Certificate Programme in Feature Engineering for Digital Advertising
-- viewing nowFeature Engineering for Digital Advertising Unlock the full potential of your digital ads with our Certificate Programme in Feature Engineering for Digital Advertising. Feature engineering is a crucial step in building effective digital ad campaigns.
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About this course
It involves selecting and transforming raw data into actionable insights that drive business results. This programme is designed for data professionals and marketers who want to learn how to engineer features that drive performance in digital advertising.
Through this programme, you'll learn how to:
Identify relevant features for digital ad campaigns
Transform raw data into actionable insights
Build and deploy feature engineering pipelines
Join our programme to gain the skills and knowledge you need to drive business results in digital advertising.
Explore our programme today and start building feature engineering skills that drive performance in digital advertising.
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Course details
•
Feature Engineering for Digital Advertising: Foundations
This unit introduces the concept of feature engineering in digital advertising, including data preprocessing, feature selection, and dimensionality reduction. It covers the importance of high-quality features in advertising campaigns and the role of feature engineering in improving campaign performance. •
Data Preprocessing for Digital Advertising
This unit focuses on data preprocessing techniques used in digital advertising, including data cleaning, handling missing values, and data normalization. It also covers the use of data visualization tools to understand the distribution of features and identify potential issues. •
Feature Extraction for Digital Advertising
This unit explores feature extraction techniques used in digital advertising, including text feature extraction, sentiment analysis, and click-through rate (CTR) modeling. It also covers the use of machine learning algorithms to extract relevant features from large datasets. •
Feature Selection for Digital Advertising
This unit discusses feature selection techniques used in digital advertising, including correlation analysis, mutual information, and recursive feature elimination. It also covers the use of feature selection methods to identify the most important features in advertising campaigns. •
Dimensionality Reduction for Digital Advertising
This unit covers dimensionality reduction techniques used in digital advertising, including principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and autoencoders. It also discusses the use of dimensionality reduction methods to reduce the number of features in advertising datasets. •
Feature Engineering for Predictive Modeling in Digital Advertising
This unit focuses on feature engineering techniques used in predictive modeling for digital advertising, including feature engineering for regression, classification, and clustering models. It also covers the use of feature engineering methods to improve the accuracy and interpretability of advertising models. •
Text Feature Engineering for Digital Advertising
This unit explores text feature engineering techniques used in digital advertising, including bag-of-words, term frequency-inverse document frequency (TF-IDF), and word embeddings. It also covers the use of text feature engineering methods to improve the performance of advertising models. •
Click-Through Rate (CTR) Modeling for Digital Advertising
This unit discusses CTR modeling techniques used in digital advertising, including linear regression, logistic regression, and neural networks. It also covers the use of CTR modeling methods to predict the likelihood of a user clicking on an ad. •
Feature Engineering for Retargeting in Digital Advertising
This unit focuses on feature engineering techniques used in retargeting for digital advertising, including feature engineering for lookalike audiences and custom audiences. It also covers the use of feature engineering methods to improve the performance of retargeting campaigns.
Feature Engineering for Digital Advertising: Foundations
This unit introduces the concept of feature engineering in digital advertising, including data preprocessing, feature selection, and dimensionality reduction. It covers the importance of high-quality features in advertising campaigns and the role of feature engineering in improving campaign performance. •
Data Preprocessing for Digital Advertising
This unit focuses on data preprocessing techniques used in digital advertising, including data cleaning, handling missing values, and data normalization. It also covers the use of data visualization tools to understand the distribution of features and identify potential issues. •
Feature Extraction for Digital Advertising
This unit explores feature extraction techniques used in digital advertising, including text feature extraction, sentiment analysis, and click-through rate (CTR) modeling. It also covers the use of machine learning algorithms to extract relevant features from large datasets. •
Feature Selection for Digital Advertising
This unit discusses feature selection techniques used in digital advertising, including correlation analysis, mutual information, and recursive feature elimination. It also covers the use of feature selection methods to identify the most important features in advertising campaigns. •
Dimensionality Reduction for Digital Advertising
This unit covers dimensionality reduction techniques used in digital advertising, including principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and autoencoders. It also discusses the use of dimensionality reduction methods to reduce the number of features in advertising datasets. •
Feature Engineering for Predictive Modeling in Digital Advertising
This unit focuses on feature engineering techniques used in predictive modeling for digital advertising, including feature engineering for regression, classification, and clustering models. It also covers the use of feature engineering methods to improve the accuracy and interpretability of advertising models. •
Text Feature Engineering for Digital Advertising
This unit explores text feature engineering techniques used in digital advertising, including bag-of-words, term frequency-inverse document frequency (TF-IDF), and word embeddings. It also covers the use of text feature engineering methods to improve the performance of advertising models. •
Click-Through Rate (CTR) Modeling for Digital Advertising
This unit discusses CTR modeling techniques used in digital advertising, including linear regression, logistic regression, and neural networks. It also covers the use of CTR modeling methods to predict the likelihood of a user clicking on an ad. •
Feature Engineering for Retargeting in Digital Advertising
This unit focuses on feature engineering techniques used in retargeting for digital advertising, including feature engineering for lookalike audiences and custom audiences. It also covers the use of feature engineering methods to improve the performance of retargeting campaigns.
Career path
| **Career Role** | **Description** |
|---|---|
| Digital Marketing Specialist | Develop and implement digital marketing strategies to achieve business objectives, utilizing data analysis and feature engineering techniques. |
| Data Analyst | Analyze data to identify trends and patterns, and develop data visualizations to communicate insights to stakeholders, leveraging feature engineering skills. |
| Business Intelligence Developer | |
| Machine Learning Engineer | Develop and deploy machine learning models to drive business decisions, utilizing feature engineering techniques to extract insights from complex data sets. |
| Quantitative Analyst | Apply mathematical and statistical techniques to analyze and model complex systems, leveraging feature engineering skills to extract insights from large data sets. |
Entry requirements
- Basic understanding of the subject matter
- Proficiency in English language
- Computer and internet access
- Basic computer skills
- Dedication to complete the course
No prior formal qualifications required. Course designed for accessibility.
Course status
This course provides practical knowledge and skills for professional development. It is:
- Not accredited by a recognized body
- Not regulated by an authorized institution
- Complementary to formal qualifications
You'll receive a certificate of completion upon successfully finishing the course.
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CERTIFICATE PROGRAMME IN FEATURE ENGINEERING FOR DIGITAL ADVERTISING
is awarded to
Learner Name
who has completed a programme at
London School of Planning and Management (LSPM)
Awarded on
05 May 2025
Blockchain Id: s-1-a-2-m-3-p-4-l-5-e
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