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Advanced Programme in Data Science AI & Machine Learning

Advanced Programme in Data Science

This advanced year-long programme takes you into full data science. You'll build the Python and statistics foundation, learn to work with data, and move into machine-learning models and real predictive projects. It's a deep, career-focused course for one of the highest-paid and most in-demand fields in tech.

Duration
1 Year
Format
Live 1-on-1
Level
Professional
Certificate
1. Osian 2. NSDC
Overview

What this course is about

Data science becomes meaningful when numbers are connected to a real question and a responsible decision. The Advanced Programme in Data Science uses live one-on-one teaching to develop that complete workflow. You will practise Python, pandas, visualisation, statistics, data cleaning, feature preparation and introductory machine learning. The programme is suitable for graduates, working professionals and motivated learners who want to build evidence-based projects rather than only watch demonstrations.

Lessons may use Jupyter notebooks, NumPy, pandas, Matplotlib, Seaborn and scikit-learn with datasets that resemble business, education, retail or operations problems. You will learn to inspect missing values, avoid leakage, compare models, choose useful metrics and explain findings in plain language. Depending on your starting point, the teacher can strengthen Python and SQL foundations before introducing regression, classification, clustering or basic model evaluation.

A capstone can move from a question and data dictionary to exploratory analysis, feature decisions, model comparison, visual storytelling and a concise recommendation. Flexible data science classes online help professionals study around work while receiving direct code review and project feedback. This advanced programme differs from a beginner analytics course through deeper Python practice, statistical reasoning and machine learning workflow, while keeping the project understandable to a non-technical reviewer.

By the end

What you’ll be able to do

  • Formulate a data question with measurable analysis objectives
  • Clean, transform and inspect tabular datasets using Python
  • Create visualisations that reveal useful patterns and exceptions
  • Apply suitable statistical and machine learning techniques
  • Evaluate models with relevant metrics and honest limitations
  • Present a reproducible data project for technical discussions
Syllabus

How the course flows

  1. 1
    Python & Maths for AIModule 1

    The Python, statistics and linear-algebra basics ML runs on.

  2. 2
    Working with DataModule 2

    Loading, cleaning and exploring datasets that models can learn from.

  3. 3
    Core ML AlgorithmsModule 3

    Regression, classification and clustering — how models actually learn.

  4. 4
    Model Training & EvaluationModule 4

    Training, testing and measuring whether a model is any good.

  5. 5
    AI ProjectModule 5

    A complete build, from raw data to a working, evaluated model.

Toolkit

Tools you’ll use

  • Python
  • NumPy & pandas
  • scikit-learn
  • TensorFlow / Keras
  • Jupyter Notebook
Audience

Who this course is for

  • Graduates moving from non-technical study into data work
  • Working analysts expanding from spreadsheets into Python
  • Engineers seeking an applied machine learning foundation
  • Business professionals wanting stronger evidence-based decisions
  • College learners building a serious analytics portfolio
Where it leads

Roles this opens up

  • Junior Data Analyst

    Clean datasets, create reports and explain patterns that support operational decisions.

  • Data Science Trainee

    Assist with feature preparation, model experiments, evaluation and project documentation.

  • Business Intelligence Associate

    Turn structured business information into dashboards, summaries and actionable questions.

Questions

Before you enrol

Basic computer use is enough to begin, though prior Python or mathematics exposure can help. The learning plan can strengthen programming and statistics before progressing to model building.

Typical practice uses Python, Jupyter, NumPy, pandas, Matplotlib, Seaborn and scikit-learn, with SQL added when appropriate. Tool choices remain connected to the project question rather than being listed without practice.

Yes, interpretation, metric selection, assumptions and limitations are part of the workflow. You will practise turning notebook findings into a concise explanation that a business or project reviewer can understand.

Yes, flexible live lessons can fit around office hours and personal commitments. Individual guidance also makes it possible to spend more time on the exact Python or statistics topic causing difficulty.

It adds stronger Python, statistics and machine learning workflow alongside exploratory analysis and reporting. A beginner analytics course normally focuses more on spreadsheets, dashboards and introductory data handling.

You can discuss a complete dataset project covering the question, cleaning, exploration, modelling, evaluation and recommendation. The important evidence is your reasoning and limitations, not a claim that every model is production-ready.