“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 Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 09 Hours |
| Rating | 3.9/5 |
| Student Enrollment | 3,652 students |
| Instructor | Prof. Dr. Sadi Evren Seker https://www.linkedin.com/in/prof.dr.sadievrenseker |
| Topics Covered |
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| Course Level | N.A |
| Total Student Reviews | 667 |
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 | ||
| Concatenation | ||
| Join (Inner Join, Left , Right or Full Outer Joins) | ||
| Grouping and Aggregation | ||
| Math Formula and String Replace | ||
| Discrete / Quantized Data and Binning | ||
| Normalization | ||
| 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
Pros
- Washington Amaral e Silva (5/5) : Great course for those who has the first contact with this program.
Cons
- 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
| Parameters | End to End Data Science Practicum with Knime | Data analyzing and Machine Learning Hands-on with KNIME | KNIME – a crash course for beginners |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 9 hours | 4.5 hours | 5 hours |
| Rating | 3.9 /5 | 4.3 /5 | 4.6 /5 |
| Student Enrollments | 3,652 | 2,052 | 2,408 |
| Instructors | Prof. Dr. ?adi Evren ?eker | Barbora Stetinova, MBA | Dan We |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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