Data Science Bundle – 3 in 1

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Nitty Gritty

• This is a 3 course bundle on Data Science.
• Course 01: Data Science and Machine Learning with R.
• Course 02: Python for Data Science and Machine Learning.
• Course 03: Ethical Hacking from Scratch.
• Multiple certificate available.

Last audited and Updated on : September 2025

Learning Outcome

• Master essential data science principles and machine learning workflows using both R and Python programming languages.
• Learn how to clean, manipulate, and visualise structured and unstructured data using R and Python libraries.
• Implement machine learning algorithms like logistic regression, decision trees, and K-means for real-world data solutions.
• Gain hands-on experience in data preprocessing, feature engineering, and hypothesis testing for accurate model development.
• Build interactive and user-friendly web applications with R Shiny to showcase data insights dynamically and effectively.

• Explore advanced Python tools such as NumPy and Pandas to manage and analyse large-scale datasets efficiently.
• Learn ethical hacking techniques, including reconnaissance, Wi-Fi hacking, and post-exploitation in a legal framework.
• Set up a personal hacking lab using tools like Linux, Bash, and PowerShell for practical cybersecurity training.
• Understand critical cybersecurity areas including mobile phone security, web application vulnerabilities, and online anonymity.
• Prepare for careers in data science, machine learning, or cybersecurity with skills aligned to industry demands.

Course Overview

The Data Science Bundle – 3 in 1 course is a comprehensive learning package that includes three highly impactful courses in one bundle: Data Science and Machine Learning with R, Python for Data Science and Machine Learning, and Ethical Hacking from Scratch. This unique bundle is designed for individuals looking to gain proficiency in both data science and machine learning using R and Python, while also acquiring essential ethical hacking skills to enhance cybersecurity knowledge.

Course 01: Data Science and Machine Learning with R starts by introducing learners to Data Science and Machine Learning using R, guiding them from the basics all the way to advanced techniques. You will learn the essential concepts of Data Types and Structures in R, explore Data Manipulation techniques, and discover the power of Data Visualisation in R. 

The course progresses into Building Webapps with R Shiny, allowing you to create interactive visualisations. Key machine learning topics, including Data Preprocessing, Linear Regression, and Logistic Regression, are covered thoroughly. 

You’ll also engage in Exploratory Data Analysis (EDA) to understand how to work with data before applying machine learning algorithms. The course concludes with insights on how to Start a Career in Data Science, preparing you for the next steps in your career.

Course 02: Python for Data Science and Machine Learning focuses on Python, one of the most versatile programming languages in the world of data science. You will learn key Python concepts for data science, including Statistics, Probability, and Hypothesis Testing. 

The course dives into essential data manipulation libraries such as NumPy and Pandas, showing you how to handle large datasets efficiently. You’ll explore advanced machine learning concepts, including Linear and Logistic Regression, Decision Trees, Random Forests, Support Vector Machines, and K-Means. 

Additionally, the course introduces techniques for Data Loading, Data Exploration, Data Cleaning, and Feature Selection. By the end of the course, you will have a solid understanding of machine learning algorithms and will be ready to apply these techniques in real-world scenarios.

Course 03: Ethical Hacking from Scratch takes a deep dive into the world of cybersecurity, teaching you how to perform ethical hacking in a legal and responsible manner. Starting with Networking Basics, you will learn how to set up your own Hacking Lab and familiarize yourself with essential tools such as Linux, Python, Bash, and PowerShell. 

The course covers a range of hacking techniques, from Wi-Fi Hacking and Reconnaissance to Launching Attacks and Post-Exploitation. You will also explore Web Application Hacking, Mobile Phone Security, and Staying Anonymous on the Web. The course concludes with strategies for making money as an ethical hacker and starting a career in Cybersecurity.

This bundle equips you with a solid foundation in both data science and ethical hacking. Whether you’re seeking to enter the field of data science, machine learning, or cybersecurity, this course will provide you with the skills to succeed in all these areas.

Who This Course Is For:

• Aspiring data scientists
• Machine learning enthusiasts
• Python and R programmers
• Individuals interested in cybersecurity and ethical hacking
• Software engineers looking to expand into data science
• Business analysts looking to use data science for decision-making
• Students aiming for a career in data science, machine learning, or cybersecurity
• Professionals wanting to enhance their skillset with ethical hacking knowledge

Requirements:

• No prior knowledge of data science or machine learning is required.
• Basic understanding of programming concepts is helpful.
• Enthusiasm for learning and exploring data science, machine learning, and cybersecurity techniques.

Course Instructor

Juan Galvan​

Juan Galvan is an experienced entrepreneur with a diverse background in Digital Marketing, E-Commerce, Web Development, and Programming. He brings real-world experience to his teaching, focusing on practical, actionable skills that learners can immediately apply. Juan’s expertise in both technical and business aspects of data science and cybersecurity makes him the perfect instructor for this comprehensive course. His practical approach ensures that students not only learn theoretical concepts but also gain the hands-on skills needed to succeed in their careers.

Accreditation

This Data Science Bundle – 3 in 1 course is accredited by CPDQE, ensuring the training meets high professional development standards. The course covers key aspects of Data Science, Machine Learning, and Ethical Hacking, offering relevant, up-to-date content recognized by employers across industries. With this CPDQE accreditation, learners gain a certification that demonstrates their knowledge and commitment to these in-demand fields. Completing this accredited bundle enhances your credibility and provides you with valuable skills in data science, machine learning, and cybersecurity, preparing you for various career opportunities in tech.

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Frequently Asked Questions

Everything you need to now about he product billing.

What does data science do?

Data science extracts insights from structured and unstructured data using techniques from statistics, machine learning, and programming. It helps businesses make data-driven decisions, automate processes, and solve complex problems across industries.

Which is better, Computer Science (CS) or Data Science (DS)?

Both are valuable, but serve different goals. Computer Science focuses on software engineering, algorithms, and systems. Data Science applies statistical methods, coding, and analytics to solve real-world problems using data. Choose based on your career interests—CS for development, DS for analytics and decision-making.

What are the 4 types of data science?

The four main types of data science tasks are:

• Descriptive Analytics – What happened?
• Diagnostic Analytics – Why did it happen?
• Predictive Analytics – What is likely to happen?
• Prescriptive Analytics – What should be done?

Is data science a good career?

Yes, data science is a high-demand, future-proof career with strong growth prospects across tech, finance, healthcare, retail, and more. It offers excellent salaries, intellectual challenges, and opportunities to make impactful decisions.

Is data science a lot of math?

Data science involves math, particularly statistics, linear algebra, and probability. However, modern tools and libraries handle much of the complexity. A solid understanding of the concepts is more important than advanced mathematics.

Is AI killing data science jobs?

No. AI is transforming data science, not replacing it. AI automates repetitive tasks, but data scientists are still essential for interpreting results, designing experiments, and creating models tailored to business goals.

Is 30 too late for data science?

Absolutely not. Many professionals successfully transition into data science in their 30s, 40s, or beyond. With the right training, mindset, and practical experience, age is not a barrier in this field.

Can AI do a data scientist job?

AI can assist with data processing and model generation, but it cannot replace the critical thinking, domain expertise, and ethical decision-making that data scientists provide. Human judgment remains essential in interpreting data.

Is data science worth it in 2025?

Yes. Data science remains a valuable career in 2025, as demand for data-driven insights continues to grow. Emerging fields like AI, IoT, and automation will further increase the need for skilled data professionals.

How to become a data scientist?

Start by learning Python, statistics, and machine learning. Build strong foundations in data wrangling, visualization, and databases. Work on real-world projects, earn certifications, and stay updated with tools like pandas, scikit-learn, and TensorFlow.

Is data scientist a stressful job?

Like any high-responsibility role, data science can be stressful at times—especially with tight deadlines and large datasets. However, good time management, clear goals, and strong team support can reduce stress levels.

Do I need a PhD to become a data scientist?

No, a PhD is not required. Many data scientists hold bachelor’s or master’s degrees. What matters most is practical experience, portfolio projects, and your ability to apply data science to solve problems.

Are data scientist jobs in danger?

While automation is evolving, data scientist roles are adapting, not disappearing. Skills in AI integration, business communication, and ethical data use are making data scientists even more critical in modern organisations.

Which job is less stressful?

Jobs in data analytics, reporting, or business intelligence tend to be less stressful than high-pressure roles in real-time machine learning or AI research. However, stress depends more on company culture and workload than on the title.

Is data science a happy career?

Yes, many data scientists report high job satisfaction due to challenging work, good salaries, and opportunities to make meaningful contributions. The ability to solve real-world problems is especially rewarding.

Is data science high paying?

Yes, data science is among the highest-paying tech careers. Salaries vary by location and experience but typically range from $70,000 to over $150,000 annually. Senior roles and specialised skills earn even more.

Is data science hard to study?

Data science can be challenging due to its interdisciplinary nature—combining coding, math, and domain knowledge. However, with structured learning and hands-on projects, it is very achievable, even for beginners.

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