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Data Science with Python

Batch Price From £300 (approx. $376.60 USD) View Dates & Prices
Custom Price From £150 (approx. $188.30 USD) Price Calculator & Booking
Short course on Data Science with Python
Total Duration: 10 Hours
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


Basic programming knowledge in Python, familiarity with libraries like Numpy, Pandas, and Matplotlib. Our Python Programming for Beginners and Data Analysis with Python Training Courses cover all of the prerequisites.

Course Dates, Prices & Enrolment

All Training Physical Classes Virtual Classes
Time Zone:
Training MethodDates and TimesPrice 
Online Training using Zoom 30 Jan 2024 - 08 Feb 2024
2 Tuesdays & 2 Thursdays
09:30 AM - 12:00 PM ET
(approx. $376.60 USD)
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Course Content

  1. Introduction to Data Science in Python
    • Overview of data science
    • Introduction to Python for data science
  2. Exploratory Data Analysis and Feature Selection
    • Data visualization techniques
    • Data exploration with Pandas and Matplotlib
    • What are features and how it is important
  3. 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 Mchine learning model
    • Machine learning Basics and Types
    • Supervised, Unsupervised, Reinforcement learning with example
  4. Supervised Learning Algorithms
    • Linear regression
    • Logistic regression
    • Decision trees and random forests
    • Project: Wine Quality Predictions
  5. Model Evaluation and Selection
    • Model evaluation metrics
    • Cross-validation
    • Model selection techniques
    • Ensemble Model
    • Model save and load
  6. Real-world Examples
    • Some real-life business applications
    • Q&A and course feedback

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Python Data Scientist

£688 (save £172)

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Python Data Analytics, AI & ML Immersive

£1,312 (save £328)

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