Pickl AI

MARCH 20, 2024

Proficiency in probability distributions, hypothesis testing, and statistical modelling enables Data Scientists to derive actionable insights from data with confidence and precision. Mastery of statistical concepts equips professionals to make informed decisions and draw accurate conclusions from empirical observations.

Data Science 52

Data Science Data Scientist Data Wrangling Machine Learning 52

Pickl AI

FEBRUARY 12, 2024

Here are some key areas often assessed: Programming Proficiency Candidates are often tested on their proficiency in languages such as Python, R, and SQL, with a focus on data manipulation, analysis, and visualization. Explain the concept of feature engineering in Maachine Learning.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Pickl AI

APRIL 3, 2025

Hypothesis Testing: Formally testing assumptions or theories about the data using statistical methods to determine if observed patterns are statistically significant or likely due to chance. Modeling: Build a logistic regression or decision tree model to predict the likelihood of a customer churning based on various factors.

Data Analysis 52

Data Analysis Data Analysis Data Visualization EDA 52

Pickl AI

APRIL 26, 2024

It covers essential topics such as SQL queries, data visualization, statistical analysis, machine learning concepts, and data manipulation techniques. Key Takeaways SQL Mastery: Understand SQL’s importance, join tables, and distinguish between SELECT and SELECT DISTINCT. How do you join tables in SQL?

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

Pickl AI

APRIL 28, 2023

As a part of the Data Science Course with Placement Guarantee , you will gain expertise in all these skill sets.

Data Scientist 52

Data Scientist Data Science Hypothesis Testing Decision Trees 52

Pickl AI

SEPTEMBER 12, 2024

Decision Trees: A supervised learning algorithm that creates a tree-like model of decisions and their possible consequences, used for both classification and regression tasks. Random Forest: An ensemble learning method that constructs multiple decision trees and merges them to improve accuracy and control overfitting.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Pickl AI

MAY 31, 2023

Begin by employing algorithms for supervised learning such as linear regression , logistic regression, decision trees, and support vector machines. Accordingly, you need to make sense of the data that you derive from the various sources for which knowledge in probability, hypothesis testing, regression analysis is important.

Data Science 52

Data Science Python Data Scientist Machine Learning 52