Mlearning.ai

MAY 23, 2023

Blind 75 LeetCode Questions - LeetCode Discuss Data Manipulation and Analysis Proficiency in working with data is crucial. This includes skills in data cleaning, preprocessing, transformation, and exploratory data analysis (EDA). Familiarity with libraries like pandas, NumPy, and SQL for data handling is important.

Data Science 52

Data Science Data Scientist Apache Hadoop Machine Learning 52

Pickl AI

JULY 25, 2023

Proficient in programming languages like Python or R, data manipulation libraries like Pandas, and machine learning frameworks like TensorFlow and Scikit-learn, data scientists uncover patterns and trends through statistical analysis and data visualization. Data Visualization: Matplotlib, Seaborn, Tableau, etc.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Dataconomy

JULY 28, 2023

How to become a data scientist Data transformation also plays a crucial role in dealing with varying scales of features, enabling algorithms to treat each feature equally during analysis Noise reduction As part of data preprocessing, reducing noise is vital for enhancing data quality.

Power BI 91

Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

Mlearning.ai

FEBRUARY 28, 2023

These are common Python libraries used for data analysis and visualization. Exploratory Data Analysis (EDA) Univariate EDA Price: The price of a used car is the target variable and has a highly skewed distribution, with a median value of around 53.5 The price is higher for used cars with automatic transmission.

Python 52

Python EDA Exploratory Data Analysis Data Analysis 52

Analytics Vidhya

APRIL 11, 2023

Introduction Analytics Vidhya DataHour is designed to provide valuable insights and knowledge to individuals looking to build a career in the data-tech industry. These sessions cover a wide range of topics, from the fields of artificial intelligence, and machine learning, and various topics related to data science.

Artificial Intelligence 286

Artificial Intelligence Artificial Intelligence Machine Learning Machine Learning 286

Pickl AI

JULY 20, 2023

Descriptive Analytics Projects: These projects focus on summarizing historical data to gain insights into past trends and patterns. Examples include generating reports, dashboards, and data visualizations to understand business performance, customer behavior, or operational efficiency.

Analytics 40

Analytics Analytics Big Data Big Data 40

Data Science Blog

FEBRUARY 23, 2023

If you can analyze data with statistical knowledge or unsupervised machine learning, just extracting data without labeling would be enough. And sometimes ad hoc analysis with simple data visualization will help your decision makings. But only with limited labeled data, decision boundaries would be ambiguous.

Supervised Learning 264

Supervised Learning Machine Learning Machine Learning Deep Learning 264