(4.6)

(1.1k Review)

Product Design Bootcamp

Dr. Liam Morgan

Master the fundamentals and advanced concepts of mechanical engineering. Learn practical applications, design principles, and cutting-edge technologies guided by industry expertise.

Course table of content

Lesson 1: Introduction to Data Science

Overview of data science and its applications in various industries.
Key concepts: data collection, analysis, and interpretation.
Introduction to the data science workflow and common tools (Python, R, SQL).

Lesson 2: Data Cleaning and Preprocessing

Understanding the importance of data cleaning in the data science process.
Techniques for handling missing data, outliers, and duplicates.
Preprocessing methods like normalization, standardization, and encoding categorical data.

Lesson 3: Exploratory Data Analysis (EDA)

Introduction to EDA techniques for understanding data patterns.
Using visualization tools (matplotlib, seaborn) to analyze datasets.
Identifying trends, correlations, and distributions in data.

Lesson 4: Introduction to Machine Learning Algorithms

Overview of supervised vs. unsupervised learning.
Introduction to basic machine learning algorithms (linear regression, decision trees, k-means clustering).
Implementing algorithms using libraries like scikit-learn.

Lesson 5: Model Evaluation and Validation

Understanding key metrics: accuracy, precision, recall, F1 score.
Techniques for model evaluation: cross-validation, train/test split.
Addressing overfitting and underfitting issues in machine learning models.

Lesson 6: Data Science Projects and Real-World Applications

Applying learned skills in end-to-end data science projects.
Building predictive models and solving real-world problems using data.
Best practices for presenting data insights and results to stakeholders.

Lesson 1: Introduction to Data Science

Overview of data science and its applications in various industries.
Key concepts: data collection, analysis, and interpretation.
Introduction to the data science workflow and common tools (Python, R, SQL).

Lesson 2: Data Cleaning and Preprocessing

Understanding the importance of data cleaning in the data science process.
Techniques for handling missing data, outliers, and duplicates.
Preprocessing methods like normalization, standardization, and encoding categorical data.

Lesson 3: Exploratory Data Analysis (EDA)

Introduction to EDA techniques for understanding data patterns.
Using visualization tools (matplotlib, seaborn) to analyze datasets.
Identifying trends, correlations, and distributions in data.

Lesson 4: Introduction to Machine Learning Algorithms

Overview of supervised vs. unsupervised learning.
Introduction to basic machine learning algorithms (linear regression, decision trees, k-means clustering).
Implementing algorithms using libraries like scikit-learn.

Lesson 5: Model Evaluation and Validation

Understanding key metrics: accuracy, precision, recall, F1 score.
Techniques for model evaluation: cross-validation, train/test split.
Addressing overfitting and underfitting issues in machine learning models.

Lesson 6: Data Science Projects and Real-World Applications

Applying learned skills in end-to-end data science projects.
Building predictive models and solving real-world problems using data.
Best practices for presenting data insights and results to stakeholders.

Lesson 1: Introduction to Data Science

Overview of data science and its applications in various industries.
Key concepts: data collection, analysis, and interpretation.
Introduction to the data science workflow and common tools (Python, R, SQL).

Lesson 2: Data Cleaning and Preprocessing

Understanding the importance of data cleaning in the data science process.
Techniques for handling missing data, outliers, and duplicates.
Preprocessing methods like normalization, standardization, and encoding categorical data.

Lesson 3: Exploratory Data Analysis (EDA)

Introduction to EDA techniques for understanding data patterns.
Using visualization tools (matplotlib, seaborn) to analyze datasets.
Identifying trends, correlations, and distributions in data.

Lesson 4: Introduction to Machine Learning Algorithms

Overview of supervised vs. unsupervised learning.
Introduction to basic machine learning algorithms (linear regression, decision trees, k-means clustering).
Implementing algorithms using libraries like scikit-learn.

Lesson 5: Model Evaluation and Validation

Understanding key metrics: accuracy, precision, recall, F1 score.
Techniques for model evaluation: cross-validation, train/test split.
Addressing overfitting and underfitting issues in machine learning models.

Lesson 6: Data Science Projects and Real-World Applications

Applying learned skills in end-to-end data science projects.
Building predictive models and solving real-world problems using data.
Best practices for presenting data insights and results to stakeholders.

Review of this Course

4.9

(1.2k Review)

James Patel,

Jan 15, 2025

"Learly made learning easy and fun. I explored data science and UI/UX design at my own pace. The detailed courses and hands-on projects helped me build confidence and real skills. Highly recommend it!"

James Patel,

Jan 15, 2025

James Patel,

Jan 15, 2025

Mike Brown,

Feb 12, 2025

"Learly provided an engaging and effective learning experience. I explored programming and graphic design at my own pace. The well-structured courses and practical projects helped me gain confidence and real-world skills. Highly recommend it!"

Mike Brown,

Feb 12, 2025

Mike Brown,

Feb 12, 2025

Kane Williamson

Feb 12, 2025

"I had high hopes, but this program exceeded every expectation. The instructors were knowledgeable, and the resources provided were top notch. Highly recommend course from learnly!"

Kane Williamson

Feb 12, 2025

"I had high hopes, but this program exceeded every expectation. The instructors were knowledgeable, and the resources provided were top notch. Highly recommend course from learnly!"

Kane Williamson

Feb 12, 2025

"I had high hopes, but this program exceeded every expectation. The instructors were knowledgeable, and the resources provided were top notch. Highly recommend course from learnly!"

Price of this course

299.99

USD

Enrolled Student:

1,100

Enrolled Student:

1,100

Enrolled Student:

1,100

Course level:

Advanced

Course level:

Advanced

Course level:

Advanced

Lesson:

Language:

English

Language:

English

Language:

English

Subtitles:

English, Spanish, French

Subtitles:

English, Spanish, French

Subtitles:

English, Spanish, French

Additional recourses:

12 files

Additional recourses:

12 files

Additional recourses:

12 files

Duration:

25h 30min

Duration:

25h 30min

Duration:

25h 30min

Certificate:

Upon completion of the course

Certificate:

Upon completion of the course

Certificate:

Upon completion of the course

Get Access to This course

Assignment

Plan to dedicate a minimum of 1–2 hours per day to watch course videos, complete data analysis exercises, and work on hands-on projects to apply your learning and build a solid foundation in data science.

Assignment

Prerequisites

Basic understanding of mathematics, statistics, and programming concepts (preferably in Python). Familiarity with tools like Excel or Google Sheets will be beneficial but not required.

Prerequisites

Basic understanding of mathematics, statistics, and programming concepts (preferably in Python). Familiarity with tools like Excel or Google Sheets will be beneficial but not required.

Prerequisites

Basic understanding of mathematics, statistics, and programming concepts (preferably in Python). Familiarity with tools like Excel or Google Sheets will be beneficial but not required.

Materials

Access to Python programming language and libraries like NumPy, pandas, and Matplotlib (free versions). A laptop or desktop with at least 8GB of RAM and a stable internet connection is required for optimal performance. Additional resources such as datasets and code samples will be provided during the course.

Materials