CRISP-ML(Q) – Data Pre-processing Using Python

CRISP-ML(Q) - Data Pre-processing Using Python

CRISP-ML(Q) – Data Pre-processing Using Python

Data Science – Data Pre-processing Using Python

Language: english

Note: 0/5 (0 notes) 120 students  New course 

Instructor(s): 360DigiTMG Elearning

Last update: 2022-09-08

What you’ll learn

  • Understand Project Management Methodology to Handle Data Related Projects in Structured Manner.
  • Understand Business Problem Definition, Setting Objectives & Constraints.
  • Understand Data Types as well as Data Collection Mechanisms.
  • Understand Exploratory Data Analytics (EDA) / Descriptive Statistics as well as Graphical Representation
  • Understand the various Data Cleansing /Pre-Processing Tasks using Python.



  • No Programming and No Statistics knowledge is needed because everything is taught right from scratch.
  • Basic Computer Knowledge and Primary School Mathematics Knowledge is sufficient.



This program will help aspirants getting into the field of data science understand the concepts of project management methodology. This will be a structured approach in handling data science projects. Importance of understanding business problem alongside understanding the objectives, constraints and defining success criteria will be learnt. Success criteria will include Business, ML as well as Economic aspects. Learn about the first document which gets created on any project which is Project Charter. The various data types and the four measures of data will be explained alongside data collection mechanisms so that appropriate data is obtained for further analysis. Primary data collection techniques including surveys as well as experiments will be explained in detail. Exploratory Data Analysis or Descriptive Analytics will be explained with focus on all the ‘4’ moments of business moments as well as graphical representations, which also includes univariate, bivariate and multivariate plots. Box plots, Histograms, Scatter plots and Q-Q plots will be explained. Prime focus will be in understanding the data preprocessing techniques using Python. This will ensure that appropriate data is given as input for model building. Data preprocessing techniques including outlier analysis, imputation techniques, scaling techniques, etc., will be discussed using practical oriented datasets.


Who this course is for

  • Beginners, Intermediate as well as Advanced learners
  • Freshers who are new of data science and want to embark into the field of data science
  • Working professionals who are working in different industries
  • Lecturers & Professors & Teachers whose primary role is to teach students on data related concepts


Course content

  • Introduction
    • Introduction to Project Management Methodology CRISP ML(Q)
    • Agenda & Stages of Analytics
    • What is Diagnostic Analytics ?
    • What is Predictive Analytics ?
    • What is Prescriptive Analytics ?
    • What is CRISP ML (Q) ?
  • Business Understanding
    • Business Understanding – Define the Scope of Application
    • Business Understanding – Define Success Criteria
    • Business Understanding – Use Cases
  • Data Understanding
    • Agenda Data Understanding
    • Introduction to Data Understanding
    • Data types-continuous data (vs) Discrete data.mp4
    • Categorical Data Vs Count Data
    • Practical Data Understanding Using Realtime Example
    • Scale of Measurement
    • Quantitative (vs) Qualitative
    • Structured Vs Unstructured Data



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