Pickl AI

MAY 30, 2024

Overview of Typical Tasks and Responsibilities in Data Science As a Data Scientist, your daily tasks and responsibilities will encompass many activities. You will collect and clean data from multiple sources, ensuring it is suitable for analysis. Sources of Data Data can come from multiple sources.

Data Analysis 52

Data Analysis Data Analysis Data Science Exploratory Data Analysis 52

Pickl AI

APRIL 3, 2025

Objective Evaluation: Allows for the assessment of performance, the effectiveness of interventions, or the testing of hypotheses. Key Processes and Techniques in Data Analysis Data Collection: Gathering raw data from various sources (databases, APIs, surveys, sensors, etc.). EDA: Calculate overall churn rate.

Data Analysis 52

Data Analysis Data Analysis Data Visualization EDA 52

Pickl AI

MAY 29, 2024

SQL is indispensable for database management and querying. Skills in data manipulation and cleaning are necessary to prepare data for analysis. Data Scientists frequently use tools like pandas in Python and dplyr in R to transform and clean data sets, ensuring accuracy in subsequent analyses.

Data Scientist 52

Data Scientist Data Science Machine Learning Machine Learning 52

Pickl AI

SEPTEMBER 12, 2024

Key Components of Data Science Data Science consists of several key components that work together to extract meaningful insights from data: Data Collection: This involves gathering relevant data from various sources, such as databases, APIs, and web scraping.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Mlearning.ai

MAY 23, 2023

The following figure represents the life cycle of data science. It starts with gathering the business requirements and relevant data. Once the data is acquired, it is maintained by performing data cleaning, data warehousing, data staging, and data architecture. Why is data cleaning crucial?

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52