Three Stats Pitfalls Facing the New Data Scientist with Sean Owen

•

0 gefällt mir•617 views

1) Data analysis can lead to misleading conclusions if analysts do not account for causation and simply observe correlations in data. Correlation does not necessarily imply causation. 2) When analyzing treatments or interventions, it is important to control for confounding variables but not mediating variables to accurately assess causal relationships. 3) Variables that are actually colliders (common effects of other variables) can introduce spurious correlations if those other common causes are not accounted for. 4) Techniques from causal inference and probabilistic graphical models, like do-calculus, can help data scientists properly reason about and interpret causal effects and the results of interventions based on observational data.

Daten & Analysen

Three Stats Pitfalls Facing  
the New Data Scientist
Sean Owen

2
Do I Know You?
• Apache Spark committer, PMC 
• “Advanced Analytics with Spark” 
• Recently: Director, Data Science @
Cloudera

3
Correlation is not causation.
But then, what is causation?

4
Which Best-Fit Line Is Best?
lm(speed ~ dist, cars) lm(dist ~ speed, cars)

5
Which Treatment is Better?
Treatment A Treatment B
Small Stones 93% (81/87) 87% (234/270)
Large Stones 73% (192/263) 69% (55/80)
78% (273/350) 83% (289/350)

6
Now, Which Treatment is Better?
Treatment A Treatment B
Low Blood pH 93% (81/87) 87% (234/270)
High Blood pH 73% (192/263) 69% (55/80)
78% (273/350) 83% (289/350)

7
Carburetors and Axle Ratio Uncorrelated
lm(drat ~ carb, mtcars)
r = -0.09

8
… Except in Both Halves of the Data?
lm(drat ~ carb, mtcars[ 
which(mtcars$cyl >= 6),])
lm(drat ~ carb, mtcars[ 
which(mtcars$cyl < 6),])
r = 0.52 r = 0.22

Resolution: Causation
• Humans reason causally
• Data doesn’t contain causal information
• Data correlations consistent with multiple causal models
• Correct inference requires adding causal model
10

11
One Consistent with Causal Knowledge
disti = β0 + β1speedi + ϵi

13
Controlling Confounders is Right
Treatment A Treatment B
Small Stones 93% (81/87) 87% (234/270)
Large Stones 73% (192/263) 69% (55/80)
78% (273/350) 83% (289/350)

15
Controlling Mediators is Wrong
Treatment A Treatment B
Low Blood pH 93% (81/87) 87% (234/270)
High Blood pH 73% (192/263) 69% (55/80)
78% (273/350) 83% (289/350)

17
Colliders Create Correlation
lm(drat ~ carb, mtcars)
r = -0.09

20
do-Calculus
P(Y|X) ≠ P(Y|do(X))
Just because it’s more often raining when you 
walk outside with an umbrella … 
… doesn’t mean that you carrying an umbrella  
makes it more likely to be raining.

22
P(C|do(S)) =
∑
t
P(C|do(S), t)P(t|do(S))
=
∑
t
P(C|do(S), do(t))P(t|do(S))
=
∑
t
P(C|do(S), do(t))P(t|S)
=
∑
t
P(C|do(t))P(t|S)
=
∑
s′
∑
t
P(C|do(t), s′)P(s′|do(t))P(t|S)
=
∑
s′
∑
t
P(C|t, s′)P(s′|do(t))P(t|S)
=
∑
s′
∑
t
P(C|t, s′)P(s′)P(t|S)

Conclusion
• Must bring causal info to data for proper interpretation
• Know common causal pitfalls!
• PGMs help reason about causal effects
• Do-calculus can clarify reasoning about intervention
23

Weitere ähnliche Inhalte

Ähnlich wie Three Stats Pitfalls Facing the New Data Scientist with Sean Owen

Application of Statistical and mathematical equations in Chemistry Part 2

Awad Albalwi

55202

احمد الجسار

Cairo 02 Stat Inference

ahmad bassiouny

Reconciling Mass And Energy Balances In An Ethylene Complex

Jim Cahill

Industrial plant optimization in reduced dimensional spaces

Capstone

a detailed discussion of the results in terms of the streamline profiles, isotherm contours, distribution of local Nusselt number, variation of velocity components, etc., is also presented. Finally, from an application standpoint, a simple correlation for the average Nusselt number is presented, which can be used for the interpolation of the present results for the intermediate values of the governing parameters in a new application.

Natural Convection Heat Transfer of Viscoelastic Fluids in a Horizontal Annulus

PMOHANSAHU

Development and validation of rp hplc method for simultaneous

chandu chatla

Estimating Tail Parameters

Alejandro Ortega

ETW3410 Assignment 2 final draft ex5

AU YONG KIN LUN, JEFFREY

Two Means, Two Dependent Samples, Matched Pairs

Long Beach City College

Chapter 3.pptx

mahamoh6

FBA

Will Gikandi

supplement-6-e28093-spc.ppt- SPC_ Operation Management

Ranjeet338208

DOE Applications in Process Chemistry Presentation

saweissman

The International Conference on Harmonization (ICH) released its Q3D Guideline for Elemental Impurities in December 2014, initiating reviews and changes in quality testing programs in bio/pharmaceutical companies around the world. In advance of the implementation dates, companies need to assess the risks of potential elemental impurities in their process and materials streams. In this presentation, experts will review the requirements of elemental impurities guidelines from ICH, the European Pharmacopeia, and United States Pharmacopeia, outline practical recommendations to address implementation challenges, and discuss key considerations for analytical testing programs.

Practical Implementation of the New Elemental Impurities Guidelines May 2015

SGS

Robots, Small Molecules & R

Rajarshi Guha

Kieran Laxen - Assessing the impacts of short-term power generation - DMUG17

IES / IAQM

Data-Handling part 1 .ppt

AhmadHashlamon

Sensitivity and Reproducibility of Protein Aggregate Analysis by Sedimentatio...

KBI Biopharma

sup06.ppt

mahmoudmustafa87

Ähnlich wie Three Stats Pitfalls Facing the New Data Scientist with Sean Owen (20)

Application of Statistical and mathematical equations in Chemistry Part 2

55202

Cairo 02 Stat Inference

Reconciling Mass And Energy Balances In An Ethylene Complex

Industrial plant optimization in reduced dimensional spaces

Natural Convection Heat Transfer of Viscoelastic Fluids in a Horizontal Annulus

Development and validation of rp hplc method for simultaneous

Estimating Tail Parameters

ETW3410 Assignment 2 final draft ex5

Two Means, Two Dependent Samples, Matched Pairs

Chapter 3.pptx

FBA

supplement-6-e28093-spc.ppt- SPC_ Operation Management

DOE Applications in Process Chemistry Presentation

Practical Implementation of the New Elemental Impurities Guidelines May 2015

Robots, Small Molecules & R

Kieran Laxen - Assessing the impacts of short-term power generation - DMUG17

Data-Handling part 1 .ppt

Sensitivity and Reproducibility of Protein Aggregate Analysis by Sedimentatio...

sup06.ppt

Mehr von Databricks

DW Migration Webinar-March 2022.pptx

Databricks

The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse. Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today. Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow. This is an educational event. Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.

Data Lakehouse Symposium | Day 1 | Part 1

Databricks

Data Lakehouse Symposium | Day 1 | Part 2

Databricks

Data Lakehouse Symposium | Day 2

Databricks

Data Lakehouse Symposium | Day 4

Databricks

In this session, learn how to quickly supplement your on-premises Hadoop environment with a simple, open, and collaborative cloud architecture that enables you to generate greater value with scaled application of analytics and AI on all your data. You will also learn five critical steps for a successful migration to the Databricks Lakehouse Platform along with the resources available to help you begin to re-skill your data teams.

5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop

Databricks

Bad data leads to bad decisions and broken customer experiences. Organizations depend on complete and accurate data to power their business, maintain efficiency, and uphold customer trust. With thousands of datasets and pipelines running, how do we ensure that all data meets quality standards, and that expectations are clear between producers and consumers? Investing in shared, flexible components and practices for monitoring data health is crucial for a complex data organization to rapidly and effectively scale. At Zillow, we built a centralized platform to meet our data quality needs across stakeholders. The platform is accessible to engineers, scientists, and analysts, and seamlessly integrates with existing data pipelines and data discovery tools. In this presentation, we will provide an overview of our platform’s capabilities, including: Giving producers and consumers the ability to define and view data quality expectations using a self-service onboarding portal Performing data quality validations using libraries built to work with spark Dynamically generating pipelines that can be abstracted away from users Flagging data that doesn’t meet quality standards at the earliest stage and giving producers the opportunity to resolve issues before use by downstream consumers Exposing data quality metrics alongside each dataset to provide producers and consumers with a comprehensive picture of health over time

Democratizing Data Quality Through a Centralized Platform

Databricks

Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.

Learn to Use Databricks for Data Science

Databricks

Application performance monitoring (APM) has become the cornerstone of software engineering allowing engineering teams to quickly identify and remedy production issues. However, as the world moves to intelligent software applications that are built using machine learning, traditional APM quickly becomes insufficient to identify and remedy production issues encountered in these modern software applications. As a lead software engineer at NewRelic, my team built high-performance monitoring systems including Insights, Mobile, and SixthSense. As I transitioned to building ML Monitoring software, I found the architectural principles and design choices underlying APM to not be a good fit for this brand new world. In fact, blindly following APM designs led us down paths that would have been better left unexplored. In this talk, I draw upon my (and my team’s) experience building an ML Monitoring system from the ground up and deploying it on customer workloads running large-scale ML training with Spark as well as real-time inference systems. I will highlight how the key principles and architectural choices of APM don’t apply to ML monitoring. You’ll learn why, understand what ML Monitoring can successfully borrow from APM, and hear what is required to build a scalable, robust ML Monitoring architecture.

Why APM Is Not the Same As ML Monitoring

Databricks

Autonomy and ownership are core to working at Stitch Fix, particularly on the Algorithms team. We enable data scientists to deploy and operate their models independently, with minimal need for handoffs or gatekeeping. By writing a simple function and calling out to an intuitive API, data scientists can harness a suite of platform-provided tooling meant to make ML operations easy. In this talk, we will dive into the abstractions the Data Platform team has built to enable this. We will go over the interface data scientists use to specify a model and what that hooks into, including online deployment, batch execution on Spark, and metrics tracking and visualization.

The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix

Databricks

In this talk, I will dive into the stage level scheduling feature added to Apache Spark 3.1. Stage level scheduling extends upon Project Hydrogen by improving big data ETL and AI integration and also enables multiple other use cases. It is beneficial any time the user wants to change container resources between stages in a single Apache Spark application, whether those resources are CPU, Memory or GPUs. One of the most popular use cases is enabling end-to-end scalable Deep Learning and AI to efficiently use GPU resources. In this type of use case, users read from a distributed file system, do data manipulation and filtering to get the data into a format that the Deep Learning algorithm needs for training or inference and then sends the data into a Deep Learning algorithm. Using stage level scheduling combined with accelerator aware scheduling enables users to seamlessly go from ETL to Deep Learning running on the GPU by adjusting the container requirements for different stages in Spark within the same application. This makes writing these applications easier and can help with hardware utilization and costs. There are other ETL use cases where users want to change CPU and memory resources between stages, for instance there is data skew or perhaps the data size is much larger in certain stages of the application. In this talk, I will go over the feature details, cluster requirements, the API and use cases. I will demo how the stage level scheduling API can be used by Horovod to seamlessly go from data preparation to training using the Tensorflow Keras API using GPUs. The talk will also touch on other new Apache Spark 3.1 functionality, such as pluggable caching, which can be used to enable faster dataframe access when operating from GPUs.

Stage Level Scheduling Improving Big Data and AI Integration

Databricks

In this talk, I would like to introduce an open-source tool built by our team that simplifies the data conversion from Apache Spark to deep learning frameworks. Imagine you have a large dataset, say 20 GBs, and you want to use it to train a TensorFlow model. Before feeding the data to the model, you need to clean and preprocess your data using Spark. Now you have your dataset in a Spark DataFrame. When it comes to the training part, you may have the problem: How can I convert my Spark DataFrame to some format recognized by my TensorFlow model? The existing data conversion process can be tedious. For example, to convert an Apache Spark DataFrame to a TensorFlow Dataset file format, you need to either save the Apache Spark DataFrame on a distributed filesystem in parquet format and load the converted data with third-party tools such as Petastorm, or save it directly in TFRecord files with spark-tensorflow-connector and load it back using TFRecordDataset. Both approaches take more than 20 lines of code to manage the intermediate data files, rely on different parsing syntax, and require extra attention for handling vector columns in the Spark DataFrames. In short, all these engineering frictions greatly reduced the data scientists’ productivity. The Databricks Machine Learning team contributed a new Spark Dataset Converter API to Petastorm to simplify these tedious data conversion process steps. With the new API, it takes a few lines of code to convert a Spark DataFrame to a TensorFlow Dataset or a PyTorch DataLoader with default parameters. In the talk, I will use an example to show how to use the Spark Dataset Converter to train a Tensorflow model and how simple it is to go from single-node training to distributed training on Databricks.

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Databricks

There is no doubt Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark has evolved to run both Machine Learning and large scale analytics workloads. There is growing interest in running Apache Spark natively on Kubernetes. By combining the flexibility of Kubernetes and scalable data processing with Apache Spark, you can run any data and machine pipelines on this infrastructure while effectively utilizing resources at disposal. In this talk, Rajesh Thallam and Sougata Biswas will share how to effectively run your Apache Spark applications on Google Kubernetes Engine (GKE) and Google Cloud Dataproc, orchestrate the data and machine learning pipelines with managed Apache Airflow on GKE (Google Cloud Composer). Following topics will be covered: – Understanding key traits of Apache Spark on Kubernetes- Things to know when running Apache Spark on Kubernetes such as autoscaling- Demonstrate running analytics pipelines on Apache Spark orchestrated with Apache Airflow on Kubernetes cluster.

Scaling your Data Pipelines with Apache Spark on Kubernetes

Databricks

Pipelines have become ubiquitous, as the need for stringing multiple functions to compose applications has gained adoption and popularity. Common pipeline abstractions such as “fit” and “transform” are even shared across divergent platforms such as Python Scikit-Learn and Apache Spark. Scaling pipelines at the level of simple functions is desirable for many AI applications, however is not directly supported by Ray’s parallelism primitives. In this talk, Raghu will describe a pipeline abstraction that takes advantage of Ray’s compute model to efficiently scale arbitrarily complex pipeline workflows. He will demonstrate how this abstraction cleanly unifies pipeline workflows across multiple platforms such as Scikit-Learn and Spark, and achieves nearly optimal scale-out parallelism on pipelined computations. Attendees will learn how pipelined workflows can be mapped to Ray’s compute model and how they can both unify and accelerate their pipelines with Ray.

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Databricks

In this talk about zipline, we will introduce a new type of windowing construct called a sawtooth window. We will describe various properties about sawtooth windows that we utilize to achieve online-offline consistency, while still maintaining high-throughput, low-read latency and tunable write latency for serving machine learning features.We will also talk about a simple deployment strategy for correcting feature drift – due operations that are not “abelian groups”, that operate over change data.

Sawtooth Windows for Feature Aggregations

Databricks

We want to present multiple anti patterns utilizing Redis in unconventional ways to get the maximum out of Apache Spark.All examples presented are tried and tested in production at Scale at Adobe. The most common integration is spark-redis which interfaces with Redis as a Dataframe backing Store or as an upstream for Structured Streaming. We deviate from the common use cases to explore where Redis can plug gaps while scaling out high throughput applications in Spark. Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue · Why? o Custom queries on top a table; We load the data once and query N times · Why not Structured Streaming · Working Solution using Redis Niche 2 : Distributed Counters · Problems with Spark Accumulators · Utilize Redis Hashes as distributed counters · Precautions for retries and speculative execution · Pipelining to improve performance

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Databricks

In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will, affect the bottom line. As a result, ensuring data quality across the entire ML pipeline is both costly, and cumbersome while data monitoring is often fragmented and performed ad hoc. To address these challenges, we built whylogs, an open source standard for data logging. It is a lightweight data profiling library that enables end-to-end data profiling across the entire software stack. The library implements a language and platform agnostic approach to data quality and data monitoring. It can work with different modes of data operations, including streaming, batch and IoT data. In this talk, we will provide an overview of the whylogs architecture, including its lightweight statistical data collection approach and various integrations. We will demonstrate how the whylogs integration with Apache Spark achieves large scale data profiling, and we will show how users can apply this integration into existing data and ML pipelines.

Re-imagine Data Monitoring with whylogs and Spark

Databricks

Machine learning (ML) models are typically part of prediction queries that consist of a data processing part (e.g., for joining, filtering, cleaning, featurization) and an ML part invoking one or more trained models. In this presentation, we identify significant and unexplored opportunities for optimization. To the best of our knowledge, this is the first effort to look at prediction queries holistically, optimizing across both the ML and SQL components. We will present Raven, an end-to-end optimizer for prediction queries. Raven relies on a unified intermediate representation that captures both data processing and ML operators in a single graph structure. This allows us to introduce optimization rules that (i) reduce unnecessary computations by passing information between the data processing and ML operators (ii) leverage operator transformations (e.g., turning a decision tree to a SQL expression or an equivalent neural network) to map operators to the right execution engine, and (iii) integrate compiler techniques to take advantage of the most efficient hardware backend (e.g., CPU, GPU) for each operator. We have implemented Raven as an extension to Spark’s Catalyst optimizer to enable the optimization of SparkSQL prediction queries. Our implementation also allows the optimization of prediction queries in SQL Server. As we will show, Raven is capable of improving prediction query performance on Apache Spark and SQL Server by up to 13.1x and 330x, respectively. For complex models, where GPU acceleration is beneficial, Raven provides up to 8x speedup compared to state-of-the-art systems. As part of the presentation, we will also give a demo showcasing Raven in action.

Raven: End-to-end Optimization of ML Prediction Queries

Databricks

Semantic segmentation is the classification of every pixel in an image/video. The segmentation partitions a digital image into multiple objects to simplify/change the representation of the image into something that is more meaningful and easier to analyze [1][2]. The technique has a wide variety of applications ranging from perception in autonomous driving scenarios to cancer cell segmentation for medical diagnosis. Exponential growth in the datasets that require such segmentation is driven by improvements in the accuracy and quality of the sensors generating the data extending to 3D point cloud data. This growth is further compounded by exponential advances in cloud technologies enabling the storage and compute available for such applications. The need for semantically segmented datasets is a key requirement to improve the accuracy of inference engines that are built upon them. Streamlining the accuracy and efficiency of these systems directly affects the value of the business outcome for organizations that are developing such functionalities as a part of their AI strategy. This presentation details workflows for labeling, preprocessing, modeling, and evaluating performance/accuracy. Scientists and engineers leverage domain-specific features/tools that support the entire workflow from labeling the ground truth, handling data from a wide variety of sources/formats, developing models and finally deploying these models. Users can scale their deployments optimally on GPU-based cloud infrastructure to build accelerated training and inference pipelines while working with big datasets. These environments are optimized for engineers to develop such functionality with ease and then scale against large datasets with Spark-based clusters on the cloud.

Processing Large Datasets for ADAS Applications using Apache Spark

Databricks

At Adobe Experience Platform, we ingest TBs of data every day and manage PBs of data for our customers as part of the Unified Profile Offering. At the heart of this is a bunch of complex ingestion of a mix of normalized and denormalized data with various linkage scenarios power by a central Identity Linking Graph. This helps power various marketing scenarios that are activated in multiple platforms and channels like email, advertisements etc. We will go over how we built a cost effective and scalable data pipeline using Apache Spark and Delta Lake and share our experiences. What are we storing? Multi Source – Multi Channel Problem Data Representation and Nested Schema Evolution Performance Trade Offs with Various formats Go over anti-patterns used (String FTW) Data Manipulation using UDFs Writer Worries and How to Wipe them Away Staging Tables FTW Datalake Replication Lag Tracking Performance Time!

Massive Data Processing in Adobe Using Delta Lake

Databricks

Mehr von Databricks (20)

DW Migration Webinar-March 2022.pptx

Data Lakehouse Symposium | Day 1 | Part 1

Data Lakehouse Symposium | Day 1 | Part 2

Data Lakehouse Symposium | Day 2

Data Lakehouse Symposium | Day 4

5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop

Democratizing Data Quality Through a Centralized Platform

Learn to Use Databricks for Data Science

Why APM Is Not the Same As ML Monitoring

The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix

Stage Level Scheduling Improving Big Data and AI Integration

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Scaling your Data Pipelines with Apache Spark on Kubernetes

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Sawtooth Windows for Feature Aggregations

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Re-imagine Data Monitoring with whylogs and Spark

Raven: End-to-end Optimization of ML Prediction Queries

Processing Large Datasets for ADAS Applications using Apache Spark

Massive Data Processing in Adobe Using Delta Lake

Kürzlich hochgeladen

Klinik_ Apotek Onlin 085657271886 Solusi Menggugurkan Masalah Kehamilan Anda Jual Obat Aborsi Asli KLINIK ABORSI TERPEECAYA _ Jual Obat Aborsi Cytotec Misoprostol Asli 100% Ampuh Hanya 3 Jam Langsung Gugur || OBAT PENGGUGUR KANDUNGAN AMPUH MANJUR OBAT ABORSI OLINE" APOTIK Jual Obat Cytotec, Gastrul, Gynecoside Asli Ampuh. JUAL ” Obat Aborsi Tuntas | Obat Aborsi Manjur | Obat Aborsi Ampuh | Obat Penggugur Janin | Obat Pencegah Kehamilan | Obat Pelancar Haid | Obat terlambat Bulan | Ciri Obat Aborsi Asli | Obat Telat Bulan | Pil Aborsi Asli | Cara Menggugurkan Konten | Cara Aborsi Tuntas | Harga Obat Aborsi Asli | Pil Aborsi | Jual Obat Aborsi Cytotec | Cara Aborsi Sendiri | Cara Aborsi Usia 1 Bulan | Cara Aborsi Usia 2 Tahun | Cara Aborsi Usia 3 Bulan | Obat Aborsi Usia 4 Bulan | Cara Abrasi Usia 5 Bulan | Cara Menggugurkan Konten | Kandungan Obat Penggugur | Cara Menghitung Usia Konten | Cara Mengatasi Terlambat Bulan | Penjual Obat Aborsi Asli | Obat Aborsi Garansi | Kandungan Obat Peluntur | Obat Telat Datang Bulan | Obat Telat Haid | Obat Aborsi Paling Murah | Klinik Jual Obat Aborsi | Jual Pil Cytotec | Apotik Jual Obat Aborsi | Kandungan Dokter Abrasi | Cara Aborsi Cepat | Jual Obat Aborsi Bergaransi | Jual Obat Cytotec Asli | Obat Aborsi Aman Manjur | Obat Misoprostol Cytotec Asli. "APA ITU ABORSI" “Aborsi Adalah dengan membendung hormon yang di perlukan untuk mempertahankan kehamilan yaitu hormon progesteron, karena hormon ini dibendung, maka jalur kehamilan mulai membuka dan leher rahim menjadi melunak,sehingga mengeluarkan darah yang merupakan tanda bahwa obat telah bekerja || maksimal 1 jam obat diminum || PENJELASAN OBAT ABORSI USIA 1 _7 BULAN Pada usia kandungan ini, pasien akan merasakan sakit yang sedikit tidak berlebihan || sekitar 1 jam ||. namun hanya akan terjadi pada saatdarah keluar merupakan pertanda menstruasi. Hal ini dikarenakan pada usiakandungan 3 bulan,janin sudah terbentuk sebesar kepalan tangan orang dewasa. Cara kerja obat aborsi : JUAL OBAT ABORSI AMPUH dosis 3 bulan secara umum sama dengan cara kerja || DOSIS OBAT ABORSI 2 bulan”, hanya berbedanya selain mengisolasijanin juga menghancurkan janin dengan formula methotrexate dikandungdidalamnya. Formula methotrexate ini sangat ampuh untuk menghancurkan janinmenjadi serpihan-serpihan kecil akan sangat berguna pada saat dikeluarkan nanti. APA ALASAN WANITA MELAKUKAN ABORSI? Aborsi di lakukan wanita hamil baik yang sudah menikah maupun belum menikah dengan berbagai alasan , akan tetapi alasan yang utama adalah alasan-alasan non medis (termasuk aborsi sendiri / di sengaja/ buatan] MELAYANI PEMESANAN OBAT ABORSI SETIAP HARI, SIAP KIRIM KESELURUH KOTA BESAR DI INDONESIA DAN LUAR NEGERI. HUBUNGI PEMESANAN LEBIH NYAMAN VIA WA/: 085657271886

Jual Obat Aborsi Surabaya ( Asli No.1 ) 085657271886 Obat Penggugur Kandungan...

ZurliaSoop

Jual obat aborsi Bandung ( 085657271886 ) Cytote pil telat bulan penggugur ka...

Klinik kandungan

原版定制【微信:153539019】《(曼大毕业证书）曼尼托巴大学毕业证》【微信:153539019】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信153539019】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信153539019】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。

一比一原版(曼大毕业证书）曼尼托巴大学毕业证成绩单留信学历认证一手价格

q6pzkpark

Top profile Call Girls In Hapur [ 7014168258 ] Call Me For Genuine Models We are available 24*7 Booking Contact Details :- WhatsApp Chat :- +91-7014168258 If you're looking for India Call girls you've come to the right place. You'll find some of the most beautiful call girls in our location with. These ladies have pleasing personalities, hot figures, and a passion for physical pleasure. Call girls in India Lucknow Many men have booked them for their erotic and soul-mixing performances, which are sure to leave you with unforgettable memories. #K09 Escort Service India is available in the city for men and women of all ages. They can satisfy your sexual needs and will make your experience even more enjoyable and memorable. Whether you're looking for a blow-job, stripping, lovemaking, or other dirty acts, you'll be able to find a match for your tastes and budget. These highly trained professionals will help you have an unforgettable night. One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7014168258 We are available 24*7 all days of the year. Call us — 7014168258 Thank you for Visiting.

Top profile Call Girls In Hapur [ 7014168258 ] Call Me For Genuine Models We ...

nirzagarg

Gartner's Data Analytics Maturity Model.pptx

chadhar227

Switzerland Constitution 2002.pdf.........

EfruzAsilolu

Vadodara 💋 Call Girl 7737669865 Call Girls in Vadodara Escort service book now Booking Contact Details :- WhatsApp Chat :- +91-7737669865 We offer all types of girls of your choice with space. Our escorts are fully cooperative and understand your needs. All types of call girls like Housewives, College girls,#K09 Russian girls, Muslim girls, Afghani girls, Bengali girls, Working girls, south Indian girls, Punjabi girls, etc. In-Call: — You Can Reach At Our Place in Bangalore Our place Which Is Very Clean Hygienic 100% safe Accommodation. Out-Call: — Service for Out Call You have To Come Pick The Girl From My Place We Also Provide Door-Step Services Hygienic: — Full Ac Neat And Clean Rooms Available In Hotel 24 * 7 Hrs In Bangalore Our Services and Rates: – One Shot — 2500/in call (time ½ hour), 5000/out call Two shot with one girl — 5000/in call (time 1 hour), 6000/out call Body to body massage with sex- 3000/in call (time 1 hour) full night for one person– 8000/in call, 10000/out call (shot limit 4 shot) We are available 24*7 all days of the year

Vadodara 💋 Call Girl 7737669865 Call Girls in Vadodara Escort service book now

gargpaaro

Discover Why Less is More in B2B Research

michael115558

SAC 25 Final National, Regional & Local Angel Group Investing Insights 2024 0...

Elaine Werffeli

Ranking and Scoring Exercises for Research

Rajesh Mondal

原版定制【微信:176555708】【伦敦大学城市学院毕业证（CITY毕业证书）】【微信:176555708】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才留信网服务项目： 1、留学生专业人才库服务（留信分析） 2、国（境）学习人员提供就业推荐信服务 3、留学人员区块链存储服务 → 【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！

怎样办理伦敦大学城市学院毕业证（CITY毕业证书）成绩单学校原版复制

vexqp

Saudi Arabia [ Abortion pills) Jeddah/riaydh/dammam/+966572737505☎️] cytotec tablets uses abortion pills 💊💊 How effective is the abortion pill? 💊💊 +966572737505) "Abortion pills in Jeddah" how to get cytotec tablets in Riyadh " Abortion pills in dammam*💊💊 The abortion pill is very effective. If you’re taking mifepristone and misoprostol, it depends on how far along the pregnancy is, and how many doses of medicine you take:💊💊 +966572737505) how to buy cytotec pills At 8 weeks pregnant or less, it works about 94-98% of the time. +966572737505[ 💊💊💊 At 8-9 weeks pregnant, it works about 94-96% of the time. +966572737505) At 9-10 weeks pregnant, it works about 91-93% of the time. +966572737505)💊💊 If you take an extra dose of misoprostol, it works about 99% of the time. At 10-11 weeks pregnant, it works about 87% of the time. +966572737505) If you take an extra dose of misoprostol, it works about 98% of the time. In general, taking both mifepristone and+966572737505 misoprostol works a bit better than taking misoprostol only. +966572737505 Taking misoprostol alone works to end the+966572737505 pregnancy about 85-95% of the time — depending on how far along the+966572737505 pregnancy is and how you take the medicine. +966572737505 The abortion pill usually works, but if it doesn’t, you can take more medicine or have an in-clinic abortion. +966572737505 When can I take the abortion pill?+966572737505 In general, you can have a medication abortion up to 77 days (11 weeks)+966572737505 after the first day of your last period. If it’s been 78 days or more since the first day of your last+966572737505 period, you can have an in-clinic abortion to end your pregnancy.+966572737505 Why do people choose the abortion pill? Which kind of abortion you choose all depends on your personal+966572737505 preference and situation. With+966572737505 medication+966572737505 abortion, some people like that you don’t need to have a procedure in a doctor’s office. You can have your medication abortion on your own+966572737505 schedule, at home or in another comfortable place that you choose.+966572737505 You get to decide who you want to be with during your abortion, or you can go it alone. Because+966572737505 medication abortion is similar to a miscarriage, many people feel like it’s more “natural” and less invasive. And some+966572737505 people may not have an in-clinic abortion provider close by, so abortion pills are more available to+966572737505 them. +966572737505 Your doctor, nurse, or health center staff can help you decide which kind of abortion is best for you. +966572737505 More questions from patients: Saudi Arabia+966572737505 CYTOTEC Misoprostol Tablets. Misoprostol is a medication that can prevent stomach ulcers if you also take NSAID medications. It reduces the amount of acid in your stomach, which protects your stomach lining. The brand name of this medication is Cytotec®.+966573737505) Unwanted Kit is a combination of two medicines, whi

Cytotec in Jeddah+966572737505) get unwanted pregnancy kit Riyadh

Abortion pills in Riyadh +966572737505 get cytotec

原版定制【微信:176555708】【伦敦大学毕业证（UoL毕业证书）】【微信:176555708】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才留信网服务项目： 1、留学生专业人才库服务（留信分析） 2、国（境）学习人员提供就业推荐信服务 3、留学人员区块链存储服务 → 【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！

怎样办理伦敦大学毕业证（UoL毕业证书）成绩单学校原版复制

vexqp

7. Epi of Chronic respiratory diseases.ppt

ibrahimabdi22

Abortion pills in Jeddah | +966572737505 | Get Cytotec

Abortion pills in Riyadh +966572737505 get cytotec

Building Real-Time Pipelines With FLaNK Timothy Spann, Principal Developer Advocate, Streaming - Cloudera Future of Data meetup, startup grind, AI Camp The combination of Apache Flink, Apache NiFi, and Apache Kafka for building real-time data processing pipelines is extremely powerful, as demonstrated by this case study using the FLaNK-MTA project. The project leverages these technologies to process and analyze real-time data from the New York City Metropolitan Transportation Authority (MTA). FLaNK-MTA demonstrates how to efficiently collect, transform, and analyze high-volume data streams, enabling timely insights and decision-making. Apache NiFi Apache Kafka Apache Flink Apache Iceberg LLM Generative AI Slack Postgresql

DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK

Timothy Spann

SR-101-01012024-EN.docx Federal Constitution of the Swiss Confederation

EfruzAsilolu

原版定制【微信:153539019】《(UCD毕业证书）加州大学戴维斯分校毕业证》【微信:153539019】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信153539019】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信153539019】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。

一比一原版(UCD毕业证书）加州大学戴维斯分校毕业证成绩单原件一模一样

wsppdmt

原版定制【微信:176555708】【旧金山城市学院毕业证（CCSF毕业证书）】【微信:176555708】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才留信网服务项目： 1、留学生专业人才库服务（留信分析） 2、国（境）学习人员提供就业推荐信服务 3、留学人员区块链存储服务 → 【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！

怎样办理旧金山城市学院毕业证（CCSF毕业证书）成绩单学校原版复制

vexqp

原版定制【微信:176555708】【圣地亚哥州立大学毕业证（SDSU毕业证书）】【微信:176555708】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才留信网服务项目： 1、留学生专业人才库服务（留信分析） 2、国（境）学习人员提供就业推荐信服务 3、留学人员区块链存储服务 → 【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！

怎样办理圣地亚哥州立大学毕业证（SDSU毕业证书）成绩单学校原版复制

vexqp

Kürzlich hochgeladen (20)

Jual Obat Aborsi Surabaya ( Asli No.1 ) 085657271886 Obat Penggugur Kandungan...

Jual obat aborsi Bandung ( 085657271886 ) Cytote pil telat bulan penggugur ka...

一比一原版(曼大毕业证书）曼尼托巴大学毕业证成绩单留信学历认证一手价格

Top profile Call Girls In Hapur [ 7014168258 ] Call Me For Genuine Models We ...

Gartner's Data Analytics Maturity Model.pptx

Switzerland Constitution 2002.pdf.........

Vadodara 💋 Call Girl 7737669865 Call Girls in Vadodara Escort service book now

Discover Why Less is More in B2B Research

SAC 25 Final National, Regional & Local Angel Group Investing Insights 2024 0...

Ranking and Scoring Exercises for Research

怎样办理伦敦大学城市学院毕业证（CITY毕业证书）成绩单学校原版复制

Cytotec in Jeddah+966572737505) get unwanted pregnancy kit Riyadh

怎样办理伦敦大学毕业证（UoL毕业证书）成绩单学校原版复制

7. Epi of Chronic respiratory diseases.ppt

Abortion pills in Jeddah | +966572737505 | Get Cytotec

DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK

SR-101-01012024-EN.docx Federal Constitution of the Swiss Confederation

一比一原版(UCD毕业证书）加州大学戴维斯分校毕业证成绩单原件一模一样

怎样办理旧金山城市学院毕业证（CCSF毕业证书）成绩单学校原版复制

怎样办理圣地亚哥州立大学毕业证（SDSU毕业证书）成绩单学校原版复制

Three Stats Pitfalls Facing the New Data Scientist with Sean Owen

1. Three Stats Pitfalls Facing   the New Data Scientist Sean Owen

2. 2 Do I Know You? • Apache Spark committer, PMC  • “Advanced Analytics with Spark”  • Recently: Director, Data Science @ Cloudera

3. 3 Correlation is not causation. But then, what is causation?

4. 4 Which Best-Fit Line Is Best? lm(speed ~ dist, cars) lm(dist ~ speed, cars)

5. 5 Which Treatment is Better? Treatment A Treatment B Small Stones 93% (81/87) 87% (234/270) Large Stones 73% (192/263) 69% (55/80) 78% (273/350) 83% (289/350)

6. 6 Now, Which Treatment is Better? Treatment A Treatment B Low Blood pH 93% (81/87) 87% (234/270) High Blood pH 73% (192/263) 69% (55/80) 78% (273/350) 83% (289/350)

7. 7 Carburetors and Axle Ratio Uncorrelated lm(drat ~ carb, mtcars) r = -0.09

8. 8 … Except in Both Halves of the Data? lm(drat ~ carb, mtcars[  which(mtcars$cyl >= 6),]) lm(drat ~ carb, mtcars[  which(mtcars$cyl < 6),]) r = 0.52 r = 0.22

9. 9 3 Answers

10. Resolution: Causation • Humans reason causally • Data doesn’t contain causal information • Data correlations consistent with multiple causal models • Correct inference requires adding causal model 10

11. 11 One Consistent with Causal Knowledge disti = β0 + β1speedi + ϵi

12. 12 DistanceSpeed

13. 13 Controlling Confounders is Right Treatment A Treatment B Small Stones 93% (81/87) 87% (234/270) Large Stones 73% (192/263) 69% (55/80) 78% (273/350) 83% (289/350)

14. 14 OutcomeTreatment Size

15. 15 Controlling Mediators is Wrong Treatment A Treatment B Low Blood pH 93% (81/87) 87% (234/270) High Blood pH 73% (192/263) 69% (55/80) 78% (273/350) 83% (289/350)

16. 16 OutcomeTreatment pH

17. 17 Colliders Create Correlation lm(drat ~ carb, mtcars) r = -0.09

18. 18 CarburetorsAxle Ratio Cylinders

19. 19 Causation and do-Calculus

20. 20 do-Calculus P(Y|X) ≠ P(Y|do(X)) Just because it’s more often raining when you  walk outside with an umbrella …  … doesn’t mean that you carrying an umbrella   makes it more likely to be raining.

21. 21 CancerSmoking Genes? Tar

23. Conclusion • Must bring causal info to data for proper interpretation • Know common causal pitfalls! • PGMs help reason about causal effects • Do-calculus can clarify reasoning about intervention 23

24. Thank You @sean_r_owen

Three Stats Pitfalls Facing the New Data Scientist with Sean Owen

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Ähnlich wie Three Stats Pitfalls Facing the New Data Scientist with Sean Owen

Ähnlich wie Three Stats Pitfalls Facing the New Data Scientist with Sean Owen (20)

Mehr von Databricks

Mehr von Databricks (20)

Kürzlich hochgeladen

Kürzlich hochgeladen (20)

Three Stats Pitfalls Facing the New Data Scientist with Sean Owen