Course Summary
The Data Science Program at Ruqtec is a focused 6-month curriculum designed to equip you with the knowledge and skills needed to excel in data science. Covering data manipulation, analysis, and visualization, as well as machine learning and SQL, this program is ideal for those passionate about leveraging data for insights and decision-making. Participants will master Python and SQL for data manipulation, explore diverse datasets, and build predictive models. By course end, they'll be adept at data analysis, visualization, and machine learning, primed for data science roles in various industries, contributing to data-driven innovation.
Price
#250,000 40% off
#150,000
Payment Mode
- One Time Payment
- Installment Payment
Duration
6 months
Next Cohort starts by August 13th, 2026
Requirement & Prerequisite
- No coding experience required
- Laptop (minimum of 4GB RAM )
- Access to internet
Classes Delivery Mode
100% online
Course Content
Data Science Fundamentals and Basic Python
Introduction to Python; Operators in Python and Control Flow; List and Loops in Python; Python Functions; Setting up a python virtual environment; Dictionaries in Python; Working with Files in Python
Object Oriented Programming
Fundamentals of Object Oriented Programming; Classes in Python; Methods and attributes of a Python class; Methods of a class and methods with arguments; Class constructor; Instant and Class variables in OOP
Relational Database Management System
Introduction to Database for Data Science; Understand Relational Database Management System; Data Manipulation using SQL; Intermediate SQL; Python with Database; Aggregates in SQLite
The Data Analysis processes
Programming workflow for Data Analysis; Use cases; Exploratory Data Analysis (EDA); Use Pandas and Seaborn for EDA
Data Manipulation & Visualization in Python
Data Cleaning in Python; Python Lambda Functions; basic statistics in python; Introduction to Pandas & Introduction to Numpy; Introduction to Data Visualization in Python; Data Visualization with Matplotlib & seaborn; Version Control System & Exploratory Data Analysis
Introduction to Machine learning
Types of machine learning: supervised; unsupervised & Reinforcement learning; Applications of machine learning Machine learning use cases; Machine learning models & algorithms
Capstone Project, Portfolio Building and Deployment
Final Project; Portfolio Building; LinkedIn and CV Optimization; Deployment Strategies and Best Practices; Course Conclusion and Graduation