Data Science with Python

Batch Price From £360 (approx. $465 USD) View Dates & Prices
Custom Price From £240 (approx. $310 USD) Price Calculator & Booking Short course on Data Science with Python

Total Duration: 12-Hour
Course level: Beginner to Intermediate
Delivery Method: Instructor-led Physical Classes or Virtual Classes
Certification: Certificate of Completion will be provided after completing the course

Course Overview

This course is designed to provide students with the knowledge and skills needed to perform data science using Python programming language. Students will learn to apply machine learning libraries such as Pandas, Seaborn and scikit-learn. The course will cover topics such as data cleaning, exploratory data analysis, machine learning algorithms, model evaluation, and selecting the appropriate model for business use.

Learning Objectives:

Understand the fundamental concepts of data science
Perform exploratory data analysis and visualization using Matplotlib and Seaborn libraries
Build predictive models using machine learning algorithms such as linear regression, logistic regression, decision trees, and random forests

Requirements

Basic programming knowledge in Python, familiarity with libraries like Numpy, Pandas, and Matplotlib.

We highly recommend you complete the following course(s) before attending the Data Science with Python course:

Data Analysis with Python

You may also complete the following course(s) before attending the Data Science with Python course but they are not mandatory:

Course Dates, Prices & Enrolment

All Training Physical Classes Virtual Classes

Time Zone:

Training Method	Dates and Times	Total Hours & Days	Price
Online Training using Zoom	01 Jun 2025 - 22 Jun 2025 4 Sundays 10:00 AM - 01:00 PM ET	12-hour over 4-day	£360 £420 (approx. $465 USD)	Enrol Now
Online Training using Zoom	23 Jun 2025 - 04 Jul 2025 Mon, Wed & Fri (2 wks) 09:30 AM - 11:30 AM ET	12-hour over 6-day	£360 £420 (approx. $465 USD)	Enrol Now
Online Training using Zoom	30 Jun 2025 - 02 Jul 2025 Mon, Tue & Wed 05:00 AM - 09:00 AM ET	12-hour over 3-day	£360 £420 (approx. $465 USD)	Enrol Now
Classroom Training	30 Jun 2025 - 02 Jul 2025 Mon, Tue & Wed 05:00 AM - 09:00 AM ET	12-hour over 3-day	£420 (approx. $543 USD)	Enrol Now
Online Training using Zoom	04 Aug 2025 - 06 Aug 2025 Mon, Tue & Wed 05:00 AM - 09:00 AM ET	12-hour over 3-day	£360 £420 (approx. $465 USD)	Enrol Now
Classroom Training	04 Aug 2025 - 06 Aug 2025 Mon, Tue & Wed 05:00 AM - 09:00 AM ET	12-hour over 3-day	£420 (approx. $543 USD)	Enrol Now
Online Training using Zoom	10 Aug 2025 - 31 Aug 2025 4 Sundays 10:00 AM - 01:00 PM ET	12-hour over 4-day	£360 £420 (approx. $465 USD)	Enrol Now
Online Training using Zoom	08 Sep 2025 - 10 Sep 2025 Mon, Tue & Wed 05:00 AM - 09:00 AM ET	12-hour over 3-day	£360 £420 (approx. $465 USD)	Enrol Now
Classroom Training	08 Sep 2025 - 10 Sep 2025 Mon, Tue & Wed 05:00 AM - 09:00 AM ET	12-hour over 3-day	£420 (approx. $543 USD)	Enrol Now
Online Training using Zoom	19 Oct 2025 - 09 Nov 2025 4 Sundays 10:00 AM - 01:00 PM ET	12-hour over 4-day	£360 £420 (approx. $465 USD)	Enrol Now
Online Training using Zoom	03 Nov 2025 - 14 Nov 2025 Mon, Wed & Fri (2 wks) 09:30 AM - 11:30 AM ET	12-hour over 6-day	£360 £420 (approx. $465 USD)	Enrol Now

Course Content

Introduction to Data Science in Python
- Overview of data science
- Introduction to Python for data science
Exploratory Data Analysis and Feature Selection
- Data visualization techniques
- Data exploration with Pandas and Matplotlib
- What are features and how are they important
Machine Learning Basics
- Introduction of Overfitting and Underfitting
- Class Imbalance and its solution
- Download the dataset from Kaggle
- Data splitting and data preparation for the Machine learning model
- Machine learning Basics and Types
- Supervised, Unsupervised, Reinforcement learning with example
Supervised Learning Algorithms
- Linear regression
- Logistic regression
- Decision trees and random forests
- Project: Wine Quality Predictions
Model Evaluation and Selection
- Model evaluation metrics
- Cross-validation
- Model selection techniques
- Ensemble Model
- Model save and load
Real-world Examples
- Some real-life business applications
- Q&A and course feedback

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