Data Science
It is related to what I’m struggling to learn from Great Learning School
-
Statistics for Data Science – Continuous Random Variable
In data science, understanding statistics is essential for making informed decisions based on data. One of the fundamental concepts in statistics is the random variable, and when it comes to continuous data, continuous random variables play a crucial role in data analysis. What is a…
-
Statistics for Data Science – Random Variable
In the field of data science, understanding statistics is crucial for analyzing data and making informed decisions. One fundamental concept in statistics is the random variable. In this blog post, we will explore what random variables are, their types, and their significance in data analysis.…
-
Statistics for Data Science – Real World Problems
Statistics plays a crucial role in data science, enabling us to make sense of large datasets, derive insights, and make informed decisions. However, many data science problems require the application of statistics to real-world challenges. In this post, we will explore how statistical methods are…
-
Statistics for Data Science – Role of Distributions in Inferential Statistics
In the field of data science, statistics plays a critical role in making data-driven decisions. One of the foundational concepts in statistics is the understanding of distributions, which are essential in inferential statistics. In this blog post, we’ll explore the role of distributions in inferential…
-
Statistics for Data Science: Descriptive vs Inferential Statistics
Statistics is one of the foundational pillars of data science. Whether you’re analyzing data to generate insights or building machine learning models, a solid understanding of statistical methods is essential. In this post, we’ll explore two fundamental branches of statistics: Descriptive and Inferential statistics. What…
-
Statistics for Data Science – ANOVA Test
In data science, one common task is to compare multiple groups to determine if there are statistically significant differences between them. While a t-test is useful for comparing two groups, what if you have three or more? That’s where the ANOVA test comes in. What…
-
Statistics for Data Science: Test for Independence
Statistics for Data Science: Test for Independence In data science, understanding relationships between variables is crucial for drawing insights and making informed decisions. One of the most fundamental statistical tests used to evaluate these relationships is the Test for Independence. This test is especially important…
-
Statistics for Data Science: Test for Equality of Variances
In data science, one of the key tasks is understanding the underlying distributions of data before applying statistical models. Often, we need to compare multiple groups or datasets, and one critical aspect of comparison is assessing whether the variability within these datasets is similar. This…
-
Statistics for Data Science – Test for One Variance
In the world of data science, understanding variability is just as important as understanding central tendencies like the mean. Often, we want to know not only where our data is centered but also how spread out it is. This is where variance comes into play.…
-
Statistics for Data Science: Test for Two Proportions
In data science, we often need to compare proportions between two groups to determine if a significant difference exists. For instance, a marketing team might want to compare the click-through rates of two different email campaigns. In such cases, a test for two proportions is…