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Data Science Certification Course using R

Course Price


Course length

5 Weeks Weekend Classes

Data Science Certification Course using R


Industrial Experts.

All the instructors at Edureka are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by Edureka for providing an awesome learning experience.

About the course

About Data Science Certification Course
Data science is a "concept to unify statistics, data analysis and their related methods" to "understand and analyse actual phenomena" with data. Data Science Training employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science from the sub-domains of machine learning, classification, cluster analysis, data mining, databases, and visualization. The Data Science Certification Course enables you to gain knowledge of the entire life cycle of Data Science, analyse and visualise different data sets, different Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes.

What are the objectives of our Data Science Online Course?
Data Science Certification Training is designed by industry experts to make you a Certified Data Scientist. The Data Science course offers:
-In-depth knowledge of Data Science Life Cycle and Machine Learning Algorithms
-Comprehensive knowledge of various tools and techniques for Data Transformation
-The capability to perform Text Mining and Sentimental analyses on text data and gain an insight into Data Visualization and Optimization techniques
-The exposure to many real-life industry-based projects which will be executed in RStudio
-Projects which are diverse in nature covering media, healthcare, social media, aviation and HR
-Rigorous involvement of an SME throughout the Data Science Training to learn industry standards and best practices

Why should you go for Data Science Training?
Data science is an evolutionary step in interdisciplinary fields like the business analysis that incorporate computer science, modelling, statistics and analytics. To take complete benefit of these opportunities, you need a structured training with an updated curriculum as per current industry requirements and best practices.
Besides strong theoretical understanding, you need to work on various real-life projects using different tools from multiple disciplines to gather a data set, process and derive insights from the data set, extract meaningful data from the set, and interpret it for decision-making purposes.
Additionally, you need the advice of an expert who is currently working in the industry tackling real-life data-related challenges.

1. Introduction of Data Science
2. Statistical Inference
3. Data Extraction, Wrangling, and Exploration
4. Introduction to Machine Learning
5. Classification Techniques
6. Unsupervised Learning
7. Recommender Engine
8. Text Mining
9. Time series
10. Deep Learning

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