Industrial Training - AptusLearn
Bhubaneswar : +91 96069 81951 ,Bengaluru : +91 96069 81950

Industrial training

Course Overview

This Course begins with an overview of Data Science and illustrates the means by which to apply statistics and analytics for smart decision making today. The first module covers explorative data analysis, descriptive and predictive analytics with Advanced AQL and Advanced R Programming. Participants learn to apply statistical concepts and model data.
The second module focuses on data mining and predictive analytics using RapidMiner Studio. Participants will be able to scrub and load data, create, and then validate predictive models for business use cases.


  • Basic knowledge of computer programming languages (any of these: R, Python, C++, Java, and SQL).
  • Probability, Statistics basic Database concepts.
  • Participants to bring his/her own Laptop (recommended: Intel i5 with 8GB RAM and 500GB HDD) for hands-on practice.
  • The following software installed: R Studio, R Packages, RapidMiner Studio Platform (free trial version), MySQL.
  • A thorough study of the pre-reading training & readiness material.

Course Objectives

  • Acquire conceptual and application skills used by Data Scientists.
  • Learn applied statistics and data analytics with “R” and SQL.
  • Master data mining and predictive analytics with RapidMiner Studio.
  • Understand the process, stages and methods of Data to Decision framework, Data Science, and components.
  • Get hands-on experiences on live business cases.

Course Outline

  • Overview
    • Data to Decision framework used in enterprises and analytical platform.
    • Data Analytics: process, stages and methods for achieving a data mining solutions.
    • Data Science and its components.
  • Data Science Foundation
    • Data Science life cycle and components.
    • Machine Learning and Artificial Intelligence.
    • Machine Learning Models – Supervised & Unsupervised Methods.
    • Feature Analysis.
    • Discussion on Business Use Cases.
    • Tools, technologies and libraries used in Data Science, including open sources.
    • Various OEM vendors and platforms available in Data Science.
    • Quizzes & Pop up questions.
  • Database Management & Advanced SQL
    • Database Concepts and Introduction to SQL.
    • Data Manipulation – INSERT, UPDATE, DELETE, SELECT.
    • Advanced SQL.
    • Data Conversion and Functions.
    • Windows / Analytical Functions.
    • Use Cases for Data Management.
    • Data management Concepts – Data Warehousing, Big Data, Hadoop, Data Lake, etc.
    • Hands-on practice.
  • “Python” Programming basics
    • Python packages Installation and Configuration.
    • Fundamentals of Python and Data Structure.
    • Loading data and Data Management Functions.
    • More functions – date and analytical.
    • Basic exploratory statistics and graphs.
    • Hands-on practice.
  • Statistical Analysis using “Python”
    • Basic statistical data types & characteristics.
    • Law of large numbers & Central Limit Theorem, Central Tendencies & Dispersion.
    • Treatment of Outliers and Missing Values.
    • Data Visualization – Plots, Graphs & Maps.
    • Probability Basics – Events, Types & Rules, Bayes’ Theorem & Confusion Matrix.
    • Statistical Distribution.
    • Inferential Statistics.
    • Regression with Categorical data.
    • Hands-on Practice with case studies & Assignments.
  • Data Analytics using RapidMiner Tool
    • CRISP-DM model and methodology.
    • Exploratory data analysis.
    • Data preparation and better process.
    • Predictive model algorithms.
    • Model construction and evaluation.
    • Model deployment and operationalizations.
    • Collaboration, automation and Web applications.
    • Developing enterprise-grade data science pipelines.
    • Hands-on Practice with Industry based Business Use Cases, Datasets & Solutions.
  • Interaction with Industry Experts
    • Expert Presentations from seasoned professionals of large IT Enterprises as well as Academicians covering the following topics:
      • Data Science Tools for Artificial Intelligence, Machine Learning & Deep Learning.
      • Global Trends in Big Data, Analytics & Business Solutions.
      • The importance of building Data Science skill within the secondary & tertiary levels.
    • Q&A Sessions
  • Business Experts
    • Business problems and assignments on Real-life datasets.
    • Build data models and solutions using industry validated process & methodologies (CRISP-DM).
  • Project Work
    • A problem statement with the dataset will be given to each student; solutions to be submitted in 2 weeks.
    • Participants should solve the problems using RapidMiner Studio pg. 2 Tool and/ or R.
    • An Industry Expert will review your model and solutions.
  • Presentation & Review
    • Present your Business Use Cases Solutions.
    • Evaluation and review comments from industry expert.
    • Guidance on careers in Data Science.




who can apply

Graduates or Students in final year of graduation


1.5 months – Instructor-led Classroom Training (1month)– Project Work (2 weeks carry home project)


Summer and Winter Batch.

Delivery Mode

Direct contact, Theory (~30%) and Hands-on and Workshop (~70%)


INR 27,500

Tools & Technologies

RapidMiner Studio Data Science Tool, Advanced SQL, Advanced R Programming and Statistics

Join us today, for a better future !!!

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