


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.

Data mining is the process of discovering patterns, correlations, and relationships within large datasets to extract valuable insights and knowledge. It involves using various techniques and algorithms to analyze and interpret data, enabling organizations to make informed decisions, identify trends, and gain a competitive advantage.

The main steps in the data mining process typically include:

  • Data collection: Gathering relevant data from various sources.
  • Data preprocessing: Cleaning, transforming, and integrating the data for analysis.
  • Exploratory data analysis: Understanding the data through statistical summaries, visualization, and preliminary insights.
  • Data modeling: Selecting appropriate algorithms and building predictive models.
  • Evaluation: Assessing the model's performance and validity.

Common data mining techniques include:

  • Classification: Assigning data instances to predefined classes or categories.
  • Clustering: Grouping similar data instances based on their characteristics. 
  • Association rule mining: Discovering patterns and relationships between variables.
  • Regression analysis: Predicting numerical values based on historical data. 
  • Anomaly detection: Identifying abnormal or outlier data points.

Data mining plays a crucial role in predictive analytics and forecasting by leveraging historical data to make predictions about future events or trends. By analyzing patterns, relationships, and trends within the data, data mining algorithms can develop predictive models that help organizations make informed decisions and anticipate future outcomes.

Some common challenges and limitations in data mining include: 

  • Data quality and reliability issues.
  • Data privacy and security concerns. 
  • Dealing with large and complex datasets. 
  • Finding the right data mining techniques for specific problems. 
  • Interpreting and validating the results of data mining models.

Data mining enables organizations to segment their customers based on various criteria such as demographics, purchasing behavior, preferences, and past interactions. By analyzing these patterns, organizations can tailor their marketing strategies to specific customer segments, personalize product recommendations, optimize pricing strategies, and improve overall customer satisfaction.

Data mining is extensively used in fraud detection and prevention. By analyzing large volumes of transactional data and identifying patterns, anomalies, and outliers, data mining algorithms can flag suspicious activities indicative of fraudulent behavior. This helps organizations take proactive measures to prevent fraud, minimize financial losses, and protect their assets.

When conducting data mining, it is important to consider ethical implications such as:

  • Ensuring data privacy and obtaining informed consent from individuals. 
  • Applying fair and unbiased practices, avoiding discrimination or profiling. 
  • Safeguarding sensitive information and protecting against data breaches. 
  • Providing transparency and explaining the purpose and implications of data mining to stakeholders. 
  • Adhering to relevant laws, regulations, and industry standards.

Data mining in healthcare and medical research enables the analysis of large datasets to identify patterns and correlations, leading to improved diagnoses, treatment effectiveness, disease surveillance, and personalized medicine.

The salary of a data mining in India ranges from INR 1,500,000 per year according to an report.

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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.

To enroll in a Data Mining course at DataMites, participants should have basic knowledge of statistics and a fundamental understanding of programming concepts. Familiarity with concepts like databases and data handling is beneficial.

The Data Mining course at DataMites is thoughtfully structured to provide a step-by-step learning experience. It covers both theoretical concepts and practical applications of Data Mining techniques, allowing participants to gain a holistic understanding of the subject.

The Data Mining course at DataMites is thoughtfully structured to provide a step-by-step learning experience. It covers both theoretical concepts and practical applications of Data Mining techniques, allowing participants to gain a holistic understanding of the subject.

The Data Mining course curriculum at DataMites typically covers a wide range of topics, including data preprocessing, data exploration, association rule mining, classification, clustering, regression, text mining, and more. Advanced concepts such as ensemble learning and deep learning may also be included.

Yes, DataMites emphasizes hands-on learning and offers practical exercises and projects as part of their Data Mining course. These practical sessions enable participants to apply their knowledge and gain practical experience in real-world scenarios.

DataMites utilizes popular tools and software used in the industry for Data Mining, such as R, Python, Weka, RapidMiner, and SQL. Participants will gain proficiency in using these tools to perform data analysis, modeling, and visualization.

DataMites offers practical assignments, projects, and case studies that simulate real-world scenarios. Instructors provide guidance and support throughout the course to help students apply Data Mining techniques effectively in different domains.

The duration of the course spans over 3 months and provides 5 capstone project and 1 client projects.

Yes, DataMites provides certifications upon successful completion of the Data Mining course. These certifications validate the participants' knowledge and skills in Data Mining and can enhance their career prospects.

DataMites offers career support and job placement assistance to students after the completion of the course. This includes resume building, interview preparation, and connecting students with job opportunities in relevant industries.

Yes, DataMites provides online learning options for their Data Mining course. Students can choose self-paced learning or instructor-led online classes, allowing flexibility in learning according to individual schedules.

At Datamites, the course fee for a Data Mining course is known for its affordability, making it accessible to a wide range of individuals who are interested in pursuing a career in this field. The live virtual training for the course has a starting fee of Rs 34,000, while the blended learning option is available at a price starting from Rs 20,000. For those opting for classroom training, the starting price is Rs 42,000.

The mode of Payment are- 

  • Net Banking
  • Check
  • Cash
  • Debit Card
  • Credit Card
  • PayPal
  • Visa
  • Master card
  • American Express

Choosing Datamites for a data mining course is a wise decision due to their comprehensive curriculum, experienced trainers, and practical approach, providing students with the knowledge and skills necessary to excel in the field and enhance their career prospects in the data mining industry.

The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -

  • 1. Job connect
  • 2. Resume Building
  • 3. Mock interview with industry experts
  • 4. Interview questions

The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.

No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.

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