Pickl AI

SEPTEMBER 4, 2023

Clean and preprocess data to ensure its quality and reliability. Statistical Analysis: Apply statistical techniques to analyse data, including descriptive statistics, hypothesis testing, regression analysis, and machine learning algorithms.

Data Analysis 52

Data Analysis Data Analysis Hypothesis Testing Machine Learning 52

Pickl AI

APRIL 26, 2024

Top 50+ Interview Questions for Data Analysts Technical Questions SQL Queries What is SQL, and why is it necessary for data analysis? SQL stands for Structured Query Language, essential for querying and manipulating data stored in relational databases. How would you segment customers based on their purchasing behaviour?

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

ODSC - Open Data Science

DECEMBER 7, 2023

You’ll take a deep dive into DataGPT’s technology stack, detailing its methodology for efficient data processing and its measures to ensure accuracy and consistency. You’ll cover the integration of LLMs with advanced algorithms in DataGPT, with an emphasis on their collaborative roles in data analysis.

Data Wrangling 98

Data Wrangling Data Science Machine Learning Machine Learning 98

Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Role of Data Scientists Data Scientists are the architects of data analysis.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 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

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. However, there are a few fundamental principles that remain the same throughout.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Pickl AI

APRIL 19, 2024

Statistics: Statistical methods are vital in AI, particularly in data analysis and machine learning. Gain proficiency in probability theory, hypothesis testing , regression analysis , and statistical inference. Proficiency in programming languages like Python is crucial for implementation.

Natural Language Processing 52

Natural Language Processing AI AI 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. It is divided into two broad categories, technical skills and non-technical skills.

Data Scientist 52

Data Scientist Data Science Hypothesis Testing Decision Trees 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. Python is a High-level, Procedural, and object-oriented language; it is also a vast language itself, and covering the whole of Python is one the worst mistakes we can make in the data science journey.

Data Science 75

Data Science Machine Learning Machine Learning Database 75

Pickl AI

JULY 28, 2023

A well-organized portfolio demonstrates your ability to work with data and draw valuable insights. Here are the steps to build an impressive data analyst portfolio: Select Relevant Projects: Choose a variety of data analysis projects that highlight your skills and cover different aspects of data analysis.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Data Visualization 52

Mlearning.ai

MAY 23, 2023

The following Venn diagram depicts the difference between data science and data analytics clearly: 3. Data analysis can not be done on a whole volume of data at a time especially when it involves larger datasets. What is the p-value and what does it indicate in the Null Hypothesis?

Data Science 52

Data Science Decision Trees Deep Learning Deep Learning 52

Pickl AI

NOVEMBER 6, 2023

Communication and Storytelling: Data Visualization is an effective way to communicate complex data and findings to both technical and non-technical audiences. Visual representations make it easier to convey information, present key findings, and tell compelling stories derived from data. Does data visualization require coding?

Data Visualization 52

Data Visualization Data Science Data Scientist Data Analysis 52

The MLOps Blog

MAY 31, 2023

As an example for catalogue data, it’s important to check if the set of mandatory fields like product title, primary image, nutritional values, etc. are present in the data. So, we need to build a verification layer that runs based on a set of rules to verify and validate data before preparing it for model training.

ML 59

ML ML Algorithm Machine Learning 59