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Next Level Python for Data Science | Working with Libraries, Frameworks, and Visualization Tools
Code:
TTPS4876
Duration:
5 Day
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$2695
USD
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This course explores using Python for data scientists to perform exploratory data analysis, complex visualizations, and large-scale distributed processing using Big Data. In this course youll learn about essential mathematical and statistics libraries such as NumPy, Pandas, SciPy, SciKit-Learn, along with frameworks like TensorFlow and Spark. It also covers visualization tools like matplotlib, PIL, and Seaborn.
This course is available in the following formats:
Duration: 5 Day
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Delivery Format: Virtual Classroom
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$ 2695 |
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Delivery Format: Virtual Classroom
|
$ 2695 |
|
|
Delivery Format: Virtual Classroom
|
$ 2695 |
|
|
Delivery Format: Virtual Classroom
|
$ 2695 |
|
|
Delivery Format: Virtual Classroom
|
$ 2695 |
Join an engaging hands-on learning environment, where youll 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.
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
Data Scientists, Data Engineers, and Software Engineers who are experienced with basic Python and data science.