What is Data Science?

Course Overview

Begin exploring the extraordinary world of digital marketing today, and take your initial steps towards the career you have always dreamed of through learning the most up-to-date digital marketing strategies.

This comprehensive course mainly focuses on teaching you practical techniques for promoting your goods, service or business through practising different digital platforms such as website promoting, email marketing, Youtube and Video Marketing, Copywriting, Blogging & SEO, Facebook, Twitter, Instagram, LinkedIn, Periscope and Pinterest, Podcasting, Google Analytics and Google Plus, etc.

To sum up, this What is Data Science? course is suitable for anyone with little knowledge of digital marketing and wishing to develop their online selling skills further or refresh their skills.

Learning Outcomes

Whether you are an aspiring professional or a complete beginner, this course will improve your expertise and boost your CV with key skills and an accredited certification attesting to your knowledge.

Entry Requirement

- This course is available to all learners of all academic backgrounds.
- Learners should be aged 16 or over to undertake the qualification.
- Some basic understanding of the English language and numeracy.

Certification

After you have successfully completed the course, you will be able to obtain an Accredited Certificate of Achievement. You can, however also obtain a Course Completion Certificate following the course completion without sitting for the test. Certificates can be obtained either in hardcopy at the cost of £29 or in PDF format at the cost of £19.

- PDF certificate’s turnaround time is 24 hours, and for the hardcopy certificate, it is 3-9 working days

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 qualification;
  - 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

What is Data Science? is a useful qualification to possess and would be beneficial for any related profession or industry.

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