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Real time Python Data Analyst course with R. - $ 4 Assessment test - 3 projects - All session recorded and shared
All python data_analyst_r_course
All python data_analyst_r_course
Kamal A
The Department of Internal Medicine at the University of Michigan needed a data warehousing solution to store biological experiments and gene expressions. I researched the requirements for the lab, architected the database and scraped the data from various data sources, built a web application, REST APIs and libraries to support the API interface for the database. As of today, the database stores over 3.5 million data points. The stack is Django, PostgreSQL & Nginx.
Data warehousing solution for Department of Internal Medicine, University of ...
Data warehousing solution for Department of Internal Medicine, University of ...
Manu Gupta
Scouting howto collecting and analyzing sensor data with hadoop or other no sql databases
Collecting and analyzing sensor data with hadoop or other no sql databases
Collecting and analyzing sensor data with hadoop or other no sql databases
Matteo Redaelli
Big Data Analytics involves examining or processing large amounts of data (unstructured and structured) to create useful information which can help organizations to critically fine tune their business plans and increase profitability. Apache HadoopTM is the most efficient data platform that simplifies and allows for the distributed processing of large data sets. The latest revolution in big data technology, Hadoop forms the core of an open source software framework, supporting the processing of large data across clustered systems. Using Hadoop, deep analytics that cannot be handled by a database engine can be run effectively.
Big data analytics_using_hadoop
Big data analytics_using_hadoop
Knowledgehut
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
Data Science With Python | Python For Data Science | Python Data Science Cour...
Data Science With Python | Python For Data Science | Python Data Science Cour...
Simplilearn
python
Abhishek Training PPT.pptx
Abhishek Training PPT.pptx
KashishKashish22
Introduction After statistics essentials, this course acquaints students to data analysis with the Python (most popular data science language today). You learn to work with different data structures in Python using the most popular data analytics and visualization packages such as numpy, pandas, matplotlib, and seaborn. Ultimately, students will use Python code and packages to solve problems; extract, transform, load, and analyze data to gain insights; and communicate the analyses, aided by appropriate visualizations. The course targets students mainly beginners who want to learn the basics of data analysis with python, or intermediate learners who want to improve their skills and apply them to real-world problems. Objectives and Outcome The objectives of this course are to help you learn how to collect, clean, manipulate, analyze, and visualize data using python, and to use various python libraries and tools for data science, such as pandas, numpy, matplotlib, scikit-learn, and more. The Prerequisites To be successful in this course, basic programming knowledge is necessary. However, this course do not assume any previous knowledge in python programming, such as data types, variables, operators, functions, loops, and conditions, and to have a basic understanding of data analysis concepts, such as statistics, probability, and machine learning. Detailed Course Outline Introduction to data analysis with python • What is data analysis and why use python? • Setting up the python environment and tools • Importing and exporting data with python Data manipulation with pandas • What is pandas and how to use it? • Creating and exploring data frames • Filtering, sorting, and grouping data • Merging, joining, and concatenating data • Putting it all together with real world data/Portfolio Data visualization with matplotlib • What is matplotlib and how to use it? • Creating and customizing plots • Choosing the right plot for your data • Adding labels, legends, and annotations Putting it all together with real world data/Portfolio Data analysis with numpy • What is numpy and how to use it? • Creating and manipulating arrays • Performing arithmetic and logical operations • Applying statistical and mathematical functions Machine Learning • Concepts in Machine learning • Data analysis with scikit-learn • What is scikit-learn and how to use it? • Preprocessing and transforming data • Splitting and cross-validating data • Evaluating and comparing models
Data Science with Python course Outline.pptx
Data Science with Python course Outline.pptx
Ferdsilinks
Slides used by David Opoku, 2015 School of Data fellow, for his skillshare about Scraping.
Skillshare - Introduction to Data Scraping
Skillshare - Introduction to Data Scraping
School of Data
Recommandé
Real time Python Data Analyst course with R. - $ 4 Assessment test - 3 projects - All session recorded and shared
All python data_analyst_r_course
All python data_analyst_r_course
Kamal A
The Department of Internal Medicine at the University of Michigan needed a data warehousing solution to store biological experiments and gene expressions. I researched the requirements for the lab, architected the database and scraped the data from various data sources, built a web application, REST APIs and libraries to support the API interface for the database. As of today, the database stores over 3.5 million data points. The stack is Django, PostgreSQL & Nginx.
Data warehousing solution for Department of Internal Medicine, University of ...
Data warehousing solution for Department of Internal Medicine, University of ...
Manu Gupta
Scouting howto collecting and analyzing sensor data with hadoop or other no sql databases
Collecting and analyzing sensor data with hadoop or other no sql databases
Collecting and analyzing sensor data with hadoop or other no sql databases
Matteo Redaelli
Big Data Analytics involves examining or processing large amounts of data (unstructured and structured) to create useful information which can help organizations to critically fine tune their business plans and increase profitability. Apache HadoopTM is the most efficient data platform that simplifies and allows for the distributed processing of large data sets. The latest revolution in big data technology, Hadoop forms the core of an open source software framework, supporting the processing of large data across clustered systems. Using Hadoop, deep analytics that cannot be handled by a database engine can be run effectively.
Big data analytics_using_hadoop
Big data analytics_using_hadoop
Knowledgehut
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
Data Science With Python | Python For Data Science | Python Data Science Cour...
Data Science With Python | Python For Data Science | Python Data Science Cour...
Simplilearn
python
Abhishek Training PPT.pptx
Abhishek Training PPT.pptx
KashishKashish22
Introduction After statistics essentials, this course acquaints students to data analysis with the Python (most popular data science language today). You learn to work with different data structures in Python using the most popular data analytics and visualization packages such as numpy, pandas, matplotlib, and seaborn. Ultimately, students will use Python code and packages to solve problems; extract, transform, load, and analyze data to gain insights; and communicate the analyses, aided by appropriate visualizations. The course targets students mainly beginners who want to learn the basics of data analysis with python, or intermediate learners who want to improve their skills and apply them to real-world problems. Objectives and Outcome The objectives of this course are to help you learn how to collect, clean, manipulate, analyze, and visualize data using python, and to use various python libraries and tools for data science, such as pandas, numpy, matplotlib, scikit-learn, and more. The Prerequisites To be successful in this course, basic programming knowledge is necessary. However, this course do not assume any previous knowledge in python programming, such as data types, variables, operators, functions, loops, and conditions, and to have a basic understanding of data analysis concepts, such as statistics, probability, and machine learning. Detailed Course Outline Introduction to data analysis with python • What is data analysis and why use python? • Setting up the python environment and tools • Importing and exporting data with python Data manipulation with pandas • What is pandas and how to use it? • Creating and exploring data frames • Filtering, sorting, and grouping data • Merging, joining, and concatenating data • Putting it all together with real world data/Portfolio Data visualization with matplotlib • What is matplotlib and how to use it? • Creating and customizing plots • Choosing the right plot for your data • Adding labels, legends, and annotations Putting it all together with real world data/Portfolio Data analysis with numpy • What is numpy and how to use it? • Creating and manipulating arrays • Performing arithmetic and logical operations • Applying statistical and mathematical functions Machine Learning • Concepts in Machine learning • Data analysis with scikit-learn • What is scikit-learn and how to use it? • Preprocessing and transforming data • Splitting and cross-validating data • Evaluating and comparing models
Data Science with Python course Outline.pptx
Data Science with Python course Outline.pptx
Ferdsilinks
Slides used by David Opoku, 2015 School of Data fellow, for his skillshare about Scraping.
Skillshare - Introduction to Data Scraping
Skillshare - Introduction to Data Scraping
School of Data
Intro to machine learning with Scikit-learn
Intro to machine learning with scikit learn
Intro to machine learning with scikit learn
Yoss Cohen
Python
Python
Python
BALUJAINSTITUTE
Tableau Customer Presentation
Tableau Customer Presentation
Tableau Customer Presentation
Splunk
Exploratory Data Analytics (EDA) is a data Pre-Processing, manual data summarization and visualization related discipline which is an earlier phase of data processing. This was one of the lectures of a full course I taught in University of Moratuwa, Sri Lanka on 2023 second half of the year.
Lecture 3 - Exploratory Data Analytics (EDA), a lecture in subject module Sta...
Lecture 3 - Exploratory Data Analytics (EDA), a lecture in subject module Sta...
Maninda Edirisooriya
The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Virtual Analytics Platform, will present some best practices for building deep learning pipelines with Spark. Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including: * optimizing cluster setup; * configuring the cluster; * ingesting data; and * monitoring long-running jobs. We will demonstrate the techniques we cover using Google’s popular TensorFlow library. More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters. Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput, and monitoring facilitates both the work of configuration and the stability of deep learning jobs.
Deep Learning on Apache® Spark™ : Workflows and Best Practices
Deep Learning on Apache® Spark™ : Workflows and Best Practices
Jen Aman
The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Virtual Analytics Platform, will present some best practices for building deep learning pipelines with Spark. Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including: * optimizing cluster setup; * configuring the cluster; * ingesting data; and * monitoring long-running jobs. We will demonstrate the techniques we cover using Google’s popular TensorFlow library. More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters. Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput, and monitoring facilitates both the work of configuration and the stability of deep learning jobs.
Deep Learning on Apache® Spark™: Workflows and Best Practices
Deep Learning on Apache® Spark™: Workflows and Best Practices
Databricks
The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Virtual Analytics Platform, will present some best practices for building deep learning pipelines with Spark. Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including: * optimizing cluster setup; * configuring the cluster; * ingesting data; and * monitoring long-running jobs. We will demonstrate the techniques we cover using Google’s popular TensorFlow library. More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters. Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput, and monitoring facilitates both the work of configuration and the stability of deep learning jobs.
Deep Learning on Apache® Spark™: Workflows and Best Practices
Deep Learning on Apache® Spark™: Workflows and Best Practices
Jen Aman
Quick introduction to Python for Pace University undergraduate students. Includes an intro to Jupyter Notebook, the Python libraries scikit-learn and pandas.
Introduction To Python
Introduction To Python
Vanessa Rene
Stitch Fix aspires to help you find the style that you will love. Data, the backbone of the business, is used to help with styling recommendations, demand modeling, user acquisition, and merchandise planning and also to influence business decisions throughout the organization. These decisions are backed by algorithms and data collected and interpreted based on client preferences. Neelesh Srinivas Salian offers an overview of the compute infrastructure used by the data science team at Stitch Fix, covering the architecture, tools within the larger ecosystem, and the challenges that the team overcame along the way. Apache Spark plays an important role in Stitch Fix’s data platform, and the company’s data scientists use Spark for their ETL and Presto for their ad hoc queries. The goal for the team running the compute infrastructure is to understand and make the data scientists’ lives easier, particularly in terms of usability of Spark, by building tools that expedite the process of getting started with Spark and transitioning from an ad hoc to a production workflow. The compute infrastructure is a part of the data platform that is responsible for all the needs of data scientists as Stitch Fix. Neelesh shares Stitch Fix’s journey, exploring its ad hoc and production infrastructure and detailing its in-house tools and how they work in synergy with open source frameworks in a cloud environment. Neelesh also discusses the additional improvements to the infrastructure that help persist information for future use and optimization and explains how the implementation of Amazon’s EMR FS has helped make it easier to read from the S3 source.
Improving ad hoc and production workflows at Stitch Fix
Improving ad hoc and production workflows at Stitch Fix
Stitch Fix Algorithms
Koalas is an open source project that provides pandas APIs on top of Apache Spark. Pandas is the standard tool for data science and it is typically the first step to explore and manipulate a data set, but pandas does not scale well to big data. Koalas fills the gap by providing pandas equivalent APIs that work on Apache Spark. There are also many libraries trying to scale pandas APIs, such as Vaex, Modin, and so on. Dask is one of them and very popular among pandas users, and also works on its own cluster similar to Koalas which is on top of Spark cluster. In this talk, we will introduce Koalas and its current status, and the comparison between Koalas and Dask, including benchmarking.
Koalas: How Well Does Koalas Work?
Koalas: How Well Does Koalas Work?
Databricks
Bootcamp Data Science using Cloudera
Bootcamp Data Science using Cloudera
Bootcamp Data Science using Cloudera
António Rodrigues
Detect hidden sensitive data in Hadoop Clusters
Chlorine
Chlorine
Benoy Antony
Stitch Fix aspires to help you find the style that you will love. Data, the backbone of the business, is used to help with styling recommendations, demand modeling, user acquisition, and merchandise planning and also to influence business decisions throughout the organization. These decisions are backed by algorithms and data collected and interpreted based on client preferences. This talk offers an overview of the compute infrastructure used by the data science team at Stitch Fix, covering the architecture, tools within the larger ecosystem, and the challenges that the team overcame along the way. Apache Spark plays an important role in Stitch Fix’s data platform, and the company’s data scientists use Spark for their ETL and Presto for their ad hoc queries. The goal for the team running the compute infrastructure is to understand and make the data scientists’ lives easier, particularly in terms of usability of Spark, by building tools that make it easier to get started with Spark and transition themselves to a daily workflow. The compute infrastructure is a part of the data platform that is responsible for all the needs of data scientists as Stitch Fix. In this talk, we look at Stitch Fix’s journey, exploring its Spark setup, in-house tools and how they work in synergy with open source frameworks in a cloud environment. There are additional improvements to the infrastructure that help persist information for future use and optimization and we look at how the implementation of Amazon’s EMR FS has helped make it easier for us to read from the S3 source.
A compute infrastructure for data scientists
A compute infrastructure for data scientists
Stitch Fix Algorithms
I am shubham sharma graduated from Acropolis Institute of technology in Computer Science and Engineering. I have spent around 2 years in field of Machine learning. I am currently working as Data Scientist in Reliance industries private limited Mumbai. Mainly focused on problems related to data handing, data analysis, modeling, forecasting, statistics and machine learning, Deep learning, Computer Vision, Natural language processing etc. Area of interests are Data Analytics, Machine Learning, Machine learning, Time Series Forecasting, web information retrieval, algorithms, Data structures, design patterns, OOAD.
Python ml
Python ml
Shubham Sharma
In this webinar, we'll see how to use Spark to process data from various sources in R and Python and how new tools like Spark SQL and data frames make it easy to perform structured data processing.
Data processing with spark in r & python
Data processing with spark in r & python
Maloy Manna, PMP®
Much of Hadoop adoption thus far has been for use cases such as processing log files, text mining, and storing masses of file data -- all very necessary, but largely not exciting. In this presentation, Michael Cutler presents a selection of methodologies, primarily using Mahout, that will enable you to derive real insight into your data (mined in Hadoop) and build a recommendation engine focused on the implicit data collected from your users.
Hadoop World 2011: Data Mining in Hadoop, Making Sense of it in Mahout! - Mic...
Hadoop World 2011: Data Mining in Hadoop, Making Sense of it in Mahout! - Mic...
Cloudera, Inc.
Presenter: Peter Wang, Co-Founder & CTO, Continuum Analytics
New Capabilities in the PyData Ecosystem
New Capabilities in the PyData Ecosystem
Turi, Inc.
Slides for the workshop on Parallel Programming in Python I gave on November 10th, 2015 at PyData NYC.
Parallel Programming in Python: Speeding up your analysis
Parallel Programming in Python: Speeding up your analysis
Manojit Nandi
outline
Course outline
Course outline
SumbalImran2
The Briefing Room with Dr. Robin Bloor and Teradata Live Webcast on May 20, 2014 Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=f09e84f88e4ca6e0a9179c9a9e930b82 Traditional data warehouses have been the backbone of corporate decision making for over three decades. With the emergence of Big Data and popular technologies like open-source Apache™ Hadoop®, some analysts question the lifespan of the data warehouse and the future role it will play in enterprise information management. But it’s not practical to believe that emerging technologies provide a wholesale replacement of existing technologies and corporate investments in data management. Rather, a better approach is for new innovations and technologies to complement and build upon existing solutions. Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor as he explains where tomorrow’s data warehouse fits in the information landscape. He’ll be briefed by Imad Birouty of Teradata, who will highlight the ways in which his company is evolving to meet the challenges presented by different types of data and applications. He will also tout Teradata’s recently-announced Teradata® Database 15 and Teradata® QueryGrid™, an analytics platform that enables data processing across the enterprise. Visit InsideAnlaysis.com for more information.
Not Your Father’s Data Warehouse: Breaking Tradition with Innovation
Not Your Father’s Data Warehouse: Breaking Tradition with Innovation
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Introduction to Data Science
Introduction to Data Science
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Introduction: Intelligence Artificielle, Machine Learning et Deep Learning
Introduction: Intelligence Artificielle, Machine Learning et Deep Learning
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The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Virtual Analytics Platform, will present some best practices for building deep learning pipelines with Spark. Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including: * optimizing cluster setup; * configuring the cluster; * ingesting data; and * monitoring long-running jobs. We will demonstrate the techniques we cover using Google’s popular TensorFlow library. More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters. Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput, and monitoring facilitates both the work of configuration and the stability of deep learning jobs.
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Koalas is an open source project that provides pandas APIs on top of Apache Spark. Pandas is the standard tool for data science and it is typically the first step to explore and manipulate a data set, but pandas does not scale well to big data. Koalas fills the gap by providing pandas equivalent APIs that work on Apache Spark. There are also many libraries trying to scale pandas APIs, such as Vaex, Modin, and so on. Dask is one of them and very popular among pandas users, and also works on its own cluster similar to Koalas which is on top of Spark cluster. In this talk, we will introduce Koalas and its current status, and the comparison between Koalas and Dask, including benchmarking.
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Hadoop World 2011: Data Mining in Hadoop, Making Sense of it in Mahout! - Mic...
Hadoop World 2011: Data Mining in Hadoop, Making Sense of it in Mahout! - Mic...
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Parallel Programming in Python: Speeding up your analysis
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Manojit Nandi
outline
Course outline
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The Briefing Room with Dr. Robin Bloor and Teradata Live Webcast on May 20, 2014 Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=f09e84f88e4ca6e0a9179c9a9e930b82 Traditional data warehouses have been the backbone of corporate decision making for over three decades. With the emergence of Big Data and popular technologies like open-source Apache™ Hadoop®, some analysts question the lifespan of the data warehouse and the future role it will play in enterprise information management. But it’s not practical to believe that emerging technologies provide a wholesale replacement of existing technologies and corporate investments in data management. Rather, a better approach is for new innovations and technologies to complement and build upon existing solutions. Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor as he explains where tomorrow’s data warehouse fits in the information landscape. He’ll be briefed by Imad Birouty of Teradata, who will highlight the ways in which his company is evolving to meet the challenges presented by different types of data and applications. He will also tout Teradata’s recently-announced Teradata® Database 15 and Teradata® QueryGrid™, an analytics platform that enables data processing across the enterprise. Visit InsideAnlaysis.com for more information.
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Auto eda
1.
Mohamedanis.benlasmar@esprit.tnLotfi.ncib@esprit.tn ahmed.rebai@esprit.tn AutoEDA using
dataprep and pandas-profiling
2.
Plan 1 ▪ Data Analytics
Life cycle ▪ Exploratory Data Analysis EDA ▪ Exploratory data analysis Using Python Libraries ▪ How to install and use dataprep.eda ▪ How to install and use pandas-profiling ▪ Conclusion
3.
Data Analytics Life
cycle 2
4.
3 Exploratory Data Analysis
EDA
5.
Exploratory data analysis
Using Python Libraries 4
6.
5 Exploratory data analysis
Using Python Libraries
7.
6 Exploratory data analysis
Using Python Libraries
8.
7 Exploratory data analysis
Using Python Libraries
9.
8 How to install
and use dataprep.eda
10.
9 How to install
and use dataprep.eda
11.
10 How to install
and use pandas-profiling
12.
Conclusion 11 When using pandas_profiling, dataprep.eda… When
using pandas, seaborn, matplotlib…
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