Pickl AI

FEBRUARY 12, 2024

Here are some key areas often assessed: Programming Proficiency Candidates are often tested on their proficiency in languages such as Python, R, and SQL, with a focus on data manipulation, analysis, and visualization. However, there are a few fundamental principles that remain the same throughout.

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Pickl AI

JULY 10, 2023

R’s data manipulation capabilities make cleaning and preprocessing data easy before further analysis. · Statistical Analysis: R has a rich ecosystem of packages for statistical analysis. You can easily learn R for Data Science through the available online courses in Pickl.AI

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Pickl AI

NOVEMBER 27, 2024

Visualisation and Reporting Python’s Matplotlib and Seaborn libraries are excellent for creating a variety of visualisations, especially during exploratory data analysis. Statistical Analysis and Testing R’s rich ecosystem for hypothesis testing, regression modelling, and Bayesian analysis makes it ideal for statistical tasks.

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Pickl AI

APRIL 3, 2025

It involves handling missing values, correcting errors, removing duplicates, standardizing formats, and structuring data for analysis. Exploratory Data Analysis (EDA): Using statistical summaries and initial visualisations (yes, visualisation plays a role within analysis!)

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Pickl AI

AUGUST 29, 2023

Data Collection: Based on the question or problem identified, you need to collect data that represents the problem that you are studying. Exploratory Data Analysis: You need to examine the data for understanding the distribution, patterns, outliers and relationships between variables.

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Pickl AI

JULY 25, 2023

Their primary responsibilities include: Data Collection and Preparation Data Scientists start by gathering relevant data from various sources, including databases, APIs, and online platforms. They clean and preprocess the data to remove inconsistencies and ensure its quality.

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Pickl AI

SEPTEMBER 18, 2024

The process of conducting Regression Analysis typically involves several steps: Step 1: Data Collection: Gather relevant data for both dependent and independent variables. This data can come from various sources such as surveys, experiments, or historical records.

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Pickl AI

APRIL 26, 2024

I conducted thorough data validation, collaborated with stakeholders to identify the root cause, and implemented corrective measures to ensure data integrity. I would perform exploratory data analysis to understand the distribution of customer transactions and identify potential segments.

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Becoming Human

MAY 15, 2023

In Inferential Statistics, you can learn P-Value , T-Value , Hypothesis Testing , and A/B Testing , which will help you to understand your data in the form of mathematics. For Data Analysis you can focus on such topics as Feature Engineering , Data Wrangling , and EDA which is also known as Exploratory Data Analysis.

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Pickl AI

SEPTEMBER 12, 2024

Anomaly Detection: Identifying unusual patterns or outliers in data that do not conform to expected behaviour. A/B Testing: A statistical method for comparing two versions of a variable to determine which one performs better.

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The MLOps Blog

MAY 31, 2023

As an example for catalogue data, it’s important to check if the set of mandatory fields like product title, primary image, nutritional values, etc. are present in the data. So, we need to build a verification layer that runs based on a set of rules to verify and validate data before preparing it for model training.

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