Name: What is Data Science?
Price: 26.99 GBP
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What is Data Science?

Overview This course provides a step-by-step introduction to data science, covering key concepts, tools, and techniques. You will explore machine …

1 Year Access
Intermediate
5 Students
10 hours, 20 minutes

What is Data Science?

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What is Data Science? Overview

This course provides a step-by-step introduction to data science, covering key concepts, tools, and techniques. You will explore machine learning, data preparation, feature selection, and model evaluation. The course also includes hands-on practice with Python libraries such as NumPy, Pandas, and Matplotlib. By the end, you will be able to work with real-world datasets, build predictive models, and confidently make data-driven decisions.

Learning Outcomes

Explain fundamental data science concepts and machine learning techniques.
Set up and prepare the system environment for data science projects.
Use Python to manipulate data and perform key operations.
Understand and apply data pre-processing techniques effectively.
Explore data visualisation methods to analyse datasets.
Select relevant features using various feature selection techniques.
Evaluate machine learning models using different performance metrics.
Compare different algorithms to choose the best fit for a given problem.
Improve model performance using ensemble methods and parameter tuning.
Deploy machine learning models for real-time predictions.

Who Is This Course For

This course is ideal for beginners who want to enter the field of data science and learn its core concepts. It is also suitable for professionals looking to enhance their skills in data analysis and machine learning. If you are a student, job seeker, or working professional in fields such as IT, business, or engineering, this course will help you build a strong foundation in data science. No prior experience is required, making it perfect for anyone eager to explore data-driven decision-making.

Eligibility Requirements

This course is open to anyone with a basic understanding of computers and mathematics. No prior programming experience is needed, but familiarity with analytical thinking can be helpful. A willingness to learn and explore new concepts is essential to succeed in this course.

Entry Requirements

Age Requirement: Applicants must be 16 or older, making the course accessible to both young learners and adults.
Academic Background: No prior qualifications required, open to all backgrounds.
Language Proficiency: A good understanding of English is essential, as all lessons are in English.
Numeracy Skills: Basic writing and numeracy skills are needed to follow the course content.

Why Choose Us

Affordable, engaging & high-quality e-learning study materials;
Tutorial videos/materials from the industry-leading experts;
Study in a user-friendly, advanced online learning platform;
Efficient exam systems for the assessment and instant result;
The UK & internationally recognised accredited
Access to course content on mobile, tablet or desktop from anywhere, anytime;
The benefit of career advancement opportunities;
24/7 student support via email.

Career Path

A career in data science offers a wide range of opportunities across industries such as healthcare, finance, retail, and technology. Completing this course can lead to roles like data analyst, machine learning engineer, and business intelligence professional. As businesses rely more on data, skilled data science professionals are in high demand. You can expect career growth in areas such as artificial intelligence, predictive analytics, and big data solutions.

Course Curriculum

Course Overview & Table of Contents
	Course Overview & Table of Contents	00:09:00
Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types
	Introduction to Machine Learning – Part 1 – Concepts , Definitions and Types	00:05:00
Introduction to Machine Learning - Part 2 - Classifications and Applications
	Introduction to Machine Learning – Part 2 – Classifications and Applications	00:06:00
System and Environment preparation - Part 1
	System and Environment preparation – Part 1	00:04:00
System and Environment preparation - Part 2
	System and Environment preparation – Part 2	00:06:00
Learn Basics of python - Assignment
	Learn Basics of python – Assignment 1	00:10:00
Learn Basics of python - Assignment
	Learn Basics of python – Assignment 2	00:09:00
Learn Basics of python - Functions
	Learn Basics of python – Functions	00:04:00
Learn Basics of python - Data Structures
	Learn Basics of python – Data Structures	00:12:00
Learn Basics of NumPy - NumPy Array
	Learn Basics of NumPy – NumPy Array	00:06:00
Learn Basics of NumPy - NumPy Data
	Learn Basics of NumPy – NumPy Data	00:08:00
Learn Basics of NumPy - NumPy Arithmetic
	Learn Basics of NumPy – NumPy Arithmetic	00:04:00
Learn Basics of Matplotlib
	Learn Basics of Matplotlib	00:07:00
Learn Basics of Pandas - Part 1
	Learn Basics of Pandas – Part 1	00:06:00
Learn Basics of Pandas - Part 2
	Learn Basics of Pandas – Part 2	00:07:00
Understanding the CSV data file
	Understanding the CSV data file	00:09:00
Load and Read CSV data file using Python Standard Library
	Load and Read CSV data file using Python Standard Library	00:09:00
Load and Read CSV data file using NumPy
	Load and Read CSV data file using NumPy	00:04:00
Load and Read CSV data file using Pandas
	Load and Read CSV data file using Pandas	00:05:00
Dataset Summary - Peek, Dimensions and Data Types
	Dataset Summary – Peek, Dimensions and Data Types	00:09:00
Dataset Summary - Class Distribution and Data Summary
	Dataset Summary – Explaining Correlation	00:11:00
Dataset Summary - Explaining Correlation
	Dataset Summary – Class Distribution and Data Summary	00:09:00
Dataset Summary - Explaining Skewness - Gaussian and Normal Curve
	Dataset Summary – Explaining Skewness – Gaussian and Normal Curve	00:07:00
Dataset Visualization - Using Histograms
	Dataset Visualization – Using Histograms	00:07:00
Dataset Visualization - Using Density Plots
	Dataset Visualization – Using Density Plots	00:06:00
Dataset Visualization - Box and Whisker Plots
	Dataset Visualization – Box and Whisker Plots	00:05:00
Multivariate Dataset Visualization - Correlation Plots
	Multivariate Dataset Visualization – Correlation Plots	00:08:00
Multivariate Dataset Visualization - Scatter Plots
	Multivariate Dataset Visualization – Scatter Plots	00:05:00
Data Preparation (Pre-Processing) - Introduction
	Data Preparation (Pre-Processing) – Introduction	00:09:00
Data Preparation - Re-scaling Data - Part 1
	Data Preparation – Re-scaling Data – Part 1	00:09:00
Data Preparation - Re-scaling Data - Part 2
	Data Preparation – Re-scaling Data – Part 2	00:09:00
Data Preparation - Standardizing Data - Part 1
	Data Preparation – Standardizing Data – Part 1	00:07:00
Data Preparation - Standardizing Data - Part 2
	Data Preparation – Standardizing Data – Part 2	00:04:00
Data Preparation - Normalizing Data
	Data Preparation – Normalizing Data	00:08:00
Data Preparation - Binarizing Data
	Data Preparation – Binarizing Data	00:06:00
Feature Selection - Introduction
	Feature Selection – Introduction	00:07:00
Feature Selection - Uni-variate Part 1 - Chi-Squared Test
	Feature Selection – Uni-variate Part 1 – Chi-Squared Test	00:09:00
Feature Selection - Uni-variate Part 2 - Chi-Squared Test
	Feature Selection – Uni-variate Part 2 – Chi-Squared Test	00:10:00
Feature Selection - Recursive Feature Elimination
	Feature Selection – Recursive Feature Elimination	00:11:00
Feature Selection - Principal Component Analysis (PCA)
	Feature Selection – Principal Component Analysis (PCA)	00:09:00
Feature Selection - Feature Importance
	Feature Selection – Feature Importance	00:06:00
Refresher Session - The Mechanism of Re-sampling, Training and Testing
	Refresher Session – The Mechanism of Re-sampling, Training and Testing	00:12:00
Algorithm Evaluation Techniques - Introduction
	Algorithm Evaluation Techniques – Introduction	00:07:00
Algorithm Evaluation Techniques - Train and Test Set
	Algorithm Evaluation Techniques – Train and Test Set	00:11:00
Algorithm Evaluation Techniques - K-Fold Cross Validation
	Algorithm Evaluation Techniques – K-Fold Cross Validation	00:09:00
Algorithm Evaluation Techniques - Leave One Out Cross Validation
	Algorithm Evaluation Techniques – Leave One Out Cross Validation	00:05:00
Algorithm Evaluation Techniques - Repeated Random Test-Train Splits
	Algorithm Evaluation Techniques – Repeated Random Test-Train Splits	00:07:00
Algorithm Evaluation Metrics - Introduction
	Algorithm Evaluation Metrics – Introduction	00:09:00
Algorithm Evaluation Metrics - Classification Accuracy
	Algorithm Evaluation Metrics – Classification Accuracy	00:08:00
Algorithm Evaluation Metrics - Log Loss
	Algorithm Evaluation Metrics – Log Loss	00:03:00
Algorithm Evaluation Metrics - Area Under ROC Curve
	Algorithm Evaluation Metrics – Area Under ROC Curve	00:06:00
Algorithm Evaluation Metrics - Confusion Matrix
	Algorithm Evaluation Metrics – Confusion Matrix	00:10:00
Algorithm Evaluation Metrics - Classification Report
	Algorithm Evaluation Metrics – Classification Report	00:04:00
Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction
	Algorithm Evaluation Metrics – Mean Absolute Error – Dataset Introduction	00:06:00
Algorithm Evaluation Metrics - Mean Absolute Error
	Algorithm Evaluation Metrics – Mean Absolute Error	00:07:00
Algorithm Evaluation Metrics - Mean Square Error
	Algorithm Evaluation Metrics – Mean Square Error	00:03:00
Algorithm Evaluation Metrics - R Squared
	Algorithm Evaluation Metrics – R Squared	00:04:00
Classification Algorithm Spot Check - Logistic Regression
	Classification Algorithm Spot Check – Logistic Regression	00:12:00
Classification Algorithm Spot Check - Linear Discriminant Analysis
	Classification Algorithm Spot Check – Linear Discriminant Analysis	00:04:00
Classification Algorithm Spot Check - K-Nearest Neighbors
	Classification Algorithm Spot Check – K-Nearest Neighbors	00:05:00
Classification Algorithm Spot Check - Naive Bayes
	Classification Algorithm Spot Check – Naive Bayes	00:04:00
Classification Algorithm Spot Check - CART
	Classification Algorithm Spot Check – CART	00:04:00
Classification Algorithm Spot Check - Support Vector Machines
	Classification Algorithm Spot Check – Support Vector Machines	00:05:00
Regression Algorithm Spot Check - Linear Regression
	Regression Algorithm Spot Check – Linear Regression	00:08:00
Regression Algorithm Spot Check - Ridge Regression
	Regression Algorithm Spot Check – Ridge Regression	00:03:00
Regression Algorithm Spot Check - Lasso Linear Regression
	Regression Algorithm Spot Check – Lasso Linear Regression	00:03:00
Regression Algorithm Spot Check - Elastic Net Regression
	Regression Algorithm Spot Check – Elastic Net Regression	00:02:00
Regression Algorithm Spot Check - K-Nearest Neighbors
	Regression Algorithm Spot Check – K-Nearest Neighbors	00:06:00
Regression Algorithm Spot Check - CART
	Regression Algorithm Spot Check – CART	00:04:00
Regression Algorithm Spot Check - Support Vector Machines (SVM)
	Regression Algorithm Spot Check – Support Vector Machines (SVM)	00:04:00
Compare Algorithms - Part 1 : Choosing the best Machine Learning Model
	Compare Algorithms – Part 1 : Choosing the best Machine Learning Model	00:09:00
Compare Algorithms - Part 2 : Choosing the best Machine Learning Model
	Compare Algorithms – Part 2 : Choosing the best Machine Learning Model	00:05:00
Pipelines : Data Preparation and Data Modelling
	Pipelines : Data Preparation and Data Modelling	00:11:00
Pipelines : Feature Selection and Data Modelling
	Pipelines : Feature Selection and Data Modelling	00:10:00
Performance Improvement: Ensembles - Voting
	Performance Improvement: Ensembles – Voting	00:07:00
Performance Improvement: Ensembles - Bagging
	Performance Improvement: Ensembles – Bagging	00:08:00
Performance Improvement: Ensembles - Boosting
	Performance Improvement: Ensembles – Boosting	00:05:00
Performance Improvement: Parameter Tuning using Grid Search
	Performance Improvement: Ensembles – Voting	00:07:00
Performance Improvement: Parameter Tuning using Random Search
	Performance Improvement: Ensembles – Bagging	00:08:00
Export, Save and Load Machine Learning Models : Pickle
	Export, Save and Load Machine Learning Models : Pickle	00:10:00
Export, Save and Load Machine Learning Models : Joblib
	Export, Save and Load Machine Learning Models : Joblib	00:06:00
Finalizing a Model - Introduction and Steps
	Finalizing a Model – Introduction and Steps	00:07:00
Finalizing a Classification Model - The Pima Indian Diabetes Dataset
	Finalizing a Classification Model – The Pima Indian Diabetes Dataset	00:07:00
Quick Session: Imbalanced Data Set - Issue Overview and Steps
	Quick Session: Imbalanced Data Set – Issue Overview and Steps	00:09:00
Iris Dataset : Finalizing Multi-Class Dataset
	Iris Dataset : Finalizing Multi-Class Dataset	00:09:00
Finalizing a Regression Model - The Boston Housing Price Dataset
	Finalizing a Regression Model – The Boston Housing Price Dataset	00:08:00
Real-time Predictions: Using the Pima Indian Diabetes Classification Model
	Real-time Predictions: Using the Pima Indian Diabetes Classification Model	00:07:00
Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset
	Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset	00:03:00
Real-time Predictions: Using the Boston Housing Regression Model
	Real-time Predictions: Using the Boston Housing Regression Model	00:08:00
Resources
	Resources – Data Science & Machine Learning with Python	00:00:00
Order Certificate
	Order Certificate	00:00:00