Data science

Data Science will make you capable of handling and presenting the insights of the data to facilitate decision making. So, what are you waiting for, this is your opportunity to learn real Data Science with a fraction of the cost of any of your undergraduate course

The course also includes practising exercises on real data for each topic you cover, because the goal is "Learn by Doing"!

For your satisfaction, I would like to mention a few topics that we will be learning in this course:

• Data Science - Basis Python programming for Data Science

• Data Science - Data Types, Comparisons Operators, if, else, elif statement, Loops, List Comprehension, Functions, Lambda Expression, Map and Filter

• Data Science - NumPy

• Data Science - Arrays, built-in methods, array methods and attributes, Indexing, slicing, broadcasting & boolean masking, Arithmetic Operations & Universal Functions

• Data Science - Pandas

• Data Science - Pandas Data Structures - Series, DataFrame, Hierarchical Indexing, Handling Missing Data, Data Wrangling - Combining, merging, joining, Groupby, Other Useful Methods and Operations, Pandas Built-in Data Visualization

• Data Science - Matplotlib

• Data Science - Basic Plotting & Object Oriented Approach

• Data Science - Seaborn

• Data Science - Distribution & Categorical Plots, Axis Grids, Matrix Plots, Regression Plots, Controlling Figure Aesthetics

• Data Science - Plotly and Cufflinks

• Data Science - Interactive & Geographical plotting

• Data Science - SciKit-Learn (one of the world's best machine learning Python library) including:

• Data Science - Linear Regression

• Data Science - Over fitting , Under fitting Bias Variance Trade-off, saving and loading your trained Machine Learning Models

• Data Science - Logistic Regression

• Data Science - Confusion Matrix, True Negatives/Positives, False Negatives/Positives, Accuracy, Misclassification Rate / Error Rate, Specificity, Precision

• Data Science - K Nearest Neighbour (KNN)

• Data Science - Curse of Dimensionality, Model Performance

• Data Science - Decision Trees

• Data Science - Tree Depth, Splitting at Nodes, Entropy, Information Gain

• Data Science - Random Forests

• Data Science - Bootstrap, Bagging (Bootstrap Aggregation)

• Data Science - K Mean Clustering

• Data Science - Elbow Method

• Data Science - Principle Component Analysis (PCA)

• Data Science - Support Vector Machine

• Data Science - Recommender Systems

• Data Science - Natural Language Processing (NLP)

• Tokenization, Text Normalization, Vectorization, Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Pipeline feature........and MUCH MORE..........!

Not only the hands-on practice using tens of real data projects, theory lectures are also provided to make you understand the working principle behind the Machine Learning models.

With this Data Science programme, you can successfully upgrade & monetise your skills because this whole course is designed & developed for both sophophile and skill diggers. Also, this course is suitable for both part-time and full-time students and employees and can be completed at a pace that suits and synchronises your learning style and adaptability.

Moreover, This Data Science course is developed by qualified industry experts and crammed with all the essential topics to give you a proper insight and understanding of Goal Settings Mastery - Win At The Game Of Life!, So that you can excel in your personal life or career.

Why People Love and Enrol the Data Science Course from Guardian Academy

• This Data Science Course is Informative & Interactive
• This Data Science Course has HD Quality Audiovisual Training Sessions Relevant to Your Career
• This Data Science Course is Developed By Qualified Industry Professionals
• This Data Science Course is made by Following the UK & EU Standards
• This Data Science Course will provide you with the Real-Life Projects Based Active Learning to Get Real Employable & Marketable Skills
• Benefit from Instant Feedback System Through Mock Exams, Assignment Evaluation or Multiple-Choice Question
• Learn At Your Pace on Your Schedule; Study Wherever, Whenever You Desire
• Easily Affordable & Accessible from Anywhere Through Your Computer, Tablet or Mobile Device
• Committed Happiness Team Ensure Intensive Mentoring Support Culture (24/7 Premium Support Via Live Chat, Email, Telephone)
• Promised To Guide You Success Through Internationally Accredited Courses
• Partnership With Some of The World's Prominent Accreditation Organisations & Top Awarding Bodies
• Earn Instant Digital Certificate Upon Successful Completion of Each Course.
• Best Prices with Regular Savings of up to 25% Over The Standard Recommended Retail Prices.
• Peer Blind Review of Our Courses to Ensure Quality & We Have Decent Refund Policy

Description of the Data Science Course

Data Science: Course Introduction & Overview

• Data Science -Welcome & Course Overview
• Data Science -Please read, it's important for you to know!
• Data Science -Set-up the Environment for the Course (lecture 1)
• Data Science -Set-up the Environment for the Course (lecture 2)
• Data Science -Download environment file and watch next lecture to setup -super-easy way
• Data Science -Two other options to setup environment
• Data Science -Important Note:
• Data Science -Possible updates in the course.

Data Science: Python Essentials

• Data Science -Python data types Part 1
• Data Science -Python Data Types Part 2
• Data Science -Comparisons Operators, if, else, elif statement
• Data Science -Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1)
• Data Science -Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2)
• Data Science -Python Essentials Exercises Overview
• Data Science -Python Essentials Exercises Solutions

Data Science: Python for Data Analysis using NumPy

• Data Science -What is Numpy? A brief introduction and installation instructions.
• Data Science -NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes.
• Data Science -NumPy Essentials - Indexing, slicing, broadcasting & boolean masking
• Data Science -NumPy Essentials - Arithmetic Operations & Universal Functions
• Data Science -NumPy Essentials Exercises Overview
• Data Science -NumPy Essentials Exercises Solutions

Data Science:Python for Data Analysis using Pandas

• Data Science -What is pandas? A brief introduction and installation instructions.
• Data Science -Pandas Introduction.
• Data Science -Pandas Essentials - Pandas Data Structures - Series
• Data Science -Pandas Essentials - Pandas Data Structures - DataFrame
• Data Science -Pandas Essentials - Hierarchical Indexing
• Data Science -Pandas Essentials - Handling Missing Data
• Data Science -Pandas Essentials - Data Wrangling - Combining, merging, joining
• Data Science -Pandas Essentials - Groupby
• Data Science -Pandas Essentials - Useful Methods and Operations
• Data Science -Pandas Essentials - Project 1 (Overview) Customer Purchases Data
• Data Science -Pandas Essentials - Project 1 (Solutions) Customer Purchases Data
• Data Science -Pandas Essentials - Project 2 (Overview) Chicago Payroll Data
• Data Science -Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data
• Data Science -Pandas Essentials - Project 2 (Solutions Part 2) Chicago Payroll Data

Data Science:Python for Data Visualization using matplotlib

• Data Science -Matplotlib Essentials (Part 1) - Basic Plotting & Object-Oriented Approach
• Data Science -Matplotlib Essentials (Part 2) - Basic Plotting & Object-Oriented Approach
• Data Science -Matplotlib Essentials (Part 3) - Basic Plotting & Object-Oriented Approach
• Data Science -Matplotlib Essentials - Exercises Overview
• Data Science -Matplotlib Essentials - Exercises Solutions
• Data Science -Matplotlib Essentials (Optional) - Advance

Data Science:Python for Data Visualization using Seaborn

• Data Science -Seaborn - Introduction & Installation
• Data Science -Seaborn - Distribution Plots
• Data Science -Seaborn - Categorical Plots (Part 1)
• Data Science -Seaborn - Categorical Plots (Part 2)
• Data Science -Seaborn - Axis Grids
• Data Science -Seaborn - Matrix Plots
• Data Science -Seaborn - Regression Plots
• Data Science -Seaborn - Controlling Figure Aesthetics
• Data Science -Seaborn - Exercises Overview
• Data Science -Seaborn - Exercise Solutions

Data Science:Python for Data Visualization using pandas

• Data Science -Pandas Built-in Data Visualization
• Data Science -Pandas Data Visualization Exercises Overview
• Data Science -Panda Data Visualization Exercises Solutions

Data Science:Python for interactive & geographical plotting using Plotly and Cufflinks

• Data Science -Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1)
• Data Science -Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2)
• Data Science -Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview)
• Data Science -Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions)

Data Science:Capstone Project - Python for Data Analysis & Visualization

• Data Science -Project 1 - Oil vs Banks Stock Price during recession (Overview)
• Data Science -Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1)
• Data Science -Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2)
• Data Science -Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3)
• Data Science -Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview)

Data Science:Python for Machine Learning (ML) - scikit-learn - Linear Regression Model

• Data Science -Introduction to ML - What, Why and Types
• Data Science -Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff
• Data Science -A note on student’s concerns and questions on FutureWarnings.
• Data Science -scikit-learn - Linear Regression Model - Hands-on (Part 1)
• Data Science -scikit-learn - Linear Regression Model Hands-on (Part 2)
• Data Science -Good to know! How to save and load your trained Machine Learning Model!
• Data Science -scikit-learn - Linear Regression Model (Insurance Data Project Overview)
• Data Science -scikit-learn - Linear Regression Model (Insurance Data Project Solutions)

Data Science:Python for Machine Learning - scikit-learn - Logistic Regression Model

• Data Science -Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificity...etc.
• Data Science -Output of classification report in scikit-learn — A small change
• Data Science -scikit-learn - Logistic Regression Model - Hands-on (Part 1)
• Data Science -scikit-learn - Logistic Regression Model - Hands-on (Part 2)
• Data Science -scikit-learn - Logistic Regression Model - Hands-on (Part 3)
• Data Science -scikit-learn - Logistic Regression Model - Hands-on (Project Overview)
• Data Science -scikit-learn - Logistic Regression Model - Hands-on (Project Solutions)

Data Science:Python for Machine Learning - scikit-learn - K Nearest Neighbors

• Data Science -Theory: K Nearest Neighbors, Curse of dimensionality
• Data Science -scikit-learn - K Nearest Neighbors - Hands-on
• Data Science -scikt-learn - K Nearest Neighbors (Project Overview)
• Data Science -scikit-learn - K Nearest Neighbors (Project Solutions)

Data Science:Python for Machine Learning - scikit-learn - Decision Tree and Random Forests

• Data Science -Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging
• Data Science -scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1)
• Data Science -scikit-learn - Decision Tree and Random Forests (Project Overview)
• Data Science -scikit-learn - Decision Tree and Random Forests (Project Solutions)

Data Science:Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs)

• Data Science -Support Vector Machines (SVMs) - (Theory Lecture)
• Data Science -scikit-learn - Support Vector Machines - Hands-on (SVMs)
• Data Science -scikit-learn - Support Vector Machines (Project 1 Overview)
• Data Science -scikit-learn - Support Vector Machines (Project 1 Solutions)
• Data Science -scikit-learn - Support Vector Machines (Optional Project 2 - Overview)

Additionally This Data Science Course is Offering,

• Data Science:Python for Machine Learning - scikit-learn - K Means Clustering

  • Data Science -Theory: K Means Clustering, Elbow method
  • Data Science -scikit-learn - K Means Clustering - Hands-on
  • Data Science -scikit-learn - K Means Clustering (Project Overview)
  • Data Science -scikit-learn - K Means Clustering (Project Solutions)

  Data Science:Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA)

  • Data Science -Theory: Principal Component Analysis (PCA)
  • Data Science -scikit-learn - Principal Component Analysis (PCA) - Hands-on
  • Data Science -scikit-learn - Principal Component Analysis (PCA) - (Project Overview)
  • Data Science -scikit-learn - Principal Component Analysis (PCA) - (Project Solutions)

  Data Science:Recommender Systems with Python - (Additional Topic)

  • Data Science - Theory: Recommender Systems their Types and Importance
  • Data Science - Python for Recommender Systems - Hands-on (Part 1)
  • Data Science - Python for Recommender Systems - - Hands-on (Part 2)

  Data Science:Python for Natural Language Processing (NLP) - NLTK - (Additional Topic)

  • Data Science - Natural Language Processing (NLP) - (Theory Lecture)
  • Data Science - NLTK - NLP-Challenges, Data Sources, Data Processing
  • Data Science - NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing
  • Data Science - NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW
  • Data Science - NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes
  • Data Science - NLTK - NLP - Pipeline feature to assemble several steps for cross-validation

Who is this Data Science course for?

For you, if you:

• Want to learn Data Science with Python
• Want to learn Machine Learning with Python
• Are tired of complicated courses and "Learn by Doing"

Requirements for this Data Science Course

• • A PC and passion to be successful!
  • Some experience in programming could be helpful but not required!

• Data Science Director £120,000 - £165,000 per annum
• Data Science Partner £100,000 - £120,000 per annum
• Data Science £90,000-£120,000 per annum
• Data Science Engineer £35,000 - £55,000 per annum
• Data Science Manager £75,000 -£95,000 per annum
• Data Science Consultant £50,000 - £70,000 per annum
• Head of Data Science £100,000 - £110,000 per annum
• Senior Data Science Engineer £50,000 - £75,000 per annum