data science

“End to End Data Science Practicum with Knime” course covers various data science tasks, beginning with project management techniques using the CRISP-DM methodology. The course includes the following steps: Business Understanding: Learn about different types of problems and business processes in real-life scenarios. Data Understanding: Understand the various types of data and the issues that arise from using it, and learn to visualize data. Data Preprocessing: Tackle issues such as noisy or dirty data, missing values, and standard data concerns. Learn data integration techniques such as concatenation, joins, and filtering. Data Transformation: Learn techniques such as pivoting, normalization, and discretization.

Classification and Prediction: Learn about machine learning techniques such as Naive Bayes, Decision Trees, Logistic Regression, and K-NN, as well as prediction and regression techniques like decision trees, polynomials, and linear regression. Unsupervised Learning: Learn about Apriori algorithms, K-means, and other unsupervised learning techniques to learn association rules and cluster data. Currently, udemy is offering the End to End Data Science Practicum with Knime course for up to 87 % off i.e. INR 449 (INR 3,500).

Who Can Opt for this Course?

  • If you are interested in data science or machine learning, wish to monetize your data, activate your data, or construct intelligent systems

Course Highlights

Key HighlightsDetails
Registration LinkApply Now!
PriceINR 449 (INR 3,50087 % off
Duration09 Hours
Student Enrollment3,652 students
InstructorProf. Dr. Sadi Evren Seker
Topics Covered
  • How are we going to cover the content?
  • Project Management Techniques: SEMMA and KDD
  • Project Management : CRISP-DM
Course LevelN.A
Total Student Reviews667

Learning Outcomes

  • From data to knowledge level, you will be able to implement whole data science projects
  • You’ll use your data science skills to solve any problem in any field, or you’ll recognize when it doesn’t apply

Course Content

S.No.Module (Duration)Topics
1.Introduction (14 minutes)Introduction to Course
How are we going to cover the content?
Which tool are we going to use?
What is unique about the course?
2.What is Data Science Project and our methodology (15 minutes)Project Management Techniques : SEMMA and KDD
Project Management : CRISP-DM
3.Knime Download and Installation (15 minutes)Knime Web Page and Download
Knime Environment and Screens
First Workflow and Data Coloring / Visualization
4.Welcome to Data Science (12 minutes)First end-to-end problem: Teaching to the machine
5.Understanding Problem (06 minutes)Types of analytics: Descriptive, Predictive and Prescriptive Analytics
6.Understanding Data (10 minutes)Data Types and Problem Types
7.Understanding Data and Data Preprocessing (02 hours 18 minutes)Introduction to Data Manipulation and Preprocessing
Data Accessing (File Reader, Excel Reader and Table Creator)
Data Discovery: Basic Visualization
Data Discovery: Advanced Visualization Examples
Row Filtering and Missing Values
Advanced Filtering: Rule Based Row Filtering
Column Filtering
Join (Inner Join, Left , Right or Full Outer Joins)
Grouping and Aggregation
Math Formula and String Replace
Discrete / Quantized Data and Binning
Pivot Operation
EXTRA : Meta Node and Data Generation in Knime
Splitting and Combining
Type Conversion (String, Numeric)
8.Modeling (18 minutes)Introduction to Machine Learning : Test and Train Datasets
Introduction to Machine Learning: Problem Types
9.Classification (01 hour 57 minutes)Bayes Theorem and Naive Bayes Model
Knime Application of Naive Bayes Algorithm
Decision Tree, Information Gain and Gini index
Decision Tree Practicum with Knime
k-Nearest Neighbor Algorithm
k-NN Practicum with Knime
Distance Metrics
Distance Metrics Practicum
SVM: Support Vector Machines
SVM Practicum
End to End Practicum with all Algorithms
Logistic Regression: Theory
Comparing Classification Algorithms
10.Association Rule Mining / Learning (22 minutes)An Introduction to ARM / ARL and A Priori Algorithm
Apriori Algorithm in Action
11.Clustering / Segmentation (01 hour 08 minutes)Introduction to Clustering and Concepts
K-Means Algorithm
K-Means: Optimum number of clusters ( k value) and WCSS
K-Means Practicum with Knime
Optimizing number of clusters (k value) in Knime with Grid Search
Hierarchical Clustering: Agglomerative and Divisive Approaches
Hierarchical Clustering Practicum with Knime
DBSCAN : Density Based Approach
DBSCAN Practicum
Comparison of Clustering Algorithms
12.Regression / Prediction Algorithms (01 hour 25 minutes)Linear Regression
Linear Regression Practicum
Introduction to Evaluation of Regression Models
Practicum of Simple Evaluation of the Regression Models
Multiple Linear Regression: Theory and Practicum
Polynomial Regression
Simple Regression Tree
Simple Regression Tree Practicum
Comparison of Regression Models
Sample Problem / Solution : Stock Market Regression
13.Knime as a tool: Advanced Knime Features and Maintenance (14 minutes)PMML Standard and Theory
PMML Action with Knime
14.Evaluation (12 minutes)Introduction to Evaluation
Baseline, ZeroR and Imbalanced Data Sets
Comparison of Imbalanced Data Solutions
Evaluation Chart

Resources Required

  • Math for high school
  • Having the capacity to install software

Featured Review

Roshan Kumar Naik (5/5) : The course provides an in-depth understanding in data science and KNIME. The trainer is also good and holds on the excitement to learn DS


  • Washington Amaral e Silva (5/5) : Great course for those who has the first contact with this program.


  • Ozhan AKDAG (1/5) : I am a bit frustrated because I was expecting to start after the first video but I am still just waiting.
  • KA Bhat (1/5) : As the course progresses, the instructor mentions about sections/topics for which videos/lectures are missing.
  • Sergej Lust (1/5) : Sorry about that rating, since I like the course but you really should solve that issue.
  • Paulo Lima (1/5) : Sorry about that rating, since I like the course but you really should solve that issue.

About the Author

The instructor of this course is Prof. Dr. Sadi Evren Seker who is a Researcher in Computer Science and Business Analytics. With 4.4 Instructor Rating and 11,393 Reviews on Udemy, he/she offers 6 Courses and has taught 39,047 Students so far.

  • Biography He joined the University of Texas at Dallas as a Post-Doc researcher after earning his BSc, MSc, and PhD in computer science and engineering
  • Dr Adi Evren Eker, who has experience lecturing on a wide range of topics at 17 universities and six different countries
  • He recently left the American institution where he taught and returned to Turkey
  • Adi Evren EKER has numerous well-known scholarly publications and patents
  • Since 2000, he has also been actively active in Turkey’s information technology sector
  • In the fields of big data, data science, and artificial intelligence, he is still actively running his own business

Comparison Table

ParametersEnd to End Data Science Practicum with KnimeData analyzing and Machine Learning Hands-on with KNIMEKNIME – a crash course for beginners
OffersINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% off
Duration9 hours4.5 hours5 hours
Rating3.9 /54.3 /54.6 /5
Student Enrollments3,6522,0522,408
InstructorsProf. Dr. ?adi Evren ?ekerBarbora Stetinova, MBADan We
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