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Computer Science & IT Support Data Science, AI & Machine Learning Programming & Web Development

Course Features

Price

Original price was: £490.00.Current price is: £14.99.

Study Method

Online | Self-paced

Course Format

Interactive PDFs, Articles & Learning Resources

Duration

11 hours, 25 minutes

Qualification

Professional Skills Development Course

Assessment

Final MCQ Exam (included in price)

Certificate

Verifiable Digital Certificate - Free

Additional info

Lifetime Access | Start Instantly

  • Share
Python Data Science workspace with code, datasets, charts and visualizations representing Pandas, NumPy, Matplotlib and Seaborn.

Overview

Python Data Science is a practical learning journey designed to help you understand how professionals collect, prepare, analyse, and communicate insights from real-world data. This course introduces the essential tools and techniques required to work confidently with data using Python, making it suitable for anyone who wants to develop strong foundations in modern data analysis and visualization.

Throughout this course, you will explore powerful Python libraries widely used in the data science industry, including Pandas, NumPy, Matplotlib, and Seaborn. You will learn how to work with structured data, perform data manipulation, clean datasets, handle missing values, manage different data formats, and transform raw information into meaningful insights. The course focuses on practical skills that help you understand how data professionals approach complex datasets and prepare them for analysis.

You will gain hands-on experience with important data operations such as filtering, sorting, grouping, combining datasets, working with indexes, handling dates and times, performing advanced queries, and applying useful data processing techniques. The course also introduces numerical computing concepts with NumPy, helping you understand arrays, tensors, and efficient data operations that support advanced analytical workflows.

A major focus of the learning experience is data visualization. You will discover how to create clear and informative charts using Matplotlib and develop advanced visual storytelling techniques with Seaborn. These skills will help you present patterns, trends, relationships, and business insights in a way that is easier for others to understand and use for decision-making.

The course also includes practical examples and case studies, including applications of data science in finance and a bonus project exploring transfer learning for sales prediction. These activities help connect technical concepts with real-world problem-solving scenarios.

By completing this course, you will develop a stronger understanding of the data science workflow and build skills that can support academic projects, professional growth, and future learning in machine learning and artificial intelligence.

After successfully completing the course, learners will receive a free course completion certificate. Multiple premium certificate and transcript options are also available for purchase if you wish to obtain additional professional documentation. Students also receive access to our 5-star-rated support available 24/7 through email for guidance whenever assistance is required.
This course is ideal for aspiring data analysts, students, developers, business professionals, and anyone interested in turning data into meaningful insights. It is suitable for learners who want to strengthen their Python skills, explore analytical workflows, or prepare for further study in machine learning, artificial intelligence, and advanced data-driven technologies.
Basic programming knowledge and familiarity with fundamental Python concepts are recommended. Understanding variables, functions, and general computer operations will help you progress smoothly. No previous experience with Pandas, NumPy, or data visualization libraries is required, as the course introduces these concepts step by step.
After completing this course, learners can move towards roles such as Data Analyst, Junior Data Scientist, Business Intelligence Analyst, Python Developer, or Machine Learning Trainee. The skills gained also provide a strong foundation for advanced studies in machine learning, artificial intelligence, predictive analytics, and professional data science projects.

Who is this course for?

Python Data Science is a practical learning journey designed to help you understand how professionals collect, prepare, analyse, and communicate insights from real-world data. This course introduces the essential tools and techniques required to work confidently with data using Python, making it suitable for anyone who wants to develop strong foundations in modern data analysis and visualization.

Throughout this course, you will explore powerful Python libraries widely used in the data science industry, including Pandas, NumPy, Matplotlib, and Seaborn. You will learn how to work with structured data, perform data manipulation, clean datasets, handle missing values, manage different data formats, and transform raw information into meaningful insights. The course focuses on practical skills that help you understand how data professionals approach complex datasets and prepare them for analysis.

You will gain hands-on experience with important data operations such as filtering, sorting, grouping, combining datasets, working with indexes, handling dates and times, performing advanced queries, and applying useful data processing techniques. The course also introduces numerical computing concepts with NumPy, helping you understand arrays, tensors, and efficient data operations that support advanced analytical workflows.

A major focus of the learning experience is data visualization. You will discover how to create clear and informative charts using Matplotlib and develop advanced visual storytelling techniques with Seaborn. These skills will help you present patterns, trends, relationships, and business insights in a way that is easier for others to understand and use for decision-making.

The course also includes practical examples and case studies, including applications of data science in finance and a bonus project exploring transfer learning for sales prediction. These activities help connect technical concepts with real-world problem-solving scenarios.

By completing this course, you will develop a stronger understanding of the data science workflow and build skills that can support academic projects, professional growth, and future learning in machine learning and artificial intelligence.

After successfully completing the course, learners will receive a free course completion certificate. Multiple premium certificate and transcript options are also available for purchase if you wish to obtain additional professional documentation. Students also receive access to our 5-star-rated support available 24/7 through email for guidance whenever assistance is required.
This course is ideal for aspiring data analysts, students, developers, business professionals, and anyone interested in turning data into meaningful insights. It is suitable for learners who want to strengthen their Python skills, explore analytical workflows, or prepare for further study in machine learning, artificial intelligence, and advanced data-driven technologies.
Basic programming knowledge and familiarity with fundamental Python concepts are recommended. Understanding variables, functions, and general computer operations will help you progress smoothly. No previous experience with Pandas, NumPy, or data visualization libraries is required, as the course introduces these concepts step by step.
After completing this course, learners can move towards roles such as Data Analyst, Junior Data Scientist, Business Intelligence Analyst, Python Developer, or Machine Learning Trainee. The skills gained also provide a strong foundation for advanced studies in machine learning, artificial intelligence, predictive analytics, and professional data science projects.

Requirements

Python Data Science is a practical learning journey designed to help you understand how professionals collect, prepare, analyse, and communicate insights from real-world data. This course introduces the essential tools and techniques required to work confidently with data using Python, making it suitable for anyone who wants to develop strong foundations in modern data analysis and visualization.

Throughout this course, you will explore powerful Python libraries widely used in the data science industry, including Pandas, NumPy, Matplotlib, and Seaborn. You will learn how to work with structured data, perform data manipulation, clean datasets, handle missing values, manage different data formats, and transform raw information into meaningful insights. The course focuses on practical skills that help you understand how data professionals approach complex datasets and prepare them for analysis.

You will gain hands-on experience with important data operations such as filtering, sorting, grouping, combining datasets, working with indexes, handling dates and times, performing advanced queries, and applying useful data processing techniques. The course also introduces numerical computing concepts with NumPy, helping you understand arrays, tensors, and efficient data operations that support advanced analytical workflows.

A major focus of the learning experience is data visualization. You will discover how to create clear and informative charts using Matplotlib and develop advanced visual storytelling techniques with Seaborn. These skills will help you present patterns, trends, relationships, and business insights in a way that is easier for others to understand and use for decision-making.

The course also includes practical examples and case studies, including applications of data science in finance and a bonus project exploring transfer learning for sales prediction. These activities help connect technical concepts with real-world problem-solving scenarios.

By completing this course, you will develop a stronger understanding of the data science workflow and build skills that can support academic projects, professional growth, and future learning in machine learning and artificial intelligence.

After successfully completing the course, learners will receive a free course completion certificate. Multiple premium certificate and transcript options are also available for purchase if you wish to obtain additional professional documentation. Students also receive access to our 5-star-rated support available 24/7 through email for guidance whenever assistance is required.
This course is ideal for aspiring data analysts, students, developers, business professionals, and anyone interested in turning data into meaningful insights. It is suitable for learners who want to strengthen their Python skills, explore analytical workflows, or prepare for further study in machine learning, artificial intelligence, and advanced data-driven technologies.
Basic programming knowledge and familiarity with fundamental Python concepts are recommended. Understanding variables, functions, and general computer operations will help you progress smoothly. No previous experience with Pandas, NumPy, or data visualization libraries is required, as the course introduces these concepts step by step.
After completing this course, learners can move towards roles such as Data Analyst, Junior Data Scientist, Business Intelligence Analyst, Python Developer, or Machine Learning Trainee. The skills gained also provide a strong foundation for advanced studies in machine learning, artificial intelligence, predictive analytics, and professional data science projects.

Career path

Python Data Science is a practical learning journey designed to help you understand how professionals collect, prepare, analyse, and communicate insights from real-world data. This course introduces the essential tools and techniques required to work confidently with data using Python, making it suitable for anyone who wants to develop strong foundations in modern data analysis and visualization.

Throughout this course, you will explore powerful Python libraries widely used in the data science industry, including Pandas, NumPy, Matplotlib, and Seaborn. You will learn how to work with structured data, perform data manipulation, clean datasets, handle missing values, manage different data formats, and transform raw information into meaningful insights. The course focuses on practical skills that help you understand how data professionals approach complex datasets and prepare them for analysis.

You will gain hands-on experience with important data operations such as filtering, sorting, grouping, combining datasets, working with indexes, handling dates and times, performing advanced queries, and applying useful data processing techniques. The course also introduces numerical computing concepts with NumPy, helping you understand arrays, tensors, and efficient data operations that support advanced analytical workflows.

A major focus of the learning experience is data visualization. You will discover how to create clear and informative charts using Matplotlib and develop advanced visual storytelling techniques with Seaborn. These skills will help you present patterns, trends, relationships, and business insights in a way that is easier for others to understand and use for decision-making.

The course also includes practical examples and case studies, including applications of data science in finance and a bonus project exploring transfer learning for sales prediction. These activities help connect technical concepts with real-world problem-solving scenarios.

By completing this course, you will develop a stronger understanding of the data science workflow and build skills that can support academic projects, professional growth, and future learning in machine learning and artificial intelligence.

After successfully completing the course, learners will receive a free course completion certificate. Multiple premium certificate and transcript options are also available for purchase if you wish to obtain additional professional documentation. Students also receive access to our 5-star-rated support available 24/7 through email for guidance whenever assistance is required.
This course is ideal for aspiring data analysts, students, developers, business professionals, and anyone interested in turning data into meaningful insights. It is suitable for learners who want to strengthen their Python skills, explore analytical workflows, or prepare for further study in machine learning, artificial intelligence, and advanced data-driven technologies.
Basic programming knowledge and familiarity with fundamental Python concepts are recommended. Understanding variables, functions, and general computer operations will help you progress smoothly. No previous experience with Pandas, NumPy, or data visualization libraries is required, as the course introduces these concepts step by step.
After completing this course, learners can move towards roles such as Data Analyst, Junior Data Scientist, Business Intelligence Analyst, Python Developer, or Machine Learning Trainee. The skills gained also provide a strong foundation for advanced studies in machine learning, artificial intelligence, predictive analytics, and professional data science projects.

    • Why You’re Here and What We Will Achieve 00:10:00
    • Prerequisites for Data Science and This Course 00:10:00
    • System Setup and Requirements Check 00:10:00
    • Understanding Pandas Series 00:10:00
    • Introduction to Pandas for Data Science (Part 1) 00:10:00
    • Pandas Data Manipulation Techniques (Part 2) 00:10:00
    • Data Cleaning with Pandas (Part 3) 00:10:00
    • Advanced Pandas Functions (Part 4) 00:10:00
    • Broadcasting Operations in Pandas 00:10:00
    • Counting and Aggregation Methods 00:10:00
    • Handling Missing Data – Common Challenges 00:10:00
    • Strategies for Dealing with Missing Values 00:10:00
    • Ensuring Correct Data Types and Formats 00:10:00
    • Sorting and Organizing Dataframes 00:10:00
    • Data Slicing Techniques (Part 1) 00:10:00
    • Data Slicing Techniques (Part 2) 00:10:00
    • Detecting Missing Values in Data 00:10:00
    • Machine Learning Insight: Full Case Study 00:10:00
    • Mastering Date and Time Data 00:10:00
    • Removing and Handling Duplicate Data 00:10:00
    • Working with Indexes Effectively 00:10:00
    • Advanced Slicing Techniques 00:10:00
    • More on Data Slicing 00:10:00
    • Additional Data Science Techniques in Pandas 00:10:00
    • Data Querying with Pandas 00:10:00
    • Handling Date Data – Advanced (Part 1) 00:10:00
    • Handling Date Data – Advanced (Part 2) 00:10:00
    • Handling Date Data – Advanced (Part 3) 00:10:00
    • Handling Date Data – Advanced (Part 4) 00:10:00
    • Grouping Data – Beginner to Advanced 00:10:00
    • MultiIndexing in Pandas 00:10:00
    • Data Science Applications in Finance 00:10:00
    • Combining DataFrames In-depth 00:10:00
    • String Manipulation and Regex Examples 00:10:00
    • Bonus Tips & Tricks for Pandas (Part 1) 00:10:00
    • Bonus Tips & Tricks for Pandas (Part 2) 00:10:00
    • Bonus Tips & Tricks for Pandas (Part 3) 00:10:00
    • What Are Tensors and Why Use NumPy? 00:10:00
    • NumPy Basics (Part 1) 00:10:00
    • NumPy Basics (Part 2) 00:10:00
    • NumPy Basics (Part 3) 00:10:00
    • NumPy Basics (Part 4) 00:10:00
    • Getting Started with Matplotlib 00:10:00
    • Advanced Matplotlib Techniques (Part 1) 00:10:00
    • Advanced Matplotlib Techniques (Part 2) 00:10:00
    • Introduction to Seaborn 00:10:00
    • Mastering Seaborn – Part 1 00:10:00
    • Mastering Seaborn – Part 2 00:10:00
    • Mastering Seaborn – Part 3 00:10:00
    • Mastering Seaborn – Part 4 00:10:00
    • Mastering Seaborn – Part 5 00:10:00
    • Mastering Seaborn – Part 6 00:10:00
    • Mastering Seaborn – Part 7 00:10:00
    • Mastering Seaborn – Part 8 00:10:00
    • Mastering Seaborn – Part 9 00:10:00
    • Mastering Seaborn – Part 10 00:10:00
    • Mastering Seaborn – Part 11 00:10:00
    • Mastering Seaborn – Part 12 00:10:00
    • Mastering Seaborn – Part 13 00:10:00
    • Mastering Seaborn – Part 14 00:10:00
    • Course Wrap-Up and What to Do Next 00:10:00
    • Bonus: Using Transfer Learning to Predict Ice Cream Sales 00:10:00
    • Exam of Become a Data Scientist: Comprehensive Guide with Python and Visualization 00:50:00
    • Premium Certificate 00:15:00
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Yes, our premium certificate and transcript are widely recognized and accepted by embassies worldwide, particularly by the UK embassy. This adds credibility to your qualification and enhances its value for professional and academic purposes.

Yes, this course is designed for learners of all levels, including beginners. The content is structured to provide step-by-step guidance, ensuring that even those with no prior experience can follow along and gain valuable knowledge.

Yes, professionals will also benefit from this course. It covers advanced concepts, practical applications, and industry insights that can help enhance existing skills and knowledge. Whether you are looking to refine your expertise or expand your qualifications, this course provides valuable learning.

No, you have lifetime access to the course. Once enrolled, you can revisit the materials at any time as long as the course remains available. Additionally, we regularly update our content to ensure it stays relevant and up to date.

I trust you’re in good health. Your free certificate can be located in the Achievement section. The option to purchase a CPD certificate is available but entirely optional, and you may choose to skip it. Please be aware that it’s crucial to click the “Complete” button to ensure the certificate is generated, as this process is entirely automated.

Yes, the course includes both assessments and assignments. Your final marks will be determined by a combination of 20% from assignments and 80% from assessments. These evaluations are designed to test your understanding and ensure you have grasped the key concepts effectively.

We are a recognized course provider with CPD, UKRLP, and AOHT membership. The logos of these accreditation bodies will be featured on your premium certificate and transcript, ensuring credibility and professional recognition.

Yes, you will receive a free digital certificate automatically once you complete the course. If you would like a premium CPD-accredited certificate, either in digital or physical format, you can upgrade for a small fee.

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Course Features

Price

Original price was: £490.00.Current price is: £14.99.

Study Method

Online | Self-paced

Course Format

Interactive PDFs, Articles & Learning Resources

Duration

11 hours, 25 minutes

Qualification

Professional Skills Development Course

Assessment

Final MCQ Exam (included in price)

Certificate

Verifiable Digital Certificate - Free

Additional info

Lifetime Access | Start Instantly

  • Share

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