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Data Science Analytics - Gratisol Life Sciences

About the Course

Data science is an automated method to analyse massive volumes of data from various sources and extract insights from it. Small and large corporate today are sitting on a gold mine of data, but their biggest challenge is extracting business insights from this data to take effective business decisions. Data analytics is the science of examining raw data to draw business- related conclusions from it and model and predict business outcomes.

This comprehensive course can help an individual gain experience on the power of data and how to explore the data using R as a tool to get valuable insights for the business. This course may also help in the improvement of organization’s performance by doing trend analysis and pattern study. The course shall help to run various algorithms using R which is one of the most effective tool to uncover the patterns within the data and compare the results and insights.

Course Benefits

  • Understand your business data better and be able to generate trend and get insights
  • Take appropriate and faster business decisions which is data driven
  • Increase efficiency and reduce cost for your business/ domain
  • Gauge customer internal/external needs and satisfaction
  • Uncover new growth opportunities for the business
  • Diagnose the business problem faster

Who Should Take this course?

  • Working professionals who intend to build their career in the field of data analytics
  • Professionals who are currently in the Data Analytics domain
  • Fresh graduates and young professionals
  • Entrepreneurs
  • Professionals from the quality team
  • Six Sigma Professionals
  • IT Professionals

Course Coverage

  • Introduction to analytics
  • Introduction to R
  • Basic building blocks of R
  • Working with data in R: Importing, exporting and data wrangling
  • Functions, loops, and data frames in R
  • Descriptive statistics using R
  • Inferential statistics using R
  • Visualization using R
  • Linear, non-linear and logistic regression
  • Classification
  • Clustering
  • Machine learning Algorithms
  • Decision trees
  • Neural Networks

Modules to be Covered:

Module 1 – Data Science Project Lifecycle
Module 2 – Introduction To R And Python Basic Statistics
Module 3 – Hypothesis Testing
Module 4 – Linear Regression
Module 5 – Logistic Regression
Module 6 – Regularization Techniques
Module 7 – Multinomial Regression

Data Mining Unsupervised

Module 8 – Data Mining Unsupervised – Clustering
Module 9 – Dimension Reduction
Module 10 – Data Mining Unsupervised – Network Analytics
Module 11 – Data Mining Unsupervised – Association Rules
Module 12 – Data Mining Unsupervised – Recommender System
Module 13 – Text Mining
Module 14 – Natural Language Processing


Module 15 – Machine Learning Classifiers – KNN
Module 16 – Classifier – Naive Bayes
Module 17 – Decision Tree And Random Forest
Module 18 – Bagging And Boosting
Module 19 – Black Box Methods
Module 20 – Survival Analysis
Module 21 – Forecasting

Assignments/Projects/Placement Support

Module 22 – Assignments
Module 23 – Projects
Module 24 – Resume Prep And Interview Support

Value added courses

Module 25 – Basics Of Hadoop And Spark
Module 26 – Basics Of R
Module 27 – Basics Of Python
Module 28 – Basics Of Azure
Module 29 – Basics Of MYSQL
Module 30 – Tableau

Certificate and Assessment

Participants will receive a certificate of completion at the end of the course on successfully clearing the assessment.

  • Assessment: Duration – 1 hour – 25 MCQs
  • Minimum Pass percentage – 50%
  • Participants will get 3 attempts at the final assessment to complete the Data Science certification
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