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Data Science with Python

Overview

This course is tailored to equip students with the essential skills to embark on a journey in data science using Python. With a focus on practical application, participants will delve into utilizing key machine learning libraries including Pandas, Seaborn, and scikit-learn. Through comprehensive coverage of topics such as data cleaning, exploratory data analysis, and machine learning algorithms, students will develop a solid foundation to tackle real-world data challenges.

Participants will gain proficiency in critical data science tasks, from preprocessing and analyzing data to evaluating and selecting the most suitable models for business applications. By the end of the course, students will emerge equipped with the knowledge and expertise to navigate the intricacies of data science, poised to make informed decisions and drive impactful insights in diverse professional settings.

Learning Objectives

Learning Objectives of this course are:

  • Gain a solid grasp of the core principles underlying data science.
  • Master exploratory data analysis and visualization techniques through Matplotlib and Seaborn libraries.
  • Develop predictive modeling skills using a range of machine learning algorithms, including linear regression, logistic regression, decision trees, and random forests.

Requirements

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

Course Contents

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 are they 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 Machine 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

Why Choose Us?

Our aim is not just to get you the qualification but also guide you through all processes while you are applying for your SIA Licence. Following are some of the reasons why you should choose us as your training provider:

  • Excellent Success Rate!
  • Fully accredited and authorized training center in London!
  • Most competitive prices with NO HIDDEN COSTS!
  • Prices are all inclusive of tuition, handbook, notebook, exams and certificate costs!
  • Nationally recognized and Ofqual accredited training course!
  • Highly skilled and qualified trainers with a great wealth of practical knowledge & experience!
  • Post-qualification email support while you are preparing to book your licence application!
  • Free guidance session on how to apply and obtain a SIA Licence!

Course Fee

£360

This Course Includes:

Total Duration
12 Hours
Course Level
Beginner
Delivery Method
Online Training

Certification:

Certificate of Completion will be provided after completing the course.

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