What is Data Science? Data science is an interdisciplinary field focused on obtaining knowledge and insights from data sets. Mainly it applies to big data. It includes preparing data for analysis, analyzing, and presenting findings. The whole process support making decisions in organizations. Disciplines involved in this cycle are computer science, statistic, mathematics, information visualization, graphic design, complex systems, business, and communication.

1. When it applies

Big data is a crucial tool for businesses. The availability and interpretation of big data have altered the business models of old industries and enabled new ones. Data scientists are responsible for analyzing big data, supplying usable information, and creating software and algorithms that help companies and organizations determine optimal business decisions.

2. Use case

It is used, among others, when defining customers’ purchasing preferences, offering the most attractive purchase price for a given product, a recommendation system, an advertising message appropriately selected for users in social media. Data Science uses practically everywhere, from grocery stores, mobile operators to insurance companies and banks.

3. Algorithms

  • Classification
  • Regression
  • Clustering
  • Dimensionality Reduction

4. Open-source applications

  • TensorFlow
  • Pytorch
  • Jupyter Notebook
  • Apache Hadoop

5. Fields

Computer science, statistic, mathematics, information visualization, graphic design, complex systems, business, and communication

6. Languages

  • Python
  • R
  • Julia
  • SQL
  • Java
  • C++

7. Types

  • Scientific Method
  • Advanced Computing
  • Big Data
  • Statistics & Probability
  • Data Engeneering
  • Data Visualization
  • Development
  • Exploratory Data Analysis
  • Machine Learning & Advanced Algorithmes

7. Tools

  • Apache Spark
  • MATLAB
  • Excel
  • Tableau
  • Jupyter
  • Matplotlib
  • Scikit- learn
  • Tensorflow
  • D3.js

8. Lifecycle

  1. Business Understanding
  2. Data Gathering
  3. Data Preparation
  4. Exploratory Data Analysis
  5. Model Planning
  6. Model Building
  7. Evaluation and Deployment
  8. Data Visualization
  9. Communicate Results

The lifecycle of data science:

lifecycle of data science


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