Data Science Certification Training

GKLM helps you to become expertise in Machine Learning Algorithms like K-Means Clustering, Random Forest, Decision Trees, Naive Bayes by the help of learning Data Science. Here you will perform Big Data Analytics by using R Programming, Hadoop and solve real life case studies on Finance, E-Commerce, and Social Media.


About The Course

Data Science, is known as information driven science, is an interdisciplinary field about logical techniques, procedures and frameworks to concentrate learning or experiences from information in different structures, either organized or unstructured, like Knowledge Discovery in Databases (KDD).

Course Objectives

During the Data Science Certification Training

After the completion of the Data Science course at GKLM, you should be able to:

  1. Learn insight into the 'Roles' played by a Data Scientist.
  2. To do analysis on Big Data using R, Hadoop and Machine Learning.
  3. Know Data Analysis Life Cycle.
  4. Learn tools and techniques for data transformation.
  5. Work with different data formats like XML, CSV and SAS, SPSS, etc.
  6. Understand Data Mining techniques and their implementation.
  7. Learn to analyze data using machine learning algorithms in R.
  8. Know how to work with Hadoop Mappers and Reducers to analyze data.
  9. Do implementation various Machine Learning Algorithms in Apache Mahout.
  10. Have knowledge of data visualization and optimization techniques.
  11. Explore the parallel processing feature in R.


  1. Introduction to Data Science
  2. Introduction to Big Data, Roles played by a Data Scientist, Analyzing Big Data using Hadoop and R, Methodologies used for analysis, the Architecture and Methodologies used to solve the Big Data problems, For example, Data Acquisition from various sources, Data preparation, Data transformation using Map Reduce (RMR), Application of Machine Learning Techniques, Data Visualization etc., problem statement of few data science problems which we shall solve during the course.

  3. Basic Data Manipulation using R
  4. Understanding vectors in R, Reading Data, Combining Data, subsetting data, sorting data and some basic data generation functions.

  5. Machine Learning Techniques Using R Part-1
  6. Machine Learning Overview, ML Common Use Cases, Understanding Supervised and Unsupervised Learning Techniques, Clustering, Similarity Metrics, Distance Measure Types: Euclidean, Cosine Measures, Creating predictive models.

  7. Machine Learning Techniques Using R Part-2
  8. Understanding K-Means Clustering, Understanding TF-IDF and Cosine Similarity and their application to Vector Space Model, Implementing Association rule mining in R.

  9. Machine Learning Techniques Using R Part-3
  10. Understanding Process flow of Supervised Learning Techniques, Decision Tree Classifier, How to build Decision trees, Random Forest Classifier, What is Random Forests, Features of Random Forest, Out of Box Error Estimate and Variable Importance, Naive Bayes Classifier.

  11. Introduction to Hadoop Architecture
  12. Hadoop Architecture, Common Hadoop commands, MapReduce and Data loading techniques (Directly in R and in Hadoop using SQOOP, FLUME, and other Data Loading Techniques), Removing anomalies from the data.

  13. Integrating R with Hadoop
  14. Integrating R with Hadoop using RHadoop and RMR package, Exploring RHIPE (R Hadoop Integrated Programming Environment), Writing MapReduce Jobs in R and executing them on Hadoop.

  15. Mahout Introduction and Algorithm Implementation
  16. Implementing Machine Learning Algorithms on larger Data Sets with Apache Mahout.

  17. Additional Mahout Algorithms and Parallel Processing using R
  18. Implementation of different Mahout algorithms, Random Forest Classifier with parallel processing Library in R.

  19. Project
  20. Project Discussion, Problem Statement and Analysis, Various approaches to solve a Data Science Problem, Pros and Cons of different approaches and algorithms.



At the end of this training program there will be quizzes that perfectly reflect the type of questions asked in the respective certification exams and helps you score better marks in certification exam.
GKLM Tech Course Completion Certification will be awarded on the completion of Project work (on expert review) and upon scoring of at least 60% marks in the quiz.

Contact Us


+91 9999160255    +91 8595181398

FAQ's


There is data everywhere and in this data is hidden information and patterns that can provide knowledgeable insights to businesses and research. Data science is basically the informed and systematic extraction of relevant information or knowledge from data. And in order to extract this relevant information, the data must be examined from all angles. This requires specialized knowledge of statistics and good computer science skills. A data scientist is a professional who has this knowledge and hence can make useful predictions based on tons of data that is generated every day. Data analytics is a niche profession and there is much more demand than there is supply. Well qualified and trained data scientists can command lucrative salaries and job positions

Towards the end of the course, all participants will be required to work on a project to get hands on familiarity with the concepts learnt. You will use various platforms such as R, Hadoop and Machine Learning on Big Data and learn to perform data analysis with support from your mentors. This project, which can also be a live industry project, will be reviewed by our instructors and industry experts. On successful completion, you will be awarded a certificate.

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GKLM
  5476 Sona Place, Opp. Spark Mall,
Kamla Nagar, New Delhi, India

+91-9999160255   +91-8595181398

  tech.gklm@gmail.com
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