Algorithm
A set of rules that a computer program follows to perform a specific task.
Artificial Intelligence (AI)
A branch of computer science that deals with the creation of intelligent machines that can perform tasks without human intervention.
Big Data
A term used to describe extremely large datasets that cannot be managed or analyzed using traditional data processing methods.
Clustering
The process of grouping similar objects or data points together based on their characteristics.
Data Mining
The process of discovering patterns in large datasets using statistical and computational techniques.
Deep Learning
A type of machine learning that uses neural networks with multiple layers to model and solve complex problems.
Feature Engineering
The process of selecting and transforming raw data into meaningful features that can be used by machine learning algorithms.
Machine Learning
A subset of artificial intelligence that uses algorithms to learn from data and make predictions or decisions without being explicitly programmed.
Natural Language Processing (NLP)
The ability of a computer to understand, interpret and generate human language.
Overfitting
When a machine learning algorithm is too closely fitted to the training data and therefore performs poorly on new data.
Principal Component Analysis (PCA)
A statistical technique used to reduce the dimensionality of large datasets while retaining as much of the original information as possible.
Regression Analysis
A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
Supervised Learning
A type of machine learning where the algorithm is trained on labeled data to make predictions or decisions about new data.
Unsupervised Learning
A type of machine learning where the algorithm is trained on unlabeled data to identify patterns or structures in the data.
Validation
The process of testing the accuracy and performance of a machine learning algorithm using a separate dataset from the one used for training.
What are the 5 E’s of big data?
The 5 E’s of big data are:
- Volume: refers to the vast amount of data that is generated daily
- Velocity: the speed at which data is generated and processed
- Variety: the different forms of data such as structured, unstructured, and semi-structured
- Veracity: the accuracy and reliability of the data
- Value: the usefulness and insights that can be extracted from the data
What are the 5 A’s of big data?
The 5 A’s of big data are:
- Analytics: using statistical and machine learning techniques to extract insights from data
- Algorithms: the set of instructions used to perform specific tasks on data
- Applications: software programs or tools used to analyze and process big data
- Architecture: the infrastructure used to store, process and manage big data
- Attitude: the mindset needed to embrace the potential of big data and data-driven decision-making
What are the seven stages of data science?
The seven stages of data science are:
- Problem definition
- Data collection
- Data preparation
- Data exploration
- Data modeling
- Model evaluation
- Deployment
What is the data science life cycle?
The data science life cycle is a process that involves various stages to extract insights from data. It includes:
- Business understanding
- Data acquisition
- Data preparation
- Data exploration
- Data modeling
- Model evaluation
- Deployment
What are the 5 stages of data lifecycle?
The 5 stages of data lifecycle are:
- Data creation
- Data processing
- Data storage
- Data analysis
- Data archiving
What are the 9 stages of data processing?
The 9 stages of data processing are:
- Data ingestion: collecting and importing data from various sources
- Data validation: checking the data for accuracy and completeness
- Data cleaning: removing or correcting errors, inconsistencies, and duplicates
- Data transformation: converting the data into a standard format for analysis
- Data integration: combining data from different sources to create a unified dataset
- Data aggregation: summarizing or grouping data based on certain criteria
- Data analysis: using statistical or machine learning techniques to extract insights from data
- Data visualization: creating graphical representations of data to aid in understanding and communication
- Data storage: storing the processed data for future use.
Faqs
What are the terminologies of data science?
Data science is a multidisciplinary field that combines various domains such as statistics, mathematics, computer science, and domain expertise. Some of the terminologies used in data science are:
Data Mining: The process of discovering hidden patterns and information from large datasets.
Machine Learning: A branch of artificial intelligence that allows machines to learn from data and make predictions or decisions.
Predictive Modeling: Using statistical algorithms to make predictions about future events based on historical data.
Data Visualization: The graphical representation of data to help humans better understand the patterns and insights in the data.
Big Data: A term used to describe extremely large and complex datasets that cannot be processed using traditional data processing techniques.
Artificial Intelligence: A field of computer science that focuses on creating intelligent machines that can perform tasks that typically require human intelligence.
What are the 4 types of data science?
Data science can be broadly classified into four types:
Descriptive Analytics: This involves analyzing historical data to gain insights and understand what has happened in the past.
Diagnostic Analytics: This involves analyzing data to identify the cause and effect of a particular event or problem.
Predictive Analytics: This involves using statistical models and machine learning algorithms to make predictions about future events or outcomes.
Prescriptive Analytics: This involves using data and algorithms to recommend a course of action that will optimize a particular outcome.
What are the 4 pillars of data science?
The 4 Pillars of Data Science refer to the four key areas of expertise required for successful data science projects:
Domain Expertise: A deep understanding of the domain or industry in which the data science project is being undertaken.
Mathematics and Statistics: The ability to use mathematical and statistical techniques to analyze and interpret data.
Computer Science: The ability to use programming languages and tools to manipulate and analyze large datasets.
Communication and Visualization: The ability to communicate insights and results to stakeholders in a clear and concise manner using data visualization techniques.
What are the 5 ps of Data Science
The 5 P’s of Data Science refer to the five stages involved in the data science process:
Problem Definition: The first step in any data science project is defining the problem that needs to be solved or the question that needs to be answered.
Data Preparation: The next step is to collect, clean, and prepare the data for analysis. This involves removing missing values, handling outliers, and transforming the data into a format suitable for analysis.
Exploratory Data Analysis: Once the data is prepared, the next step is to explore the data to gain insights and identify patterns.
Modeling: After gaining insights from the data, the next step is to build a predictive model that can make accurate predictions about future events or outcomes.
Deployment: The final step is to deploy the model and integrate it into a business process or product.