Data Collection and Processing

Data types and structures: Understanding the different types of data and how they are organized, including numeric, categorical, and textual data.

Data cleaning and pre-processing: Techniques for cleaning and preparing data before analysis, such as dealing with missing values, outliers, and data formatting.

Exploratory data analysis: Techniques for visualizing and summarizing data to discover trends, patterns, and relationships.

Statistical inference: Using statistical methods to make inferences about populations based on samples of data.

Data visualization: Techniques for creating visual representations of data to aid in understanding and presentation.

Machine learning: Understanding the principles and applications of machine learning algorithms for data analysis, such as regression analysis, clustering, decision trees, and neural networks.

Data mining: Techniques for discovering patterns and relationships in large datasets using statistical and machine learning methods.

Big data analysis: Techniques for analyzing large volumes of data that cannot be processed on a single computer.

Data warehousing: Understanding the principles of storing and managing data in a structured manner to support analysis and reporting.

Data governance and ethics: Understanding the importance of data privacy, security, and ethical implications of data collection, processing, and analysis.

Surveys: Surveys are used to collect data from participants through questionnaires or interviews, and it can be either quantitative or qualitative data.

Observational Studies: This is the recording and documenting of natural events, behaviors or experiences without manipulation or interference.

Experiments: Experiments involve the manipulation of variables to test the impact on outcomes.

Case studies: This is the in-depth examination of a particular case or scenario to draw general conclusions.

Secondary Data Analysis: The use of already existing and published data for research purposes.

Text Analysis: This data collection method involves the use of machine learning or NLP techniques to extract meaningful data from text sources.

Web Scraping: Gathering large amounts of data from websites in an automated manner using programming.

Social Media Analysis: This data collection method involves the collection of data from social media platforms for analysis.

Sensor Data Analysis: This is the analysis of data collected from IoT sensors, such as temperature gauges, heart rate monitors, and more.

Time Series Analysis: This data collection method focuses on analyzing data collected over a period to observe the tick of changes and patterns.

Machine Learning: Machine learning involves the application of statistical analysis to develop models that make predictions, classifications or decisions.

Data Visualization: The use of graphical tools to present and analyze large datasets visually.

Data Mining: The process of discovering patterns in large datasets using algorithms.

Data Cleaning: To extract useful information from data, it is necessary to clean and preprocess the data by removing duplicates, correcting errors, and filling missing values.