End to End Data Science Practicum with Knime

Name: End to End Data Science Practicum with Knime
Rating: 4.6 (811 reviews)

Applied Data Science Concepts and Techniques with Knime and hands on examples

Created byProf. Dr. Şadi Evren Şeker

Last updated 4/2019

English

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What you'll learn

You will be able to implement end to end data science projects from data to knowledge level
You will apply your data science knowledge to any problem in any domain, or you will understand if it is not applicable

Course content

14 sections • 72 lectures • 9h 12m total length

Introduction to Course5:40
How are we going to cover the content?1:34
Which tool are we going to use?4:19
Discover Knime as an open-source analytics platform; learn installation, extensions, and integration with R, Python, and Java, and explore server options on Azure and AWS, with use cases across industries.
What is unique about the course?2:38
This course presents a top-down, open-source Knime-driven data science practicum. It starts with the big picture of data science projects and then dives into methods, enhanced by real-life cross-industry cases.

Introduction to Data Manipulation and Preprocessing6:31
Data Accessing (File Reader, Excel Reader and Table Creator)8:50
Data Discovery: Basic Visualization10:14
Data Discovery: Advanced Visualization Examples6:38
Row Filtering and Missing Values11:31
Learn row filtering and handling missing values, anomalies, and noisy data in Knime to clean datasets before modeling, using range checks and practical data preprocessing techniques.
Advanced Filtering: Rule Based Row Filtering7:04
Column Filtering4:34
Explore how to filter data columns using the column filter node in knime, including include/exclude options, type-based selections, and parallel or serial filtering.
Concatenation6:42
Join (Inner Join, Left , Right or Full Outer Joins)8:01
Learn to merge data sources in Knime using the joiner to perform inner, left outer, right outer, and full outer joins on IDs to build a unified table.
Grouping and Aggregation7:04
Learn how to use Knime's group by and aggregation to summarize data, compute means, medians, minimums and maximums, and manage lists and missing values for clear, grouped insights.
Math Formula and String Replace10:10
Apply formula and string replacer in Knime to harmonize data sources, perform feature engineering, and compute metrics like body mass index.
Discrete / Quantized Data and Binning8:43
Explore discretization, quantization, and binning in Knime by splitting numerical data into bins, converting to categorical data, and naming bins using auto binner or manual boundaries.
Normalization5:30
Pivot Operation7:14
EXTRA : Meta Node and Data Generation in Knime11:42
Splitting and Combining11:00
Type Conversion (String, Numeric)6:53

Bayes Theorem and Naive Bayes Model11:21
Knime Application of Naive Bayes Algorithm14:11
Decision Tree, Information Gain and Gini index16:17
Decision Tree Practicum with Knime9:44
Build and evaluate a decision tree workflow in Knime, including training/testing split, learner/predictor configuration, and pruning considerations for clearer outcomes.
k-Nearest Neighbor Algorithm6:57
k-NN algorithm and the eager / lazy learning strategies, hyper parameters and the concept of distance in machine learning
k-NN Practicum with Knime7:59
KNN implementation, concept of probabilities for classification.
concept of confidence intervals
Distance Metrics10:31
Numeric Distances: Manhattan, Euclidean, Minkowski, Chebyshev Distances
String Distances: Levenshtein Distance
Programming Language define distances, matrix distances or date / time distances
Distance Metrics Practicum12:27
SVM: Support Vector Machines6:18
SVM Classification and concepts of hard Margin / soft Margin or types of kernels like, linear, polynomial, exponential or radial basis function (RBF).
SVM Practicum5:57
Apply svm in Knime to classify gender from height and weight using a 66/34 train-test split, exploring linear, polynomial, cubic, and rbf kernels.
End to End Practicum with all Algorithms8:30
Logistic Regression: Theory4:05
Explore logistic regression as a binary classification method powered by the sigmoid (logistic) function, mapping inputs to 0–1 probabilities and using a threshold for class predictions.
Comparing Classification Algorithms3:14

Requirements

high school math
being able to install software

Description

The course starts with a top down approach to data science projects. The first step is covering data science project management techniques and we follow CRISP-DM methodology with 6 steps below:

Business Understanding : We cover the types of problems and business processes in real life

Data Understanding: We cover the data types and data problems. We also try to visualize data to discover.

Data Preprocessing: We cover the classical problems on data and also handling the problems like noisy or dirty data and missing values. Row or column filtering, data integration with concatenation and joins. We cover the data transformation such as discretization, normalization, or pivoting.

Machine Learning: we cover the classification algorithms such as Naive Bayes, Decision Trees, Logistic Regression or K-NN. We also cover prediction / regression algorithms like linear regression, polynomial regression or decision tree regression. We also cover unsupervised learning problems like clustering and association rule learning with k-means or hierarchical clustering, and a priori algorithms. Finally we cover ensemble techniques in Knime.

Evaluation: In the final step of data science, we study the metrics of success via Confusion Matrix, Precision, Recall, Sensitivity, Specificity for classification; purity , randindex for Clustering and rmse, rmae, mse, mae for Regression / Prediction problems with Knime.

BONUS CLASSES

We also have bonus classes for artificial neural network and deep learning on image processing problems.

Warning: We are still building the course and it will take time to upload all the videos. Thanks for your understanding.

Who this course is for:

if you want to monetize your data
if you want to activate your data
if you want to create intelligent systems
if you are curious about data science or machine learning

End to End Data Science Practicum with Knime

What you'll learn

Explore related topics

Course content

Introduction4 lectures • 14min

What is Data Science Project and our methodology2 lectures • 16min

Knime Download and Installation3 lectures • 15min

Welcome to Data Science1 lecture • 13min

Understanding Problem1 lecture • 7min

Understanding Data1 lecture • 11min

Understanding Data and Data Preprocessing17 lectures • 2hr 18min

Modeling2 lectures • 18min

Classification13 lectures • 1hr 58min

Association Rule Mining / Learning2 lectures • 22min

Requirements

Description

Who this course is for: