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Quick Facts

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

Courses and Certificate Fees

Fees InformationsCertificate AvailabilityCertificate Providing Authority
INR 2436yesCoursera

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

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