Programme in Data Science and Machine Learning

The Data science and Machine Learning program is a certification course offered by Cambtech.


Nagur Ramesh, Embedded AI Developer
Nandhini M, Data Scientist

About the course

This course provides an introduction to the fundamental concepts and practical applications of data science and machine learning.

Participants will learn about the data science process, key concepts in data science, data collection and preprocessing techniques, exploratory data analysis, data visualization, machine learning algorithms, and the ethical considerations in data science.

The course also includes a hands-on project where participants will analyze a dataset and apply their knowledge to extract insights and build predictive models.

What will you learn

  • Definition and scope of data science.

  • The role of data scientists in various industries.

  • Real-world applications of data science.

  • Overview of the data science lifecycle and its iterative nature.

  • Understanding data types, big data challenges, and introduction to machine learning.

  • Techniques for data collection, preprocessing, and exploratory data analysis.

  • Importance of data visualization and popular visualization tools.

  • Introduction to machine learning algorithms and building predictive models.

  • Ethical considerations in data science and emerging trends.

Who is this for

  • Students and professionals interested in entering the field of data science and machine learning.
  • Professionals looking to enhance their skills and knowledge in data analysis and machine learning techniques.
  • Anyone interested in understanding the practical applications of data science across various industries.


  • Comprehensive coverage of fundamental concepts and practical applications.

  • Hands-on project to apply knowledge and skills.

  • Expert-led instruction with real-world examples.

  • Interactive sessions and discussions.

  • Networking opportunities with peers and industry experts.


  • There are no specific eligibility criteria for this course. Basic understanding of mathematics and statistics is beneficial but not mandatory.


  • Applicants must have completed 12th grade or its equivalent from a recognized educational board.

Course content

  • What is Data Science?

    • Definition and scope of data science.
    • Understanding the role of data scientists in various industries.
    • Real-world applications of data science.
  • The Data Science Process

    • Overview of the data science lifecycle.
    • Steps involved in data science projects (e.g., data collection, data cleaning, analysis, visualization, modeling, evaluation).
    • Understanding the iterative nature of data science.
  • Key Concepts in Data Science

    • Data types: structured, unstructured, and semi-structured data.
    • Big data and its challenges.
    • Introduction to machine learning and artificial intelligence.
  • Data Collection

    • Sources of data (e.g., databases, APIs, web scraping).
    • Best practices for collecting data.
    • Data ethics and privacy considerations.
  • Data Preprocessing

    • Understanding the importance of data cleaning.
    • Handling missing data and outliers.
    • Data transformation techniques (e.g., normalization, encoding categorical variables).
  • Exploratory Data Analysis (EDA)

    • The role of EDA in understanding data.
    • Visualizing data distributions and patterns.
    • Identifying correlations and insights.
  • Importance of Data Visualization

    • The power of visual communication.
    • Choosing the right visualization for different types of data.
  • Data Visualization Tools

    • Introduction to popular data visualization libraries (e.g., Matplotlib, Seaborn, Plotly).
    • Hands-on practice creating basic visualizations.
  • Designing Effective Visualizations

    • Best practices for creating clear and informative visualizations.
    • Avoiding common pitfalls and misleading representations.
  • What is Machine Learning?

    • Understanding the basic concepts and types of machine learning (supervised, unsupervised, reinforcement learning).
    • Real-world applications of machine learning.
  • Building a Machine Learning Model

    • Overview of the machine learning workflow.
    • Training and testing datasets.
    • Evaluation metrics for model performance.
  • Machine Learning Algorithms

    • Introduction to popular algorithms (e.g., linear regression, decision trees, k-nearest neighbors).
    • Understanding their strengths and weaknesses.
  • Data Science in Different Industries

    • How data science is applied in fields like finance, healthcare, marketing, and more.
    • Success stories and case studies.
  • Ethical Considerations in Data Science

    • Addressing bias and fairness in algorithms.
    • Ensuring data privacy and security.
  • Emerging Trends in Data Science

    • AI advancements and their impact on data science.
    • Challenges and opportunities in the future of data science.
  • Project

    • Analyzing Student Exam Performance
₹ 17700
₹ 20000
Cambtech Certification Course
About this course



30 Hours

Downloadable Files:
  • IIT-M Pravartak Certification
  • Comprehensive Content
  • Industry expert instructors
  • Case studies and Real-world examples
  • Time flexibility
  • Cost-effective
  • Live sessions
  • Mentorship sessions

Nagur Ramesh

Live Sessions

Nandhini M

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