Remove Exploratory Data Analysis Remove Hadoop Remove Hypothesis Testing
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The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Pickl AI

They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently.

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How To Learn Python For Data Science?

Pickl AI

Statistics Understand descriptive statistics (mean, median, mode) and inferential statistics (hypothesis testing, confidence intervals). These concepts help you analyse and interpret data effectively. Its flexibility allows you to produce high-quality graphs and charts, making it perfect for exploratory Data Analysis.

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Introduction to R Programming For Data Science

Pickl AI

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.

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Building ML Platform in Retail and eCommerce

The MLOps Blog

One might want to utilize an off-the-shelf ML Ops Platform to maintain different versions of data. To store Image data, Cloud storage like Amazon S3 and GCP buckets, Azure Blob Storage are some of the best options, whereas one might want to utilize Hadoop + Hive or BigQuery to store clickstream and other forms of text and tabular data.

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