Overview

Data is the new fuel of the 21st century powering several industries. One might even consider it as the new “black gold”. However, data is worthless until its transformation into invaluable insights, and that is data science.

Data Science is the process of mining massive quantities of structured and unstructured data to uncover hidden patterns and derive meaningful insights. It helps businesses easily comprehend large amounts of data and generate actionable insights to make more informed data-driven choices. The significance of Data Science is in its many applications, which vary from simple tasks like asking Siri or Alexa for suggestions to more sophisticated ones like running a self-driving car. It is one of the most rapidly growing fields and sought-after career paths. If you also have a passion for finding solutions buried in data to address business issues, this Data Science course is for you.

This all-inclusive course learning package will guide you through the ABCs of data science. You will equip yourself with numerous essential knowledge, such as — python essentials, data analysis using NumPy and Pandas, data visualisation using matplotlib, Pandas and Seaborn, etc. Additionally, this course will train you in interactive & geographical plotting using Plotly and Cufflinks.

To strengthen your knowledge basket, this course will also give you a comprehensive idea of machine learning. You will familiarise yourself with various machine learning models like Linear Regression Model, Logistic Regression Model, K Nearest Neighbors, etc. Moreover, this data science course will tell you about the theory of Decision Tree and Random Forests. With this interactive data science training, you will clearly understand crucial topics like Support Vector Machines (SVMs), Principal Component Analysis (PCA), K Means Clustering, etc.

Add this Data Science to the basket — Develop the ability to extract valuable insights from gigabytes of data.

 

What skills you will gain:

  • An extensive overview of data science.
  • Complete understanding of python essentials.
  • In-depth knowledge of data analysis using NumPy and Pandas.
  • A good hold of data visualisation using matplotlib, Pandas and Seaborn.
  • Dexterity in interactive & geographical plotting using Plotly and Cufflinks.
  • Sharpened awareness of machine learning (ML)
  • Comprehension of various machine learning models like Linear Regression Model, Logistic Regression Model, K Nearest Neighbors, etc.
  • Proficiency in the theory of Decision Tree and Random Forests.
  • Step-by-step guidance on Support Vector Machines (SVMs) and Principal Component Analysis (PCA).
  • Expertise in K Means Clustering and Elbow method.

Why buy this Data Science and Visualisation with Machine Learning?

  1. Unlimited access to the course for forever
  2. Digital Certificate, Transcript, student ID all included in the price
  3. Absolutely no hidden fees
  4. Directly receive CPD accredited qualifications after course completion
  5. Receive one to one assistance on every weekday from professionals
  6. Immediately receive the PDF certificate after passing
  7. Receive the original copies of your certificate and transcript on the next working day
  8. Easily learn the skills and knowledge from the comfort of your home

Certification

After studying the course materials of the Data Science and Visualisation with Machine Learning you will be able to take the MCQ test that will assess your knowledge. After successfully passing the test you will be able to claim the pdf certificate for free. Original Hard Copy certificates need to be ordered at an additional cost of £8.

Who is this course for?

This Data Science and Visualisation with Machine Learning does not require you to have any prior qualifications or experience. You can just enrol and start learning. 

Prerequisites

This Data Science and Visualisation with Machine Learning was made by professionals and it is compatible with all PC’s, Mac’s, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.

Career path

Following completion of this Data Science course, you will have a plethora of employment opportunities, including —

  • Data Scientist.
  • Data Architect.
  • Data Analyst.
  • Data Engineer.
  • Machine Learning Engineer.

In the United Kingdom, these occupations are compensated between £25,000 and £85,000 per year.

Course Curriculum

Welcome, Course Introduction & overview, and Environment set-up
Welcome & Course Overview 00:07:00
Set-up the Environment for the Course (lecture 1) 00:09:00
Set-up the Environment for the Course (lecture 2) 00:25:00
Two other options to setup environment 00:04:00
Python Essentials
Python data types Part 1 00:21:00
Python Data Types Part 2 00:15:00
Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) 00:16:00
Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) 00:20:00
Python Essentials Exercises Overview 00:02:00
Python Essentials Exercises Solutions 00:22:00
Python for Data Analysis using NumPy
What is Numpy? A brief introduction and installation instructions. 00:03:00
NumPy Essentials – NumPy arrays, built-in methods, array methods and attributes. 00:28:00
NumPy Essentials – Indexing, slicing, broadcasting & boolean masking 00:26:00
NumPy Essentials – Arithmetic Operations & Universal Functions 00:07:00
NumPy Essentials Exercises Overview 00:02:00
NumPy Essentials Exercises Solutions 00:25:00
Python for Data Analysis using Pandas
What is pandas? A brief introduction and installation instructions. 00:02:00
Pandas Introduction 00:02:00
Pandas Essentials – Pandas Data Structures – Series 00:20:00
Pandas Essentials – Pandas Data Structures – DataFrame 00:30:00
Pandas Essentials – Handling Missing Data 00:12:00
Pandas Essentials – Data Wrangling – Combining, merging, joining 00:20:00
Pandas Essentials – Groupby 00:10:00
Pandas Essentials – Useful Methods and Operations 00:26:00
Pandas Essentials – Project 1 (Overview) Customer Purchases Data 00:08:00
Pandas Essentials – Project 1 (Solutions) Customer Purchases Data 00:31:00
Pandas Essentials – Project 2 (Overview) Chicago Payroll Data 00:04:00
Pandas Essentials – Project 2 (Solutions Part 1) Chicago Payroll Data 00:18:00
Python for Data Visualization using matplotlib
Matplotlib Essentials (Part 1) – Basic Plotting & Object Oriented Approach 00:13:00
Matplotlib Essentials (Part 2) – Basic Plotting & Object Oriented Approach 00:22:00
Matplotlib Essentials (Part 3) – Basic Plotting & Object Oriented Approach 00:22:00
Matplotlib Essentials – Exercises Overview 00:06:00
Matplotlib Essentials – Exercises Solutions 00:21:00
Python for Data Visualization using Seaborn
Seaborn – Introduction & Installation 00:04:00
Seaborn – Distribution Plots 00:25:00
Seaborn – Categorical Plots (Part 1) 00:21:00
Seaborn – Categorical Plots (Part 2) 00:16:00
Seborn-Axis Grids 00:25:00
Seaborn – Matrix Plots 00:13:00
Seaborn – Regression Plots 00:11:00
Seaborn – Controlling Figure Aesthetics 00:10:00
Seaborn – Exercises Overview 00:04:00
Seaborn – Exercise Solutions 00:19:00
Python for Data Visualization using pandas
Pandas Built-in Data Visualization 00:34:00
Pandas Data Visualization Exercises Overview 00:03:00
Panda Data Visualization Exercises Solutions 00:13:00
Python for interactive & geographical plotting using Plotly and Cufflinks
Plotly & Cufflinks – Interactive & Geographical Plotting (Part 1) 00:19:00
Plotly & Cufflinks – Interactive & Geographical Plotting (Part 2) 00:14:00
Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Overview) 00:11:00
Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Solutions) 00:37:00
Capstone Project - Python for Data Analysis & Visualization
Project 1 – Oil vs Banks Stock Price during recession (Overview) 00:15:00
Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 1) 00:18:00
Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 2) 00:18:00
Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 3) 00:17:00
Project 2 (Optional) – Emergency Calls from Montgomery County, PA (Overview) 00:03:00
Python for Machine Learning (ML) - scikit-learn - Linear Regression Model
Introduction to ML – What, Why and Types….. 00:15:00
Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff 00:15:00
scikit-learn – Linear Regression Model – Hands-on (Part 1) 00:17:00
scikit-learn – Linear Regression Model Hands-on (Part 2) 00:19:00
Good to know! How to save and load your trained Machine Learning Model! 00:01:00
scikit-learn – Linear Regression Model (Insurance Data Project Overview) 00:08:00
scikit-learn – Linear Regression Model (Insurance Data Project Solutions) 00:30:00
Python for Machine Learning - scikit-learn - Logistic Regression Model
Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificity…etc. 00:10:00
scikit-learn – Logistic Regression Model – Hands-on (Part 1) 00:17:00
scikit-learn – Logistic Regression Model – Hands-on (Part 2) 00:20:00
scikit-learn – Logistic Regression Model – Hands-on (Part 3) 00:11:00
scikit-learn – Logistic Regression Model – Hands-on (Project Overview) 00:05:00
scikit-learn – Logistic Regression Model – Hands-on (Project Solutions) 00:15:00
Python for Machine Learning - scikit-learn - K Nearest Neighbors
Theory: K Nearest Neighbors, Curse of dimensionality …. 00:08:00
scikit-learn – K Nearest Neighbors – Hands-on 00:25:00
scikt-learn – K Nearest Neighbors (Project Overview) 00:04:00
scikit-learn – K Nearest Neighbors (Project Solutions) 00:14:00
Python for Machine Learning - scikit-learn - Decision Tree and Random Forests
Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging…. 00:18:00
scikit-learn – Decision Tree and Random Forests – Hands-on (Part 1) 00:19:00
scikit-learn – Decision Tree and Random Forests (Project Overview) 00:05:00
scikit-learn – Decision Tree and Random Forests (Project Solutions) 00:15:00
Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs)
Support Vector Machines (SVMs) – (Theory Lecture) 00:07:00
scikit-learn – Support Vector Machines – Hands-on (SVMs) 00:30:00
scikit-learn – Support Vector Machines (Project 1 Overview) 00:07:00
scikit-learn – Support Vector Machines (Project 1 Solutions) 00:20:00
scikit-learn – Support Vector Machines (Optional Project 2 – Overview) 00:02:00
Python for Machine Learning - scikit-learn - K Means Clustering
Theory: K Means Clustering, Elbow method ….. 00:11:00
scikit-learn – K Means Clustering – Hands-on 00:23:00
scikit-learn – K Means Clustering (Project Overview) 00:07:00
scikit-learn – K Means Clustering (Project Solutions) 00:22:00
Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA)
Theory: Principal Component Analysis (PCA) 00:09:00
scikit-learn – Principal Component Analysis (PCA) – Hands-on 00:22:00
scikit-learn – Principal Component Analysis (PCA) – (Project Overview) 00:02:00
scikit-learn – Principal Component Analysis (PCA) – (Project Solutions) 00:17:00
Recommender Systems with Python - (Additional Topic)
Theory: Recommender Systems their Types and Importance 00:06:00
Python for Recommender Systems – Hands-on (Part 1) 00:18:00
Python for Recommender Systems – – Hands-on (Part 2) 00:19:00
Python for Natural Language Processing (NLP) - NLTK - (Additional Topic)
Natural Language Processing (NLP) – (Theory Lecture) 00:13:00
NLTK – NLP-Challenges, Data Sources, Data Processing ….. 00:13:00
NLTK – Feature Engineering and Text Preprocessing in Natural Language Processing 00:19:00
NLTK – NLP – Tokenization, Text Normalization, Vectorization, BoW…. 00:19:00
NLTK – BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes … 00:13:00
NLTK – NLP – Pipeline feature to assemble several steps for cross-validation… 00:09:00
Resources
Resources – Data Science and Visualisation with Machine Learning 00:00:00

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