NumPy is a Python library used for working with multi-dimensional arrays and matrices for scientific computing. It allows fast operations on large data sets and arrays. NumPy arrays can be created from lists or ranges of values and support element-wise operations via universal functions. NumPy is the foundation of the Python scientific computing stack and provides key features like broadcasting for efficient computations.
Abstract: This PDSG workshop introduces the basics of Python libraries used in machine learning. Libraries covered are Numpy, Pandas and MathlibPlot.
Level: Fundamental
Requirements: One should have some knowledge of programming and some statistics.
( Python Training: https://www.edureka.co/python )
This Edureka Python Numpy tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) explains what exactly is Numpy and how it is better than Lists. It also explains various Numpy operations with examples.
Check out our Python Training Playlist: https://goo.gl/Na1p9G
This tutorial helps you to learn the following topics:
1. What is Numpy?
2. Numpy v/s Lists
3. Numpy Operations
4. Numpy Special Functions
This document is useful when use with Video session I have recorded today with execution, This is document no. 2 of course "Introduction of Data Science using Python". Which is a prerequisite of Artificial Intelligence course at Ethans Tech.
Disclaimer: Some of the Images and content have been taken from Multiple online sources and this presentation is intended only for Knowledge Sharing
Introduction to Python Pandas for Data AnalyticsPhoenix
Pandas is an open-source, BSD-licensed Python library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Python with Pandas is used in a wide range of fields including academic and commercial domains including finance, economics, Statistics, analytics, medical...
Abstract: This PDSG workshop introduces the basics of Python libraries used in machine learning. Libraries covered are Numpy, Pandas and MathlibPlot.
Level: Fundamental
Requirements: One should have some knowledge of programming and some statistics.
( Python Training: https://www.edureka.co/python )
This Edureka Python Numpy tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) explains what exactly is Numpy and how it is better than Lists. It also explains various Numpy operations with examples.
Check out our Python Training Playlist: https://goo.gl/Na1p9G
This tutorial helps you to learn the following topics:
1. What is Numpy?
2. Numpy v/s Lists
3. Numpy Operations
4. Numpy Special Functions
This document is useful when use with Video session I have recorded today with execution, This is document no. 2 of course "Introduction of Data Science using Python". Which is a prerequisite of Artificial Intelligence course at Ethans Tech.
Disclaimer: Some of the Images and content have been taken from Multiple online sources and this presentation is intended only for Knowledge Sharing
Introduction to Python Pandas for Data AnalyticsPhoenix
Pandas is an open-source, BSD-licensed Python library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Python with Pandas is used in a wide range of fields including academic and commercial domains including finance, economics, Statistics, analytics, medical...
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
This is the basic introduction of the pandas library, you can use it for teaching this library for machine learning introduction. This slide will be able to help to understand the basics of pandas to the students with no coding background.
Introduction to Pandas and Time Series Analysis [PyCon DE]Alexander Hendorf
Most data is allocated to a period or to some point in time. We can gain a lot of insight by analyzing what happened when. The better the quality and accuracy of our data, the better our predictions can become.
Unfortunately the data we have to deal with is often aggregated for example on a monthly basis, but not all months are the same, they may have 28 days, 31 days, have four or five weekends,…. It’s made fit to our calendar that was made fit to deal with the earth surrounding the sun, not to please Data Scientists.
Dealing with periodical data can be a challenge. This talk will show to how you can deal with it with Pandas.
This Edureka Python Programming tutorial will help you learn python and understand the various basics of Python programming with examples in detail. Below are the topics covered in this tutorial:
1. Python Installation
2. Python Variables
3. Data types in Python
4. Operators in Python
5. Conditional Statements
6. Loops in Python
7. Functions in Python
8. Classes and Objects
Looking for a computer institute to learn Full Stack development and Digital Marketing? Our institute offers comprehensive courses in both areas, providing students with the skills and knowledge needed to succeed in today's digital landscape
This Edureka Python Matplotlib tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) explains what is data visualization and how to perform data visualization using Matplotlib. It also explains how to modify your plot and how to plot various types of graphs. Below are the topics covered in this tutorial:
1. Why Data Visualization?
2. What Is Data Visualization?
3. Various Types Of Plots
4. What Is Matplotlib?
6. How To Use Matplotlib?
The slides I was using when delivering a meetup about the matplotlib library. More info about that meetup can be found at https://www.meetup.com/life-michael/events/271738271/
What are Data structures in Python? | List, Dictionary, Tuple Explained | Edu...Edureka!
YouTube Link: https://youtu.be/m9n2f9lhtrw
** Python Certification Training: https://www.edureka.co/data-science-python-certification-course **
This Edureka video on 'Data Structures in Python' will help you understand the various data structures that Python has built into itself such as the list, dictionary, tuple and more. Further, we will also understand stacks, queues, trees and how they are implemented in Python using classes and functions. The video is divided into the following parts:
What are Data Structures?
Why are Data Structures needed?
Types of Data Structures in Python
Built-In Data Structures
Lists
Dictionary
Tuple
Sets
User-Defined Data Structure
Array
Stack
Queue
Linked List
Tree
Graph
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
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Castbox: https://castbox.fm/networks/505?country=in
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
This is the basic introduction of the pandas library, you can use it for teaching this library for machine learning introduction. This slide will be able to help to understand the basics of pandas to the students with no coding background.
Introduction to Pandas and Time Series Analysis [PyCon DE]Alexander Hendorf
Most data is allocated to a period or to some point in time. We can gain a lot of insight by analyzing what happened when. The better the quality and accuracy of our data, the better our predictions can become.
Unfortunately the data we have to deal with is often aggregated for example on a monthly basis, but not all months are the same, they may have 28 days, 31 days, have four or five weekends,…. It’s made fit to our calendar that was made fit to deal with the earth surrounding the sun, not to please Data Scientists.
Dealing with periodical data can be a challenge. This talk will show to how you can deal with it with Pandas.
This Edureka Python Programming tutorial will help you learn python and understand the various basics of Python programming with examples in detail. Below are the topics covered in this tutorial:
1. Python Installation
2. Python Variables
3. Data types in Python
4. Operators in Python
5. Conditional Statements
6. Loops in Python
7. Functions in Python
8. Classes and Objects
Looking for a computer institute to learn Full Stack development and Digital Marketing? Our institute offers comprehensive courses in both areas, providing students with the skills and knowledge needed to succeed in today's digital landscape
This Edureka Python Matplotlib tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) explains what is data visualization and how to perform data visualization using Matplotlib. It also explains how to modify your plot and how to plot various types of graphs. Below are the topics covered in this tutorial:
1. Why Data Visualization?
2. What Is Data Visualization?
3. Various Types Of Plots
4. What Is Matplotlib?
6. How To Use Matplotlib?
The slides I was using when delivering a meetup about the matplotlib library. More info about that meetup can be found at https://www.meetup.com/life-michael/events/271738271/
What are Data structures in Python? | List, Dictionary, Tuple Explained | Edu...Edureka!
YouTube Link: https://youtu.be/m9n2f9lhtrw
** Python Certification Training: https://www.edureka.co/data-science-python-certification-course **
This Edureka video on 'Data Structures in Python' will help you understand the various data structures that Python has built into itself such as the list, dictionary, tuple and more. Further, we will also understand stacks, queues, trees and how they are implemented in Python using classes and functions. The video is divided into the following parts:
What are Data Structures?
Why are Data Structures needed?
Types of Data Structures in Python
Built-In Data Structures
Lists
Dictionary
Tuple
Sets
User-Defined Data Structure
Array
Stack
Queue
Linked List
Tree
Graph
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Effective Numerical Computation in NumPy and SciPyKimikazu Kato
Presented at PyCon JP 2014.
Video is available at
http://bit.ly/1tXYhw6
This talk explores case studies of effective usage of Numpy/Scipy and shows that the computational speed sometimes improves drastically with the appropriate derivation of formulas and performance-conscious implementation. I especially focus on scipy.sparse, the module for sparse matrices, which is often useful in the areas of machine learning and natural language processing.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
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This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
3. NumPy is a Python C extension library for array-oriented computing
Efficient
In-memory
Contiguous (or Strided)
Homogeneous (but types can be algebraic)
NumPy is suited to many applications
Image processing
Signal processing
Linear algebra
A plethora of others
4. NumPy is the foundation of theNumPy is the foundation of the
python scientific stackpython scientific stack
10. Array Element Type (dtype)Array Element Type (dtype)
NumPy arrays comprise elements of a single data type
The type object is accessible through the .dtype attribute
Here are a few of the most important attributes of dtype objects
dtype.byteorder — big or little endian
dtype.itemsize — element size of this dtype
dtype.name — a name for this dtype object
dtype.type — type object used to create scalars
There are many others...
11. Array dtypes are usually inferred automatically
But can also be specified explicitly
In [16]: a = np.array([1,2,3])
In [17]: a.dtype
Out[17]: dtype('int64')
In [18]: b = np.array([1,2,3,4.567])
In [19]: b.dtype
Out[19]: dtype('float64')
In [20]: a = np.array([1,2,3], dtype=np.float32)
In [21]: a.dtype
Out[21]: dtype('int64')
In [22]: a
Out[22]: array([ 1., 2., 3.], dtype=float32)
12. NumPy Builtin dtype HierarchyNumPy Builtin dtype Hierarchy
np.datetime64 is a new addition in NumPy 1.7
13. Array CreationArray Creation
Explicitly from a list of values
As a range of values
By specifying the number of elements
In [2]: np.array([1,2,3,4])
Out[2]: array([1, 2, 3, 4])
In [3]: np.arange(10)
Out[3]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
In [4]: np.linspace(0, 1, 5)
Out[4]: array([ 0. , 0.25, 0.5 , 0.75, 1. ])
20. Array ViewsArray Views
Simple assigments do not make copies of arrays (same semantics as
Python). Slicing operations do not make copies either; they return views
on the original array.
Array views contain a pointer to the original data, but may have different
shape or stride values. Views always have flags.owndata equal to
False.
In [2]: a = np.arange(10)
In [3]: b = a[3:7]
In [4]: b
Out[4]: array([3, 4, 5, 6])
In [5]: b[:] = 0
In [6]: a
Out[6]: array([0, 1, 3, 0, 0, 0, 0, 7, 8, 9])
In [7]: b.flags.owndata
Out[7]: False
21. Universal Functions (ufuncs)Universal Functions (ufuncs)
NumPy ufuncs are functions that operate element-wise on one or more
arrays
ufuncs dispatch to optimized C inner-loops based on array dtype
23. AxisAxis
Array method reductions take an optional axis parameter that specifies
over which axes to reduce
axis=None reduces into a single scalar
In [7]: a.sum
()
Out[7]: 105
25. axis=0 reduces into the zeroth dimension
axis=0 reduces into the first dimension
In [8]: a.sum(axis=0)
Out[8]: array([15, 18, 21, 24,
27])
In [9]: a.sum(axis=1)
Out[9]: array([10, 35, 60])
26. BroadcastingBroadcasting
A key feature of NumPy is broadcasting, where arrays with different, but
compatible shapes can be used as arguments to ufuncs
In this case an array scalar is broadcast to an array with shape (5, )
27. A slightly more involved broadcasting example in two dimensions
Here an array of shape (3, 1) is broadcast to an array with shape
(3, 2)
28. Broadcasting RulesBroadcasting Rules
In order for an operation to broadcast, the size of all the trailing
dimensions for both arrays must either:
be equal OR be one
A (1d array): 3
B (2d array): 2 x 3
Result (2d array): 2 x 3
A (2d array): 6 x 1
B (3d array): 1 x 6 x 4
Result (3d array): 1 x 6 x 4
A (4d array): 3 x 1 x 6 x 1
B (3d array): 2 x 1 x 4
Result (4d array): 3 x 2 x 6 x 4
29. Square Peg in a Round HoleSquare Peg in a Round Hole
If the dimensions do not match up, np.newaxis may be useful
In [16]: a = np.arange(6).reshape((2, 3))
In [17]: b = np.array([10, 100])
In [18]: a * b
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
in ()
----> 1 a * b
ValueError: operands could not be broadcast together with shapes (2,3) (2)
In [19]: b[:,np.newaxis].shape
Out[19]: (2, 1)
In [20]: a *b[:,np.newaxis]
Out[20]:
array([[ 0, 10, 20],
[300, 400, 500]])
31. Fancy IndexingFancy Indexing
NumPy arrays may be used to index into other arrays
In [2]: a = np.arange(15).reshape((3,5))
In [3]: a
Out[3]:
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]])
In [4]: i = np.array([[0,1], [1, 2]])
In [5]: j = np.array([[2, 1], [4, 4]])
In [6]: a[i,j]
Out[6]:
array([[ 2, 6],
[ 9, 14]])
32. Boolean arrays can also be used as indices into other arrays
In [2]: a = np.arange(15).reshape((3,5))
In [3]: a
Out[3]:
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]])
In [4]: b = (a % 3 == 0)
In [5]: b
Out[5]:
array([[ True, False, False, True, False],
[False, True, False, False, True],
[False, False, True, False, False]], dtype=bool)
In [6]: a[b]
Out[6]: array([ 0, 3, 6, 9, 12])
40. ResourcesResources
These slides are currently available at
http://docs.scipy.org/doc/numpy/reference/
http://docs.scipy.org/doc/numpy/user/index.html
http://www.scipy.org/Tentative_NumPy_Tutorial
http://www.scipy.org/Numpy_Example_List
https://github.com/ContinuumIO/tutorials/blob/master/IntrotoNumPy.pdf
41. The EndThe End
Many thanks toMany thanks to
Ben Zaitlin
Stéfan van der Walt
Amy Troschinetz
Maggie Mari
Travis Oliphant
Questions?Questions?