Pickl AI

APRIL 3, 2025

Key Processes and Techniques in Data Analysis Data Collection: Gathering raw data from various sources (databases, APIs, surveys, sensors, etc.). Exploratory Data Analysis (EDA): Using statistical summaries and initial visualisations (yes, visualisation plays a role within analysis!) EDA: Calculate overall churn rate.

Data Analysis 52

Data Analysis Data Analysis Data Visualization EDA 52

Becoming Human

MAY 15, 2023

One is a scripting language such as Python, and the other is a Query language like SQL (Structured Query Language) for SQL Databases. There is one Query language known as SQL (Structured Query Language), which works for a type of database. SQL Databases are MySQL , PostgreSQL , MariaDB , etc. Why do we need databases?

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Data Science Machine Learning Machine Learning Database 75

Pickl AI

MAY 30, 2024

Also Read: Explore data effortlessly with Python Libraries for (Partial) EDA: Unleashing the Power of Data Exploration. These include databases, APIs, web scraping, and public datasets. By checking patterns, distributions, and anomalies, EDA unveils insights crucial for informed decision-making.

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Data Analysis Data Analysis Data Science Exploratory Data Analysis 52

Pickl AI

NOVEMBER 4, 2024

Statistics Understand descriptive statistics (mean, median, mode) and inferential statistics (hypothesis testing, confidence intervals). Perform exploratory Data Analysis (EDA) using Pandas and visualise your findings with Matplotlib or Seaborn. These concepts help you analyse and interpret data effectively.

Data Science 52

Data Science Python Machine Learning Machine Learning 52

Pickl AI

JULY 25, 2023

They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Their primary responsibilities include: Data Collection and Preparation Data Scientists start by gathering relevant data from various sources, including databases, APIs, and online platforms. ETL Tools: Apache NiFi, Talend, etc.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Pickl AI

SEPTEMBER 12, 2024

Key Components of Data Science Data Science consists of several key components that work together to extract meaningful insights from data: Data Collection: This involves gathering relevant data from various sources, such as databases, APIs, and web scraping. Data Cleaning: Raw data often contains errors, inconsistencies, and missing values.

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Data Analyst Data Science Machine Learning Machine Learning 52

The MLOps Blog

MAY 31, 2023

So, a better database architecture would be to maintain multiple tables where one of the tables maintains the past 3 months history with session-level details, whereas other tables may contain weekly aggregated click, ATC, and order data. are captured and compared by formulating a hypothesis test to conclude with statistical significance.

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