Build a Practical Data Science Foundation
Learn how to work with data using Python, statistics, data preparation, visualisation and machine learning foundations. The pathway is designed for students, freshers and professionals moving toward data-driven roles.
Python for Data
Use Python for data handling, analysis and repeatable workflows.
Statistics Foundation
Understand descriptive statistics, probability and analytical thinking.
Machine Learning Path
Progress into supervised and unsupervised learning foundations.
Portfolio Work
Build analysis and modelling projects suitable for discussion in interviews.
Who This Course Is For
- Students and freshers interested in data careers.
- Python learners moving into analytics and machine learning.
- Working professionals who want practical data skills.
- Learners preparing for AI and machine learning pathways.
What You Will Be Able to Do
- Clean and prepare datasets for analysis.
- Use Python libraries and data structures for analytical work.
- Apply statistics to explain and compare data.
- Build basic machine learning workflows.
- Present findings through visualisation and project documentation.
Course Curriculum
Python & Data Foundations
- Python recap
- NumPy concepts
- Pandas-style data handling
- Data cleaning
- Missing values and transformation
- Exploratory analysis
Statistics & Visualisation
- Descriptive statistics
- Probability foundations
- Distributions
- Correlation
- Visualisation principles
- Insight communication
Machine Learning Foundations
- Problem framing
- Train/test thinking
- Regression
- Classification
- Clustering
- Model evaluation concepts
Projects & Workflow
- Dataset selection
- Cleaning pipeline
- Feature preparation
- Model experimentation
- Result presentation
- Portfolio documentation
Projects & Practical Work
Project difficulty is adjusted to the learner's level and batch progress. The goal is to turn concepts into demonstrable work rather than only complete theory modules.
- Exploratory data analysis project
- Business dataset cleaning/reporting project
- Beginner machine learning project
- Data science capstone
Career Preparation
Mango Engineers' learning process can include project review, portfolio/GitHub readiness, resume guidance, mock interview practice and placement assistance. Placement assistance is support, not a job guarantee.
- Data Science pathway
- Junior Data Analyst
- Machine Learning pathway
- Analytics Associate
Frequently Asked Questions
Python knowledge is strongly recommended. Beginners can first complete the Python foundation before moving into data science.
Yes, the pathway introduces core machine learning concepts and practical model workflows.
Yes. Practical data analysis and modelling projects are part of the learning approach.
No. Career preparation and placement assistance can be provided, but employment is not guaranteed.
