Careers360 Logo
Browse by Stream
Search Colleges, Exams, Schools & more
Interested in this College?
Get updates on Eligibility, Admission, Placements Fees Structure
Compare

Quick Facts

Medium Of InstructionsMode Of LearningMode Of Delivery
EnglishSelf StudyVideo and Text Based

Courses and Certificate Fees

Fees InformationsCertificate AvailabilityCertificate Providing Authority
INR 2421yesCoursera

The Syllabus

Videos
  • Welcome and Introduction
  • Introduction to Data Science
  • What is Data?
  • Types of Data
  • Machine Learning
  • Supervised vs Unsupervised Learning
  • K-Means Clustering
  • Preparing your Data
  • A Real World Dataset
Practice Exercise
  • Types of Data – Review Information
  • Supervised vs Unsupervised – Review Information
  • K-Means Clustering – Review Information
  • Week 1 Summative Assessment

Videos
  • 2.0: Week 2 Introduction
  • 2.1 – Introduction to Mathematical Concepts of Data Clustering
  • 2.2 – Mean of One Dimensional Lists
  • 2.3 – Variance and Standard Deviation
  • 2.4 Jupyter Notebooks
  • 2.5 Variables
  • 2.6 Lists
  • 2.7 Computing the Mean
  • 2.8 Better Lists: NumPy
  • 2.9 Computing the Standard Deviation
  • Week 2 Conclusion
Readings
  • Population vs Sample, Bias
  • Variability, Standard Deviation and Bias
  • Python Style Guide
  • Numpy and Array Creation
Practice Exercise
  • Population vs Sample – Review Information
  • Mean of One Dimensional Lists – Review Information
  • Variance and Standard Deviation – Review Information
  • Jupyter Notebooks – Review Information
  • Variables – Review Information
  • Lists – Review Information
  • Computing the Mean – Review Information
  • Better Lists – Review Information
  • Computing the Standard Deviation – Review Information
  • Week 2 Summative Assessment

Videos
  • Week 3 Introduction
  • 3.1 Multidimensional Data Points and Features
  • 3.2 Multidimensional Mean
  • 3.3 Dispersion: Multidimensional Variables
  • 3.4 Distance Metrics
  • 3.5 Normalisation
  • 3.6 Outliers
  • 3.7 Basic Plotting
  • 3.7a Storing 2D Coordinates in a Single Data Structure
  • 3.8 Multidimensional Mean
  • 3.9 Adding Graphical Overlays
  • 3.10 Calculating the Distance to the Mean
  • 3.11 List Comprehension
  • 3.12 Normalisation in Python
  • 3.13 Outliers and Plotting Normalised Data
  • Week 3 Conclusion
Readings
  • Multidimensional Data Points and Features Recap
  • Multidimensional Mean Recap
  • Multidimensional Variables Recap
  • Distance Metrics Recap
  • Normalisation Recap
  • Note on Matplotlib
  • Matplotlib Scatter Plot Documentation
  • Matplotlib Patches Documentation
  • List Comprehension Documentation
  • 3.12 Errata
Practice Exercise
  • Multidimensional Data Points and Features – Review Information
  • Multidimensional Mean – Review Information
  • Dispersion: Multidimensional Variables – Review Information
  • Distance Metrics – Review Information
  • Normalisation – Review Information
  • Outliers – Review Information
  • Basic Plotting – Review Information
  • Storing 2D Coordinates – Review Information
  • Multidimensional Mean – Review Information
  • Adding Graphical Overlays – Review Information
  • Calculating Distance – Review Information
  • List Comprehension – Review Information
  • Normalisation in Python – Review Information
  • Outliers – Review Information
  • Week 3 Summative Assessment

Videos
  • Week 4 Introduction
  • 4.1: Using the Pandas Library to Read csv Files
  • 4.1a: Sorting and Filtering Data Using Pandas
  • 4.1b: Labelling Points on a Graph
  • 4.1c: Labelling all the Points on a Graph
  • 4.2: Eyeballing the Data
  • 4.3: Using K-Means to Interpret the Data
  • Week 4: Conclusion
Readings
  • Week 4 Code Resources
  • Pandas Read_CSV Function
  • More Pandas Library Documentation
  • The Pyplot Text Function
  • For Loops in Python
  • Documentation for sklearn.cluster.KMeans
Practice Exercise
  • Using the Pandas Library to Read csv Files – Review Information
  • Sorting and Filtering Data Using Pandas – Review Information
  • Labelling Points on a Graph – Review Information
  • Labelling all the Points on a Graph – Review Information
  • Eyeballing the Data – Review Information
  • Using K-Means to Interpret the Data – Review Information
  • Week 4 Summative Assessment

Videos
  • Introduction to Week 5
  • 5.1 Can a Machine Detect Fake Notes?
  • 5.2 Working for a Client
  • 5.3 How to Organize Work on Your Project
  • 5.4 Dealing With Difficulties
  • 5.5 No Data no Data Science: Introduction of the Dataset
  • 5.6 Modelling
  • 5.7 Presenting the Project Results
  • 5.8 Concluding Remarks
Readings
  • Week 5 Code Resource – the Dataset for our Project
  • Saving plt.scatter Outputs as Figures
  • Additional Recommended Reading for Week 5
Practice Exercise
  • How Would You Help? – Review Information
  • Python – Review Information
  • Week 5 Summative Assessment

Instructors

Articles

Similar College

Similar Courses

Data Science for Business Innovation

Polytechnic University of Milan, Milan via European Institute of Innovation and Technology Coursera

Months 4Free

Tools for Data Science

IBM via  Coursera

Months 74.12 K

Applied Data Science Capstone

IBM via  Coursera

Months 54.12 K
View All Courses

More Courses by University of London, London

Citizenship and the Rule of Law

University of London, London via  Coursera

Months 6Free

Responsive Web Design

University of London, London via Goldsmiths, University of London, London Coursera

Months 34.12 K

Mathematics for Computer Science

University of London, London via Goldsmiths, University of London, London Coursera

Months 63.27 K
View All Courses

Explore on Careers360

Download Careers360 App
All this at the convenience of your phone

  • Regular Exam Updates

  • Best College Recommendations

  • College & Rank predictors

  • Detailed Books and Sample Papers

  • Question and Answers

Scan and download the app

OR

Back to top