DATA Science

John Barku — Sun, 04 Aug 2024 23:58:06 +0000

Data Science

What is Data Science?
Data Science is an interdisciplinary field focused on extracting knowledge, manipulating and analyzing data, and using data to answer questions or make recommendations.
A data scientist is a professional who creates programming code and combines it with statistical knowledge to develop insights from data.

Categories of Data Science;
Data Management: Collecting, persisting, and retrieving data securely, efficiently, and cost-effectively
Data Integration and Transformation: Extract, transform, and load data (ETL). Some of the data is distributed in multiple repositories such as databases

Data Visualization: Graphical representation of data and information in charts, plots, maps, and animations. It conveys data more effectively.

Model Building: You train the data and analyze patterns using suitable machine-learning algorithms

Model Deployment: Integrate a model into a production environment. Here the machine learning model is made available to third-party apps via APIs, helping them make data-based decisions.

Model Monitoring and Assessment: Tracks deployed models and model assessment checks for accuracy, fairness, and robustness monitoring

Open Source Tools for Data Science
Data management:

Relational Databases;
a. MySQL
b. PostgreSQL
NoSQL Databases;
a. MongoDB
b. Apache CouchDB
c. Apache Cassandra
File Based Tools;
a. Hadoop File System
b. Cloud File System

Data Integration and Transformation:

Apache Airflow
KubeFlow
Apache SparkSQL

Data Visualization:

Pixie Dust
Hue
Kibana
Apache Superset

Model Deployment:

Apache Prediction IO
Seldon
Mleap
TensorFlow

Model Monitoring and Assessment:

Model DB
Prometheus

Libraries for Data Science
Scientific Computing Libraries;
• Pandas
• Numpy

Visualization Libraries;
• Matplotlib
• Seaborn

Machine Learning and Deep Learning Libraries;
• Scikit-Learn
• Keras
• TensorFlow
• Pytorch

DEV Community: John Barku

DATA Science

Data Science