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ADS&ML
Applied Data Science & Machine Learning

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English
Intermediate
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Course Overview

Applied Data Science & Machine Learning is a five-day advanced program designed to enhance data science skills. The course covers advanced data wrangling, linear regression, machine learning algorithms, and includes a capstone project. Participants will learn sophisticated data wrangling techniques using dplyr and tidyr in R, handling missing data and performing complex transformations. The course delves into linear regression, covering multiple regression analysis, model evaluation, and diagnostics, with hands-on exercises for practical application. Machine learning is a key focus, exploring supervised and unsupervised techniques, model training, evaluation, and tuning using the caret package in R. The program culminates with a capstone project, allowing participants to integrate and apply their skills in a comprehensive data science project, from data acquisition to model evaluation. By the end of this course, participants will master advanced data science techniques and machine learning, ready to tackle complex challenges and drive data-driven decisions.

Ard Z.
Training Advisor
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Key Takeaways

1
Develop advanced data wrangling skills using dplyr and tidyr in R, preparing complex data sets for analysis by addressing missing data and outliers.
2
Construct and interpret multiple linear regression models to analyze variable relationships and derive actionable insights.
3
Evaluate machine learning models using supervised and unsupervised techniques, assessing their effectiveness and accuracy.
4
Implement model training, evaluation, and tuning using the caret package in R, optimizing performance through hyperparameter tuning.
5
Synthesize learned concepts in a capstone project, integrating data acquisition, cleaning, analysis, and model evaluation to create a comprehensive data science solution.

Harvard University
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This program is from Harvard’s Office of the Vice Provost for Advances in Learning (VPAL), in association with HarvardX. It is offered in collaboration with GetSmarter, an edX partner.Gain a holistic perspective on cybersecurity risk and mitigation, and get recognised for your knowledge with a premier certificate from Harvard’s VPAL.

Course Outline

DAY 1 Datas Wrangling
→ Module 1: Introduction to Data Cleaning and Wrangling
→ Module 2: Handling Missing Data and Outliers
→ Module 3: Data Transformation Techniques
→ Module 4: Using dplyr for Data Manipulation
→ Module 5: Using tidyr for Data Wrangling
→ Module 6: Practical Session: Data Wrangling Exercises
→ Module 7: Q&A and Wrap-Up
DAY 2 Linear Regression
→ Module 1: Concepts of Linear Regression
→ Module 2: Simple Linear Regression Analysis
→ Module 3: Multiple Regression Analysis
→ Module 4: Model Evaluation and Diagnostics
→ Module 5: Practical Session: Linear Regression in R
→ Module 6: Case Study: Real-World Regression Problems
→ Module 7: Q&A and Wrap-Up
DAY 3 Machine Learning
→ Module 1: Introduction to Machine Learning
→ Module 2: Supervised Learning Techniques
→ Module 3: Unsupervised Learning Techniques
→ Module 4: Using caret Package for Model Training
→ Module 5: Model Evaluation and Tuning
→ Module 6: Practical Session: Machine Learning in R
→ Module 7: Q&A and Wrap-Up
DAY 4 Capstone Project Preparation
→ Module 1: Introduction to the Capstone Project
→ Module 2: Project Planning and Data Acquisition
→ Module 3: Data Cleaning and Preparation
→ Module 4: Exploratory Data Analysis
→ Module 5: Model Building and Evaluation
→ Module 6: Visualization and Presentation of Results
→ Module 7: Q&A and Wrap-Up.
DAY 5 Capstone Project Presentation
→ Module 1: Finalizing the Capstone Project
→ Module 2: Project Presentation Guidelines
→ Module 3: Group Work: Final Touches on Projects
→ Module 4: Project Presentations
→ Module 5: Feedback and Discussion
→ Module 6: Program Summary and Next Steps
→ Module 7: Certification Ceremony and Closing Remarks

Who Should Attend?

This highly practical and interactive course has been specifically designed for

→ Business and Data Analysts: Professionals
who need to analyze and report on data to
inform business decisions.

→ Project Managers: Those who manage
projects and require data to plan and
report progress.

→ Finance Professionals: Analysts and
managers who need to handle complex
financial data and forecasts.

→ IT Professionals: Those who support data
systems and need to understand data flows
and reporting.

→ Operations Managers: Managers needing to
optimize operations through data analysis.

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