Anaconda Training
Data Science Foundations
At the conclusion of this 4-day course you will have a solid understanding of how Anaconda
Enterprise and the Python ecosystem work together to help you perform quantitative and
qualitative analyses. This course covers the core libraries for data processing and analysis,
statistical computation, and an overview of machine learning. You’ll learn how to access tabular
data stored in various file formats along with data stored in relational databases and distributed
storage systems.
Preparation
Students will connect to an Anaconda Enterprise instance maintained by the Anaconda Training
department.
This course is meant for all levels of Python and Data Science backgrounds. However, these
DataCamp courses are very useful to provide background preparation.
• Intro to Python for Data Science
• Intermediate Python for Data Science
Curriculum
See following pages for a detailed outline of each section.
• Getting Started with Anaconda Enterprise (1 day)
• Essential Pandas (1 day)
• Access Big Data with Anaconda Enterprise (1/2 day)
• Statistical Modeling and Analysis (1/2 day)
• Machine Learning with Scikit-Learn (1 day)
About Anaconda, Inc.
With more than 13 million users, Anaconda is the world’s most popular data science platform and the foundation of modern machine
learning. Anaconda Enterprise delivers data science and machine learning at speed and scale, unleashing the full potential of our
customers’ data science and machine learning initiatives.
206 E 9th Street, Floor 18, Austin, TX 78701
anaconda.com
Day 1
Getting Started with Anaconda Enterprise
Duration: 1 day
Anaconda Enterprise Platform
• Log into Anaconda Enterprise
Overview of Projects, Deployments, and Channels
• Working with projects
Install packages and environments
Commit and share projects
• Working in Sessions
Jupyter Notebook
JupyterLab
Zeppelin
Review Core Python Syntax
• Python concepts & constructs
Python object model & modules
Flow control
Functions
• Data structures
Methods/attributes for common data structures
Idioms for slicing, indexing, iteration, and comprehension
Files & file I/O: common methods & idioms, context managers
206 E 9th Street, Floor 18, Austin, TX 78701
anaconda.com
Day 2
Essential Pandas
Duration: 1 day
• Data Exploration
reading data sources
selections & summary statistics
data analysis methods
filtering data using logical conditions
plotting in Jupyter notebooks
• Data Formats
Flat text files: CSV, TSV
Binary Formats: HDF5, SAS, Excel, databases
Structured files: JSON
• Data Processing
using vectorized operations
transforming strings & datetimes
• Time series
creating & using datetime indexes
resampling time series
using rolling windows
• Grouping
Grouping data by column values
resampling time series
• Merging & Joining DataFrames
appending & concatenating DataFrames
joins/merges on Index
merges on multiple columns
merging with missing values
206 E 9th Street, Floor 18, Austin, TX 78701
anaconda.com
Day 3
Access Big Data with Anaconda Enterprise
Duration: 1/2 day
• Accessing Databases
Connect with SQLAlchemy
Execute queries
Retrieve results
• PySpark
HIVE tables and storage
DataFrame objects
Processing Hadoop files
Statistical Modeling and Analysis
Duration: 1/2 day
• Overview of scipy.stats
• Sampling empirical distributions
• Construct PDF and CDF
• Hypothesis testing
• Statsmodels
linear regression
regression analysis
logistic regression
building design matrices with R-like equations
206 E 9th Street, Floor 18, Austin, TX 78701
anaconda.com
Day 4
Machine Learning with Scikit-Learn
Duration: 1 day
Supervised Learning
• Model Training and validation
• Regression problems
Linear models
Support vector machines
Decision trees
• Classification problems
K-nearest neighbors classification
Naive Bayes classification
Support vector machines
Decision trees and ensemble strategies
• Model building and scoring
Scoring functions & cross-validation
Feature selection
Feature extraction
• Pipelines
• Grid search parameter optimization
Unsupervised Learning
• Feature extraction
• Clustering problems
K-means & hierarchical clustering
DBScan
• Dimensionality reduction
PCA, LDA, NMF
• Detection & treatment of outliers
206 E 9th Street, Floor 18, Austin, TX 78701
anaconda.com
