Data Science- Project Management Methodology-CRISP-DM-2026

Name: Data Science- Project Management Methodology-CRISP-DM-2026
Rating: 4.6 (670 reviews)

End to End High Level Overview of Data Science & AI projects

Created by360DigiTMG Elearning

Last updated 3/2026

English

English [Auto],

What you'll learn

Learn about Amazing Project Management Methodology (CRISP-DM) in Handling Data Science & Artificial Intelligence Projects.
You will be able to understand End to End High Level Overview of Data Science & AI projects.

Course content

8 sections • 34 lectures • 4h 58m total length

Introduction About Trainer & Course2:12
Get to know about best trainer in the space of Analytics world

Underatanding KDD, SEMMA & CRISP-DM16:12
Learn the various project management methodologies including KDD, SEMMA and CRISP-DM. Understand the various stages of KDD - Selection, Preprocessing, Transformation, Data Mining & Evaluation. Also understand the various stages of SEMMA methodology - Sample, Explore, Modify, Model, Assess. Finally get introduced to CRISP-DM.
Business Problem -A4:28
Understand on how to record the business problem using a real-life use case from Banking sector. Also learn to record the business objectives and business constraints using the data optimization terminologies.
Comparison between KDD, SEMMA, CRISP-DM9:28
Learn the quick comparison of the 3 project management methodologies - KDD, SEMMA, CRISP-DM. Get started with CRISP-DM in further detail starting with the first stage - Business Understanding, which contains 4 steps. Get a brief overview of all the 4 steps of stage 1 of CRISP-DM.
Business Problem -B3:55
Understand on how to record the business problem using a real-life use case from credit card department of Financial institute. Learn on how to record the business objectives to exactly impact the fraud happening and how do we at the same time arrest the inconvenience caused.
Business Problem -C4:59
Understand on agriculture related business problem & the unique drone driven solution in data collection and drone analytics. Also learn about the business objectives alongside the business constraints, which will levy restriction in providing the best solution in solving the problems.
Business Problem -D & E4:55
E-commerce is at its prime and there are plethora of business problems in this emerging sector. Understanding not just the business problem but also documenting the right objectives to be achieved while the constraints are levied from customers, is a problem worth solving. Learn about the same as part of this lecture.

Data Understanding Introduction1:25
Understand the 2nd Stage of CRISP-DM, which is Data Understanding. Learn high level overview of the 4 key steps included in the 2nd stage of the project management methodology for data mining.
A. Data Collection10:35
Learn about the first step of stage 2 of CRISP-DM, that is, Data Collection. Understand about the 2 types of data collection techniques including Primary as well as Secondary Data Sources alongside the pros and cons of each of these sources.
A-1: Differentiation of Various Data Collection20:01
Understand about the various data types, which are pivotal for the success of data collection. Understanding on whether the solution to the problem, at a high level, needs real time processing capabilities or delayed prediction suffices the needs is something to learn about in finer detail.
A-2 : Data Collection Using Survey5:33
Understand the various stages included in devising the survey questionnaire is worth the effort. Conducting surveys is an extremely costly affair and hence significant time needs to be spent on this primary data collection mechanism.
A-3 : Data Collection Using Design Of Experiments7:03
Understand about another primary data collection techniques - Design of Experiments (DoE). Learn about practical use cases in understand various factors based on which design of experiment is performed.
A-4 : Exploratory Data Analysis27:25
Master exploratory data analysis with inferential and descriptive statistics, sampling frames and simple random sampling, hypothesis testing, outlier handling, assessment, and univariate to multivariate analyses including scatter plots and clustering.
A-5 : Data Quality Analysis16:15
Data quality analysis identifies potential errors, missing data, outliers, and granularity issues, using attribute agreement analysis and gauge R&R to validate measurements and metadata.

Modelling Introduction5:07
Determine modeling approach within time and data constraints by assessing data size, sources, and streaming vs batch, then balance accuracy and speed with simple models, feature engineering, and dimension reduction.
Model Building Supervised and Unsupervised Learning5:02
Learn supervised and unsupervised learning, including linear regression and classification, by modeling the relationship between Y and X. Explore prediction, recommendation, and retrieval problems with output and input variables.
Model Building Re-inforcement Learning3:55
Model Building Semi-Supervised and Other Learnings8:50
Combine labeled radiology images with unlabeled data in semi-supervised learning to build cancer models. Use clustering and transfer learning to reuse cnn features like edges and blobs for improved accuracy.
Model Building Learning Examples8:56
Selecting Model Technique-When Y is known ?18:27
Selecting Model Technique-When Y is unknown ?7:44
Selecting Model Technique- For Forecasting and Time-series data4:49
Data Separation Technique6:09
Explore data separation techniques in data science, including training, validation, and test splits, to prevent information leakage and evaluate model generalization through iterative training and testing.
Model Evaluation Technique - Over fitting Vs Under fitting Datasets3:22
Model Evaluation Technique-Balanced and Imbalanced Datasets6:25
Learn model evaluation for balanced and imbalanced datasets, including train–validation–test splits, tuning logistic regression and decision trees, and applying five-fold cross-validation and random resampling to estimate future error.
Errors Accuracy & Measures | When Y is continuous ?7:22
Evaluate regression models with continuous outputs using mean error, mean absolute deviation, mean squared error, root mean squared error, mean percentage error, and mean absolute percentage error, linking to accuracy.
Errors Accuracy & Measures | When Y is discrete ?7:49
Conti....| When Y is discrete ?6:38
Model Evaluation Technique-ROC Curve and Model Assessment8:14
Understand the roc curve and area under the curve to assess model discrimination by comparing true positive rate and false positive rate, with threshold choices for deployment and business needs.

Requirements

Knowledge of Data Science Basics is recommended but not mandatory.

Description

This course includes a structured approach of handling the data related projects for maximizing the success rate. Learn about insights on how data is assisting organizations to make informed data-driven decisions. Data is treated as the new oil for all the industries and sectors which keep organizations ahead in the competition. Learn the application of Big Data Analytics in real-time, you will understand the need for analytics with a use case. Also, learn about the best project management methodology for Data Mining - CRISP-DM at a high level.

Learners will understand about Project management methodology - CRISP-DM, in handling Data Science projects or Artificial Intelligence projects end to end.
Learn about all the 6 stages including Business Understanding, Data Understanding, Data Preparation, Data Modeling, Model Evaluation and finally Model Deployment.
Learn about Data Collection, Data Cleansing, Data Preparation, Data Munging, Data Wrapping, etc.
Learn about the preliminary steps taken to churn the data, known as exploratory data analysis. In this module, you also are introduced to statistical calculations which are used to derive information from data. We will begin to understand how to perform a descriptive analysis.
Learn about continuous probability distribution. Understand the properties of a continuous random variable and its distribution under normal conditions. To identify the properties of a continuous random variable, statisticians have defined a variable as a standard, learning the properties of the standard variable and its distribution. You will learn to check if a continuous random variable is following normal distribution using a normal Q-Q plot.
Learn the science behind the estimation of value for a population using sample data.

Who this course is for:

Data Science Beginners, Intermediate and Advanced users, Artificial Intelligence Beginners, Intermediate and Advanced users.

Data Science- Project Management Methodology-CRISP-DM-2026

What you'll learn

Explore related topics

Course content

Introduction About Trainer1 lecture • 2min

Introduction to PM Methodologies & Detailed Understanding1 lecture • 8min

Phase-1 | Business Understanding6 lectures • 44min

Phase-2 | Data Understanding7 lectures • 1hr 28min

Phase-3 | Data Preparation2 lectures • 36min

Phase-4 | Modelling15 lectures • 1hr 49min

Phase-5 | Evaluation1 lecture • 5min

Phase-6 | Deployment1 lecture • 6min

Requirements

Description

Who this course is for: