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

• 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

• 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

• 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

• 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

• 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

• 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

• 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