Digital Badges

Digital badge image

What are Digital Badges?

Each seminar in the Data Science for All series includes an optional, no cost, post-seminar quiz to demonstrate the skills you have learned and earn a digital badge that can be posted on your LinkedIn profile to show future employers the additional skills you are learning outside of the classroom

Python Foundations Digital Badge

Python Foundations

  • Describe the Python programming environment
  • Use Spyder to execute and modify Python programs
  • Interpret, modify and create basic programs in Python
  • Read and write simple text files
Exploring Relationships in Graphs Digital Badge

Exploring Relationships in Graphs

  • Understand the node and edge structure of a graph schema
  • Basics of the Cypher query language working with paths and properties
  • Loading nodes and edges from a file, constraints, indexes, and merge
  • Multi-path queries using aggregation, filtering, and composing queries using WITH clauses
Statistical Foundations Digital Badge

Statistical Foundations

  • Exploratory data analysis using Python libraries such as Numpy and Pandas and Jupyter Notebook
  • Basic probability theory, Bayes Theorem, the Central Limit Theorem, mean, median, mode, and standard deviation
  • Problem solving using Binomial and Normal distribution
  • Data visualization using Matplotlib and Seaborn
Spark and Jupyter Notebooks Digital Badge

Spark and Jupyter Notebooks

  • Creating and working with DataFrames and temporary views in Apache Spark
  • Understand the importance of documenting their work and using markdown in Jupyter notebooks
  • Basic PySpark and Spark SQL queries using DataFrames
  • Visualizing DataFrames as bar, line, and area charts
Data Wrangling Digital Badge

Data Wrangling

  • Listing different sources of data and data classifications
  • Describing the data science system and data wrangling
  • Interpreting, modifying and creating basic Python programs to wrangle data using Pandas in Jupyter notebooks in Google Colaboratory
  • Receiving data input from the keyboard and from text files, and outputting data to the screen and to text files
  • Using Pandas’ data frames to identify and correct simple data anomalies
  • Using Pandas to wrangle data from Spotify
Learning in TensorFlow Digital Badge

Learning in TensorFlow

  • Defining machine learning
  • Describing the history of TensorFlow and keras
  • Interpreting a basic program in Jupyter notebooks for the classic machine learning example of classifying hand-written digits
  • Loading the MNIST dataset
  • Exploring the MNIST dataset
  • Preparing the MNIST dataset
  • Creating a machine learning model to classify hand-written digits
  • Reporting the accuracy of the model
Telling Your Data Story Using Tableau Digital Badge

Telling Your Data Story Using Tableau

  • Importing data and adjusting data types, dimensions, and measures
  • Creating charts, including line, area, bar, 100% stacked bar, box plots, and maps
  • Filtering data, using the Marks Card, setting analysis options, and creating animations
  • Creating parameters, sets, calculated fields, and level of detail (LOD) calculations
Introduction to Machine Learning for Data Science Digital Badge

Introduction to Machine Learning for Data Science

  • Understand various machine learning techniques and their applications, analyze regression, and classification problems
  • Supervised learning, linear regression analysis using Python libraries - Numpy, Pandas, Sklearn
  • Unsupervised learning, solving classification problems using K-Means clustering in Python
  • Analyze the accuracy of machine learning models using commonly used loss functions