Pickl AI

OCTOBER 14, 2024

Summary: This article explores different types of Data Analysis, including descriptive, exploratory, inferential, predictive, diagnostic, and prescriptive analysis. Introduction Data Analysis transforms raw data into valuable insights that drive informed decisions. What is Data Analysis?

Data Analysis 52

Data Analysis Data Analysis EDA Data Mining 52

Pickl AI

AUGUST 27, 2024

Summary: Python simplicity, extensive libraries like Pandas and Scikit-learn, and strong community support make it a powerhouse in Data Analysis. It excels in data cleaning, visualisation, statistical analysis, and Machine Learning, making it a must-know tool for Data Analysts and scientists. Why Python?

Data Analysis 52

Data Analysis Data Analysis Python Data Analyst 52

Data Science Dojo

AUGUST 11, 2023

Machine learning is a field of computer science that uses statistical techniques to build models from data. These models can be used to predict future outcomes or to classify data into different categories. It provides a fast and efficient way to manipulate data arrays. Pandas is a library for data analysis.

Data Science 195

Data Science Python Data Scientist Decision Trees 195

Pickl AI

NOVEMBER 5, 2024

This article explores the definitions of Data Science and AI, their current applications, how they are shaping the future, challenges they present, future trends, and the skills required for careers in these fields. Key Takeaways Data-driven decisions enhance efficiency across various industries.

Data Science 52

Data Science Artificial Intelligence Artificial Intelligence AI 52

Pickl AI

OCTOBER 15, 2024

Summary: Statistical Modeling is essential for Data Analysis, helping organisations predict outcomes and understand relationships between variables. Introduction Statistical Modeling is crucial for analysing data, identifying patterns, and making informed decisions.

Decision Trees 52

Decision Trees Hypothesis Testing Clustering Data Analysis 52

Women in Big Data

APRIL 17, 2024

Learning Objectives Recap: Paradigms in Data Science: We explored the two main paradigms in data science: descriptive analytics (understanding what happened in the past) and predictive analytics (using models to forecast future outcomes).

Exploratory Data Analysis 52

Exploratory Data Analysis EDA Machine Learning Machine Learning 52

Pickl AI

JULY 23, 2023

Using the right data analytics techniques can help in extracting meaningful insight, and using the same to formulate strategies. The analytics techniques like descriptive analytics, predictive analytics, diagnostic analytics and others find application in diverse industries, including retail, healthcare, finance, and marketing.

Analytics 52

Analytics Analytics Predictive Analytics Data Analysis 52