Applied Data Science with Python Specialization Certificate

This repository contains the most recent versions of all projects and peer assessments for the Applied Data Science with Python Coursera specialization.

Applied Plotting, Charting & Data Representation in Python

• Basic Charting

  Manipulate data and demonstrate procedure of composit charts.

• Charting Fundamentals

  Create insightful plots, build complex features using artist layer and add animation and interactivity to visualizations.

• Applied Visualizations

  Project stating a research question and visuals addressing it.

Applied Machine Learning in Python

• Intro to Scikit Learn

  Build and evaluate basic k-nearest neighbours classifier on a breast cancer dataset.

• Supervised Machine Learning, Part 1

  Understand the strengths and weaknesses of a particular supervised learning method and apply techniques like regularization, feature scaling and cross validation to avoid common pitfalls.

• Evaluation

  Optimize a machine learning algorithm using a specific evaluation metric appropriate for a given task.

• Supervised Machine Learning, Part 2

  Projecy to determine whether a given blight ticket (fine) of property maintainance in City of Detroit will be paid on time.

Applied Text Mining in Python

• Working with Text

  Perform text cleaning and write regular expressions to find textual patterns

• Basic Natural Language Processing

  Build two spelling recommender systems based on jaccard distance and edit distance.

• Classification of Text

  Explore text message data and create a model to classify message as spam or not.

• Topic Modeling

  Build a Gensim's LDA (Latent Dirichlet Allocation) model to model topics in news data and extract 10 topics.

Applied Social Network Analysis in Python

• Basics on NetworkX

  Construct and manipulate networks of different types using different network classes and node and edge attributes in NetworkX.

• Network Connectivity

  Process and analyze an internal communication network between employees of mid-sized manufacturing company.

• Influence Measures and Network Centralization

  Explore measures of centrality on two networks i.e, friendship and blog network.

• Network Evolution

  Work with a company email network and predict the probability of node receiving management level salary. Also, use the current network to predict the future connections.