DATA SCIENCE USING PYTHON
ONLINE TRAINING COURSE
The Data Science Lifecycle
Data Acquisition
Data Entry
Signal Reception
Data Extration
CAPTURE
This is the collection of raw structured and unstructured data from all relevant sources using a variety of methods, ranging from manual input and web scraping to real-time data capture from systems and devices.
Data Cleansing
Signal Staging
Data Processing
Data Architecture
MAINTAIN
This can encompass everything from data cleansing, reduplication, and reformatting to combining data into a data warehouse, data lake, or other unified storage for analysis utilizing ETL (extract, transform, load) or other data integration tools.
Data Clustering
Data Classification
Data Modeling
Data Summarization
PROCESS
Data scientists look for biases, trends, ranges, and distributions of values in the data to see if it's suitable for predictive analytics.
Predictive Analysis
Regression
Qualatative Analysis
Text Mining
ANALYZE
data scientists use statistical analysis, predictive analytics, regression, algorithms, and other techniques to extract insights from the data.
Data Reporting
Data Visualization
Decision Making
Business Intelligence
COMMUNICATE
Data scientists can utilize specific visualization tools or use data science programming languages such as R or Python, which have components for producing visuals. Finally, the insights are presented as reports, charts, and other data visualizations.