News / Blog: #analysis

What is Natural Language Processing (NLP)?

Natural Language Processing (NLP) is a subfield of Artificial Intelligence that deals with natural language processing. It encompasses a range of techniques that attempt to translate human language into machine-readable formats and vice versa. Examples include automatic text analysis, machine translation, dialog systems, and text classification.

What are data analysis methods?

Data analysis methods are techniques used to examine and analyze data to identify trends, patterns, and other useful information. Some of the most common data analysis methods are regression analysis, cluster analysis, descriptive statistics, exploratory data analysis, machine learning, hypothesis testing, and causal analysis.

What are data science practices?

1. Data ingestion and analysis: data ingestion is the process of collecting, processing, and analyzing data from various sources to gain useful insights.

2. Data visualization: this is the process of presenting data in visual formats such as charts, graphs, and maps to identify trends and gain insights.

3. Machine learning: machine learning is a branch of artificial intelligence that enables computers to learn from experience without being explicitly programmed.

4. Predictive analytics: predictive analytics is a process of using data to predict possible future events and make decisions based on them.

5. Deep learning: deep learning is a subfield of machine learning in which so-called neural networks are used to solve complex problems.

What is Data Wrangling?

Data wrangling is the process of converting or mapping data from one source to another so that it can be used for analysis or other purposes. It may involve cleaning, adapting, analyzing, transforming, and modifying data so that it can be converted to another format suitable for the application purpose.

What is Predictive Analysis?

Predictive analysis is a process used to predict or forecast future events by using data from the past and current state. It is used to make better decisions by identifying similar trends and developments. It helps companies better understand risk by predicting potential outcomes. It also enables companies to better predict customer behavior and increase sales.