← Back to the catalogueData Science with Python
Beginner to Advanced
Level
Classroom . Online . Hybrid
Mode
Course overview
A complete data science course in Python, from raw files to a communicated result. The course spends its first half on the work that dominates real projects collection, cleaning, exploration and statistics and its second half on modelling and communication. Every module uses public datasets from Kaggle and government portals.
Learners produce a portfolio of three notebooks and one dashboard.
Who it is for
- Graduates targeting data science roles
- Analysts working in Excel or SQL today
- Developers moving into data work
- Research and academic staff
Prerequisites
Basic programming logic. Python is taught from the beginning.
How it runs
Learn → Practise → Build → Experience → Demonstrate, ending in a capstone. Delivered by practitioners from the engineering bench, in Madurai, Coimbatore and online.
Final project
End-To-End Data Science Case Study
Programme sheet
The printed sheet carries the full module breakdown, labs, project work and certification path. Fees, dates and formats for the next intake are confirmed by the education team on enquiry.
Learning outcomes
01
Load and reshape data from files, APIs and databases.
02
Clean and validate messy real-world datasets.
03
Run exploratory analysis and state findings clearly.
04
Apply descriptive and inferential statistics correctly.
05
Engineer features and build baseline models.
06
Visualise results for technical and business readers.
07
Document and publish reproducible notebooks.
Module structure
9 modulesSyntax . Data structures . Functions . Files . Jupyter and Colab workflow
Module 02
NumPy and pandas
Arrays . Broadcasting . Series and DataFrames . Indexing . Joins . Group-by . Time series
JSON and APIs . SQL queries from Python . Web scraping basics . Data ethics
Missing values . Type errors . Text normalisation . Duplicates . Outliers . Validation rules
Lab
Cleaning a Public Dataset
Module 05
Exploratory Data Analysis
Distributions . Segmentation . Anomalies . Pivots . Forming hypotheses
Lab
EDA Notebook . Correlation
Descriptive statistics . Distributions . Probability . Hypothesis tests . A/B testing
Matplotlib . Seaborn . Plotly . Annotation . Dashboard basics
Module 08
Modelling Basics
Feature engineering . Baseline models . Regression and classification . Validation . Metrics
Module 09
Communicating Results
Narrative structure . Executive summaries . Reproducibility . Version control . Portfolio publishing
Assessment & certification
Module assignments and labs
25%
Final project and review
40%
Learners who complete all modules, submit the final project and clear the review receive a course completion certificate from Kaizen Infinities Private Limited. Project work is documented for the learner's portfolio, and interview preparation is included in the closing sessions.
Career outcomes . Roles this programme prepares for
Data ScientistData AnalystPython Developer (data)Business AnalystResearch Analyst
