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How to Integrate Both Python & R into Data Science Workflows

Pickl AI

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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Roadmap to Learn Data Science for Beginners and Freshers in 2023

Becoming Human

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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The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Pickl AI

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. ETL Tools: Apache NiFi, Talend, etc.

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Top 50+ Data Analyst Interview Questions & Answers

Pickl AI

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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Building ML Platform in Retail and eCommerce

The MLOps Blog

There can be multiple sources of data at the same time, which can be available in different forms like image, text, and tabular form. One might want to utilize an off-the-shelf ML Ops Platform to maintain different versions of data. How to set up a data processing platform? are present in the data.

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