Data Science Dojo

JANUARY 22, 2023

In this blog, we will discuss exploratory data analysis, also known as EDA, and why it is important. We will also be sharing code snippets so you can try out different analysis techniques yourself. EDA is an iterative process of conglomerative activities which include data cleaning, manipulation and visualization.

Exploratory Data Analysis 370

Exploratory Data Analysis EDA Data Analysis Data Analysis 370

Pickl AI

NOVEMBER 4, 2024

This article will guide you through effective strategies to learn Python for Data Science, covering essential resources, libraries, and practical applications to kickstart your journey in this thriving field. Key Takeaways Python’s simplicity makes it ideal for Data Analysis. in 2022, according to the PYPL Index.

Data Science 52

Data Science Python Machine Learning Machine Learning 52

Pickl AI

DECEMBER 10, 2024

Summary: Discover the best Data Science books for beginners that simplify Python, statistics, and Machine Learning concepts. Paired with structured learning plans and online communities, they help build foundational skills and confidence for a successful journey into Data Science.

Data Science 52

Data Science Python Machine Learning Machine Learning 52

Towards AI

MARCH 17, 2023

Speaking mathematically [Image credits: All of statistics by Larry Wasserman book ] Where are we currently using CLT? One of the most important applications is hypothesis testing. [I I am going to write a separate blog on hypothesis testing, but till then, you can refer attached link.].

Hypothesis Testing 98

Hypothesis Testing Data Mining Data Mining Data Mining 98

Pickl AI

MAY 16, 2024

Descriptive statistics summarize your data (averages, spreads), while inferential statistics use samples to draw conclusions about larger populations. Descriptive statistics paint a picture of your data, while inferential statistics make predictions based on that picture. Through statistical tests (e.g.,

Data Science 52

Data Science Hypothesis Testing Data Analysis Data Analysis 52

Pickl AI

JUNE 9, 2024

Mathematical Foundations Concepts like probability and regression analysis are essential tools in Data Science, illustrating how mathematical principles underpin critical methodologies used in the field. Statistics Statistics is the backbone of Data Science, providing essential Data Analysis and interpretation techniques.

Data Science 52

Data Science Hypothesis Testing Data Scientist Machine Learning 52

Dataconomy

MAY 2, 2023

Statistics In the field of machine learning, tools and tables play a critical role in creating models from data. Additionally, statistics and its various branches, including analysis of variance and hypothesis testing, are fundamental in building effective algorithms. R is especially popular in academia and research.

ML 110

ML ML Machine Learning Machine Learning 110