The amount of data available to us is growing rapidly, but what is required to make useful conclusions out of it?
Outline
1. Different tactics to gather your data
2. Cleansing, scrubbing, correcting your data
3. Running analysis for your data
4. Bring your data to live with visualizations
5. Publishing your data for rest of us as linked open data
Python for Data Science | Python Data Science Tutorial | Data Science Certifi...Edureka!
( Python Data Science Training : https://www.edureka.co/python )
This Edureka video on "Python For Data Science" explains the fundamental concepts of data science using python. It will also help you to analyze, manipulate and implement machine learning using various python libraries such as NumPy, Pandas and Scikit-learn.
This video helps you to learn the below topics:
1. Need of Data Science
2. What is Data Science?
3. How Python is used for Data Science?
4. Data Manipulation in Python
5. Implement Machine Learning using Python
6. Demo
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check out our Python Training Playlist: https://goo.gl/Na1p9G
Data Wrangling and Visualization Using PythonMOHITKUMAR1379
Python is open source and has so many libraries for data wrangling and visualization that makes life of data scientists easier. For data wrangling pandas is used as it represent tabular data and it has other function to parse data from different sources, data cleaning, handling missing values, merging data sets etc. To visualize data, low level matplotlib can be used. But it is a base package for other high level packages such as seaborn, that draw well customized plot in just one line of code. Python has dash framework that is used to make interactive web application using python code without javascript and html. These dash application can be published on any server as well as on clouds like google cloud but freely on heroku cloud.
The amount of data available to us is growing rapidly, but what is required to make useful conclusions out of it?
Outline
1. Different tactics to gather your data
2. Cleansing, scrubbing, correcting your data
3. Running analysis for your data
4. Bring your data to live with visualizations
5. Publishing your data for rest of us as linked open data
Python for Data Science | Python Data Science Tutorial | Data Science Certifi...Edureka!
( Python Data Science Training : https://www.edureka.co/python )
This Edureka video on "Python For Data Science" explains the fundamental concepts of data science using python. It will also help you to analyze, manipulate and implement machine learning using various python libraries such as NumPy, Pandas and Scikit-learn.
This video helps you to learn the below topics:
1. Need of Data Science
2. What is Data Science?
3. How Python is used for Data Science?
4. Data Manipulation in Python
5. Implement Machine Learning using Python
6. Demo
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check out our Python Training Playlist: https://goo.gl/Na1p9G
Data Wrangling and Visualization Using PythonMOHITKUMAR1379
Python is open source and has so many libraries for data wrangling and visualization that makes life of data scientists easier. For data wrangling pandas is used as it represent tabular data and it has other function to parse data from different sources, data cleaning, handling missing values, merging data sets etc. To visualize data, low level matplotlib can be used. But it is a base package for other high level packages such as seaborn, that draw well customized plot in just one line of code. Python has dash framework that is used to make interactive web application using python code without javascript and html. These dash application can be published on any server as well as on clouds like google cloud but freely on heroku cloud.
Data Science Training | Data Science Tutorial for Beginners | Data Science wi...Edureka!
***** Data Science Training - https://www.edureka.co/data-science *****
This Edureka tutorial on "Data Science Training" will provide you with a detailed and comprehensive training on Data Science, the real-life use cases and the various paths one can take to become a data scientist. It will also help you understand the various phases of Data Science.
Data Science Blog Series: https://goo.gl/1CKTyN
http://www.edureka.co/data-science
In this presentation, I have talked about Big Data and its importance in brief. I have included the very basics of Data Science and its importance in the present day, through a case study. You can also get an idea about who a data scientist is and what all tasks he performs. A few applications of data science have been illustrated in the end.
Data Science With Python | Python For Data Science | Python Data Science Cour...Simplilearn
This Data Science with Python presentation will help you understand what is Data Science, basics of Python for data analysis, why learn Python, how to install Python, Python libraries for data analysis, exploratory analysis using Pandas, introduction to series and dataframe, loan prediction problem, data wrangling using Pandas, building a predictive model using Scikit-Learn and implementing logistic regression model using Python. The aim of this video is to provide a comprehensive knowledge to beginners who are new to Python for data analysis. This video provides a comprehensive overview of basic concepts that you need to learn to use Python for data analysis. Now, let us understand how Python is used in Data Science for data analysis.
This Data Science with Python presentation will cover the following topics:
1. What is Data Science?
2. Basics of Python for data analysis
- Why learn Python?
- How to install Python?
3. Python libraries for data analysis
4. Exploratory analysis using Pandas
- Introduction to series and dataframe
- Loan prediction problem
5. Data wrangling using Pandas
6. Building a predictive model using Scikit-learn
- Logistic regression
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you'll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques.
Learn more at: https://www.simplilearn.com
Best Data Science Ppt using Python
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
The Agenda for the Webinar:
1. Introduction to Python.
2. Python and Big Data.
3. Python and Data Science.
4. Key features of Python and their usage in Business Analytics.
5. Business Analytics with Python – Real world Use Cases.
Top 5 Python Libraries For Data Science | Python Libraries Explained | Python...Simplilearn
Python is the most widely used programming language today. When it comes to solving Data Science tasks and challenges, Python never ceases to surprise its audience. Most data scientists are already leveraging the power of Python programming every day. Python is easy to learn, easier to debug, widely used, object-oriented, open source, high-performance language and there are many more benefits of using Python programming. Python has been built with extraordinary libraries which are used by programmers everyday in solving the problems. So, now let us talk about the Top 5 Python libraries for Data Science.
Below are the Top 5 Python libraries for Data science:
1. Tensorflow ( 00:29 )
2. Numpy ( 03:01 )
3. Scipy ( 06:38 )
4. Pandas ( 08:20 )
5. Matplotlib ( 11:41 )
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you'll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jump start your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. A Data Scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a Data Scientist, you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.
Install the required Python environment and other auxiliary tools and libraries
2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
3. Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions
Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave
4. Perform data analysis and manipulation using data structures and tools provided in the Pandas package
5. Gain expertise in machine learning using the Scikit-Learn package
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
Learn more at: https://www.simplilearn.com/
Python has been one of the premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. It’s easy to learn simple syntax is very accessible to new programmers and is similar to Matlab, C/C++, Java, or Visual Basic. Python is general purpose and comparatively easy to learn with an increased adoption for analytical and quantitative computing. For over a decade, Python has been used in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing.
How to transform and select variables/features when creating a predictive model using machine learning. To see the source code visit https://github.com/Davisy/Feature-Engineering-and-Feature-Selection
This presentation briefly explains the following topics:
Why is Data Analytics important?
What is Data Analytics?
Top Data Analytics Tools
How to Become a Data Analyst?
Analytics, Business Intelligence, and Data Science - What's the Progression?DATAVERSITY
Data analysis can include looking back at historical data, understanding what an organization currently has, and even looking forward to predictions of the future. This presentation will talk about the differences between analytics, business intelligence, and data science, as well as the differences in architecture — and possibly even organization maturity — that make each successful.
Learn more about these topics we will explore including:
Defining analytics, business intelligence, and data science
Differences in architecture
When to use analytics, business intelligence, or data science
Whether there has been an evolution between analytics, business intelligence, and data science
3 pillars of big data : structured data, semi structured data and unstructure...PROWEBSCRAPER
There are 3 pillars of Big Data
1.Structured data
2.Unstructured data
3.Semi structured data
Businesses worldwide construct their empire on these three pillars and capitalize on their limitless potential.
Big Data Tutorial | What Is Big Data | Big Data Hadoop Tutorial For Beginners...Simplilearn
This presentation about Big Data will help you understand how Big Data evolved over the years, what is Big Data, applications of Big Data, a case study on Big Data, 3 important challenges of Big Data and how Hadoop solved those challenges. The case study talks about Google File System (GFS), where you’ll learn how Google solved its problem of storing increasing user data in early 2000. We’ll also look at the history of Hadoop, its ecosystem and a brief introduction to HDFS which is a distributed file system designed to store large volumes of data and MapReduce which allows parallel processing of data. In the end, we’ll run through some basic HDFS commands and see how to perform wordcount using MapReduce. Now, let us get started and understand Big Data in detail.
Below topics are explained in this Big Data presentation for beginners:
1. Evolution of Big Data
2. Why Big Data?
3. What is Big Data?
4. Challenges of Big Data
5. Hadoop as a solution
6. MapReduce algorithm
7. Demo on HDFS and MapReduce
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Learn the basics of data visualization in R. In this module, we explore the Graphics package and learn to build basic plots in R. In addition, learn to add title, axis labels and range. Modify the color, font and font size. Add text annotations and combine multiple plots. Finally, learn how to save the plots in different formats.
Python standard library & list of important librariesgrinu
We know that a module is a file with some Python code, and a package is a directory for sub packages and modules. But the line between a package and a Python library is quite blurred.
A Python library is a reusable chunk of code that you may want to include in your programs/ projects. Compared to languages like C++ or C, a Python libraries do not pertain to any specific context in Python. Here, a ‘library’ loosely describes a collection of core modules. Essentially, then, a library is a collection of modules. A package is a library that can be installed using a package manager like rubygems or npm.
Data Science Training | Data Science Tutorial for Beginners | Data Science wi...Edureka!
***** Data Science Training - https://www.edureka.co/data-science *****
This Edureka tutorial on "Data Science Training" will provide you with a detailed and comprehensive training on Data Science, the real-life use cases and the various paths one can take to become a data scientist. It will also help you understand the various phases of Data Science.
Data Science Blog Series: https://goo.gl/1CKTyN
http://www.edureka.co/data-science
In this presentation, I have talked about Big Data and its importance in brief. I have included the very basics of Data Science and its importance in the present day, through a case study. You can also get an idea about who a data scientist is and what all tasks he performs. A few applications of data science have been illustrated in the end.
Data Science With Python | Python For Data Science | Python Data Science Cour...Simplilearn
This Data Science with Python presentation will help you understand what is Data Science, basics of Python for data analysis, why learn Python, how to install Python, Python libraries for data analysis, exploratory analysis using Pandas, introduction to series and dataframe, loan prediction problem, data wrangling using Pandas, building a predictive model using Scikit-Learn and implementing logistic regression model using Python. The aim of this video is to provide a comprehensive knowledge to beginners who are new to Python for data analysis. This video provides a comprehensive overview of basic concepts that you need to learn to use Python for data analysis. Now, let us understand how Python is used in Data Science for data analysis.
This Data Science with Python presentation will cover the following topics:
1. What is Data Science?
2. Basics of Python for data analysis
- Why learn Python?
- How to install Python?
3. Python libraries for data analysis
4. Exploratory analysis using Pandas
- Introduction to series and dataframe
- Loan prediction problem
5. Data wrangling using Pandas
6. Building a predictive model using Scikit-learn
- Logistic regression
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you'll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques.
Learn more at: https://www.simplilearn.com
Best Data Science Ppt using Python
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
The Agenda for the Webinar:
1. Introduction to Python.
2. Python and Big Data.
3. Python and Data Science.
4. Key features of Python and their usage in Business Analytics.
5. Business Analytics with Python – Real world Use Cases.
Top 5 Python Libraries For Data Science | Python Libraries Explained | Python...Simplilearn
Python is the most widely used programming language today. When it comes to solving Data Science tasks and challenges, Python never ceases to surprise its audience. Most data scientists are already leveraging the power of Python programming every day. Python is easy to learn, easier to debug, widely used, object-oriented, open source, high-performance language and there are many more benefits of using Python programming. Python has been built with extraordinary libraries which are used by programmers everyday in solving the problems. So, now let us talk about the Top 5 Python libraries for Data Science.
Below are the Top 5 Python libraries for Data science:
1. Tensorflow ( 00:29 )
2. Numpy ( 03:01 )
3. Scipy ( 06:38 )
4. Pandas ( 08:20 )
5. Matplotlib ( 11:41 )
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you'll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jump start your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. A Data Scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a Data Scientist, you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.
Install the required Python environment and other auxiliary tools and libraries
2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
3. Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions
Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave
4. Perform data analysis and manipulation using data structures and tools provided in the Pandas package
5. Gain expertise in machine learning using the Scikit-Learn package
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
Learn more at: https://www.simplilearn.com/
Python has been one of the premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. It’s easy to learn simple syntax is very accessible to new programmers and is similar to Matlab, C/C++, Java, or Visual Basic. Python is general purpose and comparatively easy to learn with an increased adoption for analytical and quantitative computing. For over a decade, Python has been used in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing.
How to transform and select variables/features when creating a predictive model using machine learning. To see the source code visit https://github.com/Davisy/Feature-Engineering-and-Feature-Selection
This presentation briefly explains the following topics:
Why is Data Analytics important?
What is Data Analytics?
Top Data Analytics Tools
How to Become a Data Analyst?
Analytics, Business Intelligence, and Data Science - What's the Progression?DATAVERSITY
Data analysis can include looking back at historical data, understanding what an organization currently has, and even looking forward to predictions of the future. This presentation will talk about the differences between analytics, business intelligence, and data science, as well as the differences in architecture — and possibly even organization maturity — that make each successful.
Learn more about these topics we will explore including:
Defining analytics, business intelligence, and data science
Differences in architecture
When to use analytics, business intelligence, or data science
Whether there has been an evolution between analytics, business intelligence, and data science
3 pillars of big data : structured data, semi structured data and unstructure...PROWEBSCRAPER
There are 3 pillars of Big Data
1.Structured data
2.Unstructured data
3.Semi structured data
Businesses worldwide construct their empire on these three pillars and capitalize on their limitless potential.
Big Data Tutorial | What Is Big Data | Big Data Hadoop Tutorial For Beginners...Simplilearn
This presentation about Big Data will help you understand how Big Data evolved over the years, what is Big Data, applications of Big Data, a case study on Big Data, 3 important challenges of Big Data and how Hadoop solved those challenges. The case study talks about Google File System (GFS), where you’ll learn how Google solved its problem of storing increasing user data in early 2000. We’ll also look at the history of Hadoop, its ecosystem and a brief introduction to HDFS which is a distributed file system designed to store large volumes of data and MapReduce which allows parallel processing of data. In the end, we’ll run through some basic HDFS commands and see how to perform wordcount using MapReduce. Now, let us get started and understand Big Data in detail.
Below topics are explained in this Big Data presentation for beginners:
1. Evolution of Big Data
2. Why Big Data?
3. What is Big Data?
4. Challenges of Big Data
5. Hadoop as a solution
6. MapReduce algorithm
7. Demo on HDFS and MapReduce
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Learn the basics of data visualization in R. In this module, we explore the Graphics package and learn to build basic plots in R. In addition, learn to add title, axis labels and range. Modify the color, font and font size. Add text annotations and combine multiple plots. Finally, learn how to save the plots in different formats.
Python standard library & list of important librariesgrinu
We know that a module is a file with some Python code, and a package is a directory for sub packages and modules. But the line between a package and a Python library is quite blurred.
A Python library is a reusable chunk of code that you may want to include in your programs/ projects. Compared to languages like C++ or C, a Python libraries do not pertain to any specific context in Python. Here, a ‘library’ loosely describes a collection of core modules. Essentially, then, a library is a collection of modules. A package is a library that can be installed using a package manager like rubygems or npm.
Python is often a choice for development that needs to be applied for census and data analysis to work, or data scientists whose work should be integrated into web applications or the production environment. In particular, python actually looks at the learning point of the machine. The combination of python's teaching and library libraries makes it particularly suited to develop modern lenses and predecessors forecasts directly connected to the production process.
Data science training in Chennai.
Are you interested
Call now:+91 996 252 8294
Top 10 Python Libraries for Machine Learning.pptxAdam John
ML libraries are available in many programming languages, but python being the most user-friendly and easy to manage language, and having a large developer community, is best suited for machine learning purposes and that's why many ML libraries are being written in Python.
Python's dominance in scientific computing stems from its remarkable simplicity and versatility. Its clear syntax appeals to scientists and researchers, enabling rapid development of complex algorithms. With libraries like NumPy, SciPy, and Matplotlib, Python effortlessly handles mathematical and data-driven tasks. Its open-source nature fosters collaboration, driving innovation in the scientific realm. Python's power, accessibility, and extensive toolset make it an indispensable asset for scientific computing.
In this presentation, we describe that how can we implemented in IoT or Internet of things using Python language there is plenty of library and module for IoT in python which we discussed in this presentation Thank You.
5 Best Libraries for Data Analysis:
In the dynamic world of data analysis, Python has emerged as a powerhouse for professionals seeking robust solutions to their data-related challenges. The secret to its versatility lies in its libraries. In this SlideShare presentation, we'll delve into the top 5 Python libraries that can transform your data analysis endeavors. Whether you're a data scientist, analyst, or enthusiast, these libraries are essential tools in your arsenal.
1: Introduction
Let's kick off by introducing the topic and the importance of Python in data analysis.
2: NumPy - The Foundation of Data Analysis
In this slide, we discuss NumPy, the fundamental library for scientific computing with Python. Learn how it supports multi-dimensional arrays and mathematical functions, laying the groundwork for various data operations.
3: Pandas - Your Data Manipulation Ally
Moving on, we explore Pandas, a powerful library for data manipulation and analysis. We'll discuss how DataFrames and Series help in data cleaning, transformation, and tabular data handling.
4: Matplotlib - Creating Stunning Visualizations
Visualizing data is vital, and Matplotlib is the go-to library for this purpose. We'll explain its vast array of plotting options and how it can be used for static, animated, or interactive visualizations.
5: Seaborn - Simplifying Data Visualization
Seaborn, a library built on Matplotlib, makes data visualization even more accessible. We'll explore its high-level interface for creating stylish and informative statistical graphics.
6: Scikit-Learn - Your Machine Learning Companion
Machine learning is integral to data analysis, and Scikit-Learn is your go-to library for it. Learn how to build, evaluate, and deploy machine learning models for classification, regression, clustering, and more.
7: Conclusion
In this slide, we recap the significance of these five Python libraries in the world of data analysis. These libraries are the keys to unlocking the full potential of your data.
The future scope of Python is exceptionally promising, making it a crucial skill for individuals seeking a competitive edge in the tech industry. Python's versatility, simplicity, and extensive library ecosystem continue to drive its adoption in fields such as web development, data science, artificial intelligence, and more. In Delhi, the demand for Python professionals is particularly high, with numerous job opportunities and career growth prospects. Enrolling in a Python course in Delhi can provide you with the knowledge and skills needed to capitalize on these opportunities and excel in this dynamic and evolving field.
Python for Data Engineering: Why Do Data Engineers Use Python?hemayadav41
Discover why data engineers prefer using Python for their data engineering tasks. Explore the versatility, ease of use, extensive libraries, integration with big data technologies, and data visualisation capabilities that make Python an invaluable tool in the field. Institutes like Uncodemy, Udemy, Simplilearn, Ducat, and 4achivers, provide the best Python Course with Job Placement in Jaipur, Kanpur, Gorakhpur, Mumbai, Pune, Delhi, Noida, and all over India."
Python is a widely-used, high-level programming language known for its simplicity, readability, and extensive library support. It is favored by developers for its ease of use and ability to handle diverse tasks, making it suitable for various applications ranging from web development to data analysis and artificial intelligence.
The best way to learn Python depends on your learning style, goals, and preferences. However, a structured approach often involves:
1. **Start with Basics**: Begin with beginner-friendly tutorials or courses that cover Python syntax, data types, control structures, and functions.
2. **Practice Regularly**: Reinforce your learning by practicing coding exercises, solving problems on coding platforms, and working on small projects.
3. **Build Projects**: Apply your knowledge to real-world projects that interest you, such as web development, data analysis, automation scripts, or games.
4. **Explore Resources**: Utilize a variety of resources such as online courses, textbooks, documentation, and community forums to deepen your understanding and explore advanced topics.
5. **Collaborate and Seek Feedback**: Join Python communities, participate in coding forums, and collaborate with others to share knowledge, get feedback, and learn from different perspectives.
6. **Stay Updated**: Python is constantly evolving, so stay updated with the latest features, libraries, and best practices by following blogs, attending conferences, and exploring new learning materials.
overview of python programming language.pptxdmsidharth
Python, born out of Guido van Rossum's vision in the late 1980s and formally introduced in 1991, stands tall as one of the foremost programming languages in today's digital landscape. Its journey from inception to dominance reflects a narrative of simplicity, versatility, and unwavering community support. At its core, Python embodies a design philosophy that prioritizes readability, fostering an environment where developers can express their ideas with clarity and conciseness. This philosophy, encapsulated in the famous maxim "Readability counts," has been instrumental in attracting a diverse array of practitioners, ranging from seasoned professionals to eager novices.
Similar to Data Science with Python Libraries (20)
An electronic health record (EHR) is a collection of patient’s electronically-stored health information in a digital and systematic format. EHR system can store data accurately.
Disease prediction and doctor recommendation systemsabafarheen
This paper will tell you how the system will work in terms of disease prediction also will suggest you nearest hospital with experienced doctors, cheap fees
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
2. OBJECTIVES
Data Science as it enables computers
to get into a mode of self-learning?
01 Python
Libraries
Concludes with the list of
areas where AI can assist
human beings.
02
CONCLUSIONS
03
4. Python libraries are specific
files containing pre-written code
that can be imported into your
code base by using Python’s
import feature. This increases
your code reusability
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8. NUMPY
one of the core libraries, provides
support for arrays containing pixels
of image data points.
PYTH
ON
LIBRA
RIES
MATPLOTLIB
visualization library in
Python for 2D plots of
arrays.
PANDAS
provides high-performance,
easy to use structures and
data analysis tools.