Next Level Python for Data Science Training
This course explores using Python for data scientists to perform exploratory data analysis, complex visualizations, and large-scale distributed processing using Big Data. In this course you’ll learn about essential mathematical and statistics libraries such as NumPy, Pandas, SciPy, SciKit-Learn, along with frameworks like TensorFlow and Spark. It also covers visualization tools like matplotlib, PIL, and Seaborn.
Next Level Python for Data Science
Course Overview
Join an engaging hands-on learning environment, where you’ll learn:
- How to work with Python in a Data Science context
- How to use NumPy, Pandas, and MatPlotLib
- How to create and process images with PIL
- How to visualize with Seaborn
- Key features of SciPy and SciKit Learn
- How to interact with Spark using DataFrames
- How to use SparkSQL, MLlib, and Big Data streaming
- This course has a 50% hands-on labs to 50% lecture ratio with engaging instruction,
- demos, group discussions, labs, and project work.
Course Outline
- Python Review
- Python Language
- Essential Syntax
- Lists, Sets, Dictionaries, and Comprehensions
- Functions
- Classes, Modules, and imports
- Exceptions
- iPython
- iPython basics
- Terminal and GUI shells
- Creating and using notebooks
- Saving and loading notebooks
- Ad hoc data visualization
- Web Notebooks (Jupyter)
- NumPy
- NumPy basics
- Creating arrays
- Indexing and slicing
- Large number sets
- Transforming data
- Advanced tricks
- SciPy
- What can SciPy do?
- Most useful functions
- Curve fitting
- Modeling
- Data visualization
- Statistics
- SciPy subpackages
- Clustering
- Physical and mathematical Constants
- FFTs
- Integral and differential solvers
- Interpolation and smoothing
- Input and Output
- Linear Algebra
- Image Processing
- Distance Regression
- Root-finding
- Signal Processing
- Sparse Matrices
- Spatial data and algorithms
- Statistical distributions and functions
- C/C++ Integration
- pandas
- pandas overview
- Dataframes
- Reading and writing data
- Data alignment and reshaping
- Fancy indexing and slicing
- Merging and joining data sets
- matplotlib
- Creating a basic plot
- Commonly used plots
- Ad hoc data visualization
- Advanced usage
- Exporting images
- The Python Imaging Library (PIL)
- PIL overview
- Core image library
- Image processing
- Displaying images
- seaborn
- Seaborn overview
- Bivariate and univariate plots
- Visualizing Linear Regressions
- Visualizing Data Matrices
- Working with Time Series data
- SciKit-Learn Machine Learning Essentials
- SciKit overview
- SciKit-Learn overview
- Algorithms Overview
- Classification, Regression, Clustering, and Dimensionality Reduction
- SciKit Demo
- TensorFlow Overview
- TensorFlow overview
- Keras
- Getting Started with TensorFlow
- PySpark Overview
- Python and Spark
- SciKit-Learn vs. Spark MLlib
- Python at Scale
- PySpark Demo
- RDDs and DataFrames
- DataFrames and Resilient Distributed Datasets (RDDs)
- Partitions
- Adding variables to a DataFrame
- DataFrame Types
- DataFrame Operations
- Dependent vs. Independent variables
- Map/Reduce with DataFrames
- Spark SQL
- Spark SQL Overview
- Data stores: HDFS, Cassandra, HBase, Hive, and S3
- Table Definitions
- Queries
- Spark MLib
- MLib overview
- MLib Algorithms Overview
- Classification Algorithms
- Regression Algorithms
- Decision Trees and forests
- Recommendation with ALS
- Clustering Algorithms
- Machine Learning Pipelines
- Linear Algebra (SVD, PCA)
- Statistics in MLib
- Spark Streaming
- Streaming overview
- Integrating Spark SQL, MLlib, and Streaming
Intended Audience
Data Scientists, Data Engineers, and Software Engineers who are experienced with basic Python and data science.
Prerequisites
Before attending this course, you should have:
- A solid data analytics and data science background
- Python experience
- Topics are covered in-depth and are geared for experienced students who have
- taken one of the prerequisite courses below or have practical hands-on experience.
Related training topics
Justify your training
Use this sample request letter — copy it into an email to your manager and personalize the bracketed details to make the case for the time and budget.
Sample training request letter
Subject: Request for Programming & Development training from Applied Technology Academy
[Decision Maker Name],
I'm writing to request time and budget approval to complete Applied Technology Academy's course, Next Level Python for Data Science Training. The information below outlines how this training benefits our organization, the tasks I'll be able to perform after completing it, and relevant cost and funding details.
Course Description
This course explores using Python for data scientists to perform exploratory data analysis, complex visualizations, and large-scale distributed processing using Big Data. In this course you’ll learn about essential mathematical and statistics libraries such as NumPy, Pandas, SciPy, SciKit-Learn, along with frameworks like TensorFlow and Spark. It also covers visualization tools like matplotlib, PIL, and Seaborn. Applied Technology Academy is an award-winning, SBA-certified woman-owned training provider (est. 2008) whose instructors are active practitioners; the course is hands-on with virtual labs and a learn-by-doing methodology.
Course Objectives
Once I've completed the course, I'll have hands-on, job-ready skills in programming & development that I can apply immediately to our work.
Expected Organizational Benefits
After completing this course, I will be better equipped to apply these skills directly to our projects, reduce our reliance on outside expertise, strengthen our team's capabilities, and share what I learn with colleagues.
Expected Cost & Funding
Course fee: [request an itemized quote at the link below]. Applied Technology Academy supports multiple funding paths that may reduce or cover this cost: GSA MAS purchasing and government purchase orders, military credentialing funding (Army CA, AF COOL, CG COOL), VA GI Bill and VR&E, ATA Flexible Spending, and student financing. Private team cohorts are available if colleagues should attend with me.
Conclusion
This training provides practical, hands-on experience I can apply immediately to strengthen our work in programming & development. Additional course information is available at https://appliedtechnologyacademy.com/python-training/next-level-python-for-data-science-training/.
Thank you for your consideration,
[Your Name]
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