In this slide presentation i have shown the basic application of statistics in computer science and engineering

1. APPLICATION OF STATISTICS IN CSE
Submitted to:
Atia Sanjida Talukder

2. What is Statistics?
Statistics is the science of collecting , organizing , analyzing , summarizing , interpreting numerical
data and making valid inferences or conclusion on the base of the data.
• A brunch of Mathematics.
• Some scholars pinpoint the origin of statistics to 1663.
• The modern field of statistics emerged in the late 19th and early 20.
• Gerolamo Cardano the earliest pioneer on the mathematics of probability(17th ).
• Karl Pearson a founder of mathematical statistics.
• We can predict about huge population by analyzing sample.

3. Applications of Statistics in Computer Science and Engineering
• Machine learning.
• Data mining(data management and data analysis).

4. Applications of Statistics in Machine learning
Machine learning is a subset of artificial intelligence in the field of computer science that
often uses statistical techniques to give computers the ability to "learn" with data, without
being explicitly programmed.

5. I. Machine learning and statistics are closely related fields.
II. The ideas of machine learning, from methodological principles to theoretical tools, have had
a long pre-history in statistics.
III. The term data science as a placeholder to call the overall field of Machine learning
IV. Two statistical modelling paradigms in machine learning : data model and algorithmic model
V. “Algorithmic model" means more or less the machine learning algorithms like Random
forest.
Machine learning’s Relation to statistics

6. Applications of Statistics in Data mining
Data mining is the process of discovering patterns in large data sets involving methods
at the intersection of machine learning, statistics, and database systems.

7. Data mining relation with Statistics
I. Both data mining and statistics are related
II. Both are all about discovering and identifying structures in data, with the aim of Turning
data to information
III. Although the aims of both these techniques overlap, they have different approaches.
IV. Statistics is only about quantifying data. While Data Mining uses tools to find relevant
properties of data, it is a lot like math.
V. Data mining, on the other hand, builds models to detect patterns and relationships in
data, particularly from large data bases.

8. THANK YOU