What is Data Science, applications, tools used, challenges?

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What is Data Science, applications, tools used, challenges?

What is Data Science, applications, tools used, challenges?
What is Data Science?
1.  Data Science is a multi-disciplinary blend of computer science, mathematics, machine learning, statistics, programming, business strategies and problem solving techniques.
2.  It includes data exploration, analysis, insights, advance machine learning algorithms and data product engineering.
Why Data Science should be used?

1.  Better answers.
2.  Better decision making ability.
3.  Speeds up the performance.
4.  To know the Business analytics and trends.
Domains where data Science can be used

1.  Social Media
2.  Travel
3.  Marketing
4.  Sales
5.  Automation
6.  Health care
7.  Ecommerce
Tools / languages used in Data Science

1.  R
2.  Python
3.  MATLAB
4.  Weka
5.  SAS
6.  Tableau
7.  Excel
8.  Hadoop
9.  MySQL
Challenges in Data Science

1.  Multiple data sources.
2.  Different types of data; structured, unstructured, semi structured.
3.  Customers want personalization.
4.  Quality of data.
5.  Quantity of data.
How problems are solved in Data Science?

Various Algorithms are used such as:
1.  Classification Algorithm.
2.  Clustering Algorithm.
3.  Regression Algorithm.
4.  Anomaly Detection Algorithm.
5.  Reinforcement learning.

Learn : Difference between classification, clustering and regression.
Real life Applications of Data Science

1. Recommendation system of Amazon, Netflix, Spotify.
    2. Gmail spam filter.
    3.  Gaming.
    4.  Fraud detection.
    5. Internet search. 
    6. Automated cars.
    7.  Image/ speech recognition.
    8. Logistics.
    9. Digital Advertisements.
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