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10 Things You Are Not Told About Data Science

Details: 5. Computers and machine learning cannot detect bias in data. Computers have no context of what data is capturing and not capturing. To the computer, data is just numbers. As a data science professional, qualitative analysis of data is just as important as the empirical. I am not a fan of the phrase being “data-driven.”. towards data science

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What are the Data Science and Data Analytics Tools

Details: Data visualization is a graphical representation of data. It is as important as any other aspect of a data science project. A clear and concise visualization can help communicate key information about data for better and quick decision-making because more than 65% of people are visual learners according to the ILS test statistics. american association for the advancement of science

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The Most Crucial Part of Your First Data Science Project

Details: Photo by Sergey Zolkin on Unsplash. I published my first ever data science project in my senior year of high school, in September 2020. The feeling while working on that project is something I always remember to this day. I woke up every day that month, excited to make more progress in my code and eventually share my insights with the community. online masters in data science

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Getting hands-on with DBT — Data Build Tool

Details: Step 5: Verify data is created in Postgres. Since we are using postgres in our warehouse, the data created by the dummy model(our transformation), can be verified in postgres. To see how it was created take a look at my_first_dbt_model.sql in your project. postgres=# select * from my_first_dbt_model; id — — 1 (1 row) Step 5: Run tests medium data science

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6 Hierarchical Data Visualizations by Kruthi Krishnappa Jul, 2022

Details: Hierarchical data is a type of data structure where data points are linked to each other through parent-child relationships which form a tree structure. Hierarchal data is a common data structure so it is important to know how to visualize it. The visualization techniques used for this vary from other data structures because of the need to ms in data science

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So You’ve Got a Really Big Dataset. Here’s How You Clean It.

Details: A simple way is to fill the missing data based on other values in the column. If the column has skewed data, take the median (numerical) or mode (non-numeric) so that you are drawing from the majority and don’t end up shifting the distribution. If the column has unskewed data, take the mean for the same reasons! data science udacity

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July Edition: Zooming In on Statistics by TDS Editors Jul, 2022

Details: For data professionals, that means statistics: a discipline that can occasionally feel dusty and underappreciated despite forming the basis on which everything else—shiny gizmos and all—rests. As Adrienne Kline aptly puts it in one of our highlights this month (scroll down to find it), “while many of these exciting algorithms have immense

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Deploying Your Machine Learning Model Is Just the Beginning

Details: The amount of data drift depends on the problem domain, an is especially severe in adversarial domains such as fraud and abuse. What to do about this problem? One solution is to add a buffer: for example, if the business goal is a precision of 95%, you can try tuning your model to an operating point of 96–97% on the offline evaluation set in

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Towards Geometric Deep Learning I: On the Shoulders of Giants

Details: T he last decade has witnessed an experimental revolution in data science and machine learning, epitomised by deep learning methods. Indeed, many high-dimensional learning tasks previously thought to be beyond reach — computer vision, playing Go, or protein folding — are in fact feasible with appropriate computational scale.

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Extract State-of-the-Art Insights from every Piece of Text

Details: To increase the quality of our input data we remove all news records which have less than 10 words per headline. Applying this condition reduces the number of records from 200853 to 189339. After having dropped all records with headlines that have less than 10 words, and having applied an 80/20 training and validation dataset split the category

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GANs: Generative Adversarial Networks — An Advanced Solution …

Details: The two models compete against each other in a zero-sum game. The generator model tries to generate new data samples similar to those in the problem domain. Meanwhile, the discriminator tries to identify whether the example presented is fake (comes from a generator ) or real (comes from the actual data domain).

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Matching, Weighting, or Regression

Details: There is a tight connection between the IPW estimator and linear regression with covariates. This is particularly evident when we have a one-dimensional, discrete covariate X. In this case, the estimand of IPW (i.e. the quantity that IPW estimates) is given by. Equivalent formulation of the IPW estimand, image by Author.

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Computer Vision: Convolution Basics by Harsh Yadav Jul, 2022

Details: We have different kernels for different tasks like blurring, sharpening or edge detection. The convolution happens between the input image and the given kernel. It is the sliding dot product between the kernel and the localised section of the input image. Figure 4: Input (5, 5); Kernel (3, 3); Convolution 2D (Image by Author) In the above image

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Data Scientists Need to Know Just One Statistical Test

Details: As a consequence, data scientists may feel overwhelmed and ask themselves: “Should I know all of them? And how will I know when to use one over the other?” I am here to reassure you: as a data professional, there is only one test that you need to know. Not because 1 test is important and the other 103 are negligible. But because:

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Understand Context Managers in Python and Learn to Use Them …

Details: Create a custom context manager. Let’s now create a custom context manager in order to understand the technical mechanism behind the scene. To create a context manager, we can create a class that implements the __enter__() and __exit__() magic methods.__enter__() includes the setup code for the context and will be executed when the context is created. . It …

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How To Sort Pandas DataFrames Towards Data Science

Details: All we need to do this time is to provide the column names as a list and pass it into by argument: >>> df.sort_values (by= ['colB', 'colC']) The above statement will sort the DataFrame into ascending order based on the values of columns colB and colC: colA colB colC colD colE. 0 1 A 140.0 False 3.5.

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YOLOv6: next-generation object detection — review and comparison

Details: The field of computer vision has rapidly evolved in recent years and achieved results that seemed like science fiction a few years back. From analyzing X-ray images and diagnosing patients to (semi-)autonomous cars, we’re witnessing a revolution in the making. These breakthroughs have many causes — building better, more accessible compute …

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Are you interpreting your logistic regression correctly

Details: When data scientists start a new classification project, logistic regression is often the first model we try. We use it to get a feeling for the most important features and the direction of the dependence. Afterwards, we may switch to a less interpretable classifier such as gradient boosted trees or random forests, if we want to gain

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Some Of The Most Important SQL Commands Towards Data …

Details: SELECT strftime ('%m', OrderDate) as Month, SUM (Quantity) as Total_Quantity. from Dummy_Sales_Data_v1. GROUP BY strftime ('%m', OrderDate) If you are also using SQLite DB Browser like me, you have to use the function strftime () to extract the date parts as below. You need to use ‘%m’ in strftime () to extract month.

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Beautiful Bars: Scaled Gradient Fill on Bar Plots

Details: Even better, we can use scaled gradients and custom scaled gradients to teach our audience how to interpret the data. The good news — you now have the tools to make beautiful (and meaningful!) bar plots. The bad news — standard bar plots will now look more plain and boring to you than ever before (scroll up to the first bar plot and cringe

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5 Steps to Build Efficient Data Pipelines with Apache Airflow

Details: Developing Cost-Efficient Data Pipelines. As a data engineer, one of the major concerns while working on a project is the efficiency of the data pipeline that is required to process terabytes worth of data. Although the solution is usually straightforward, there might be instances where pipeline architecture, infrastructure or the underlying

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An Introduction to Regularization by Aashish Nair Jul, 2022

Details: First, we prepare the data, creating train and test sets. Most linear classifiers in the Scikit-learn module allow users to modify the regularization technique with the penalty and C parameters. The penalty parameter refers to the regularization technique incorporated by the algorithm. The C parameter defines the strength of regularization used

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On Fading Lockdown Effectiveness. A Data Analysis of the …

Details: To measure the level of adherence between restrictions and mobility we estimated the strength of the relationship between the daily restriction and mobility data, to measure the degree to which changes in the levels of travel restrictions impact non-essential travel. We used two adherence estimates to do this: Coefficient of Determination (r-squared or r2) — based on …

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Geo Lift Experiments II: Spotify Blend Case Study

Details: The data is also not representative of actual Spotify user engagement data. Let’s import our dependencies and explore the experiment data. import pandas as pd import numpy as np import pymc3 as pm import theano.tensor as tt import matplotlib.pyplot as plt import arviz as az experiment_data = pd.read_csv('experiment_data.csv') experiment_data

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Buy Till You Die: Understanding Customer Lifetime Value

Details: With the data at hand, we need to calculate four distinct quantities for each customer: Recency , or the time between the customer’s first and last purchase; Frequency , or the count of time periods the customer made a purchase in (Important note: some resources around the web claim that frequency is the number of repeat purchases the

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The gap between research and robust AI Towards Data Science

Details: Hence, robust AI is one of the key open problems in AI research. We will not bridge the gap between research and robust AI by focusing on standard image classification datasets, like ImageNet. Instead, we need to create algorithms to increase the robustness and interpretability of Deep Learning models. You can find all the code in this repo.

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PyScript v. Flask: How to Create a Python App in the Browser or on …

Details: To get the data we need to use the function open_url from the Pyodide package (PyScript is based on Pyodide and its library is integrated into PyScript). We can then create a Pandas dataframe from this data. Next, we filter the data. The dataframe currently contains data for several decades but we are only going to use that from the year 2020.

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Bulk – Towards Data Science

Details: Learn different ways to index documents in bulk efficiently — When we need to create an Elasticsearch index, the data sources are normally not normalized and cannot be imported directly. The original data can be stored in a database, in raw CSV/XML files, or even obtained from a third-party API. In this case, we need to pre-process the data

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Understanding Python Context-Managers for Absolute Beginners

Details: Another example lies in database-connections. After we create a connection to a database and use it to read/write data to that database, we mustn’t forget to commit the data and close the connection. The regular implementation: cursor = conn.cursor() cursor.execute("SELECT * FROM sometable") cursor.close()

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How I Landed an Amazon SDE Internship Without a Computer …

Details: I would have brushed up more on Data Structures and Algorithms. This is something that certainly let me down and I was lucky enough to get a question that could be solved with basic data structures that are a a built in part of Python. This could have certainly tripped me up otherwise and is something that I will look to brush up on in the future.

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ML Model Deployment Strategies. An illustrated guide to …

Details: 22 hours ago · Data and Concept drift — Over a span of time real-world data keeps changing, and might not be reflected in the model. Like, say, how buying power is related to salaries, which might change yearly or monthly. Or how the consumer buying pattern changed during the Covid-19 pandemic, but models mostly relied on historical data.

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Data Democratization. Brief Introduction, Definition, Pros &… by …

Details: In today’s world, every business is bombarded with data from each and every angle possible. There always remains a constant pressure to use various insights we garner from the data to improvise on our business performance. Hence, the incredible amount of the usage of the processed data has surged the desire and demand for data-democratization.So, if you are …

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What is a Data Hub

Details: A Data Hub is a data exchange with frictionless data flow at its core. It can be described as a solution consisting of different technologies: Data Warehouse, Engineering, Data Science. It’s rather a technology, but an approach to more effectively determine where, when, and for whom data needs to be mediated, shared, and then linked and/or

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10 Sklearn Gems Buried In the Docs Waiting To Be Found

Details: The regressor parameter accepts both regressors or pipelines that end with them. It also has transformer parameter for which you pass a transformer class to be applied on the target. If the transformer is a function, like np.log, you can pass it to func argument.. Then, calling fit will transform both the feature and target arrays and fits the regressor. . Learn more about it …

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Real-Time Typeahead Search with Elasticsearch (AWS OpenSearch)

Details: Now, with the data uploaded, we have done all the work on the server-side. OpenSearch automatically indexes the data to be ready for queries. We can now start working on the client-side to querying the data from the domain. To read more about the querying languages, here are 2 options: Get started with the AWS OpenSearch Service Developer Guide

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System Monitoring Tools – Towards Data Science

Details: This article is written in partnership with Kyle Kirwan, Co-founder and CEO at Bigeye What Is Observability? In 1969, humans first stepped on the moon thanks to a lot of clever engineering and 150,000 lines of code. Among other things, this code enabled engineers at mission control to have the full view of…. Observability. 8 min read.

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The fascinating world of Voronoi diagrams by Francesco Bellelli

Details: In data science, Lloyd’s algorithm is at the basis of k-means clustering — one of the most popular clustering algorithms. k-means clustering is typically initiated by taking kk random “centroids” in space. Then, data points are grouped in kk clusters by alternating between 1) assigning data points to the closest “centroid” (this is

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