statistics,
data analysis,
and machine learning
in order to analyze data and extract
knowledge and insights from it.

Examples of where Data Science is needed:

For route planning: To discover the best routes to ship

To foresee delays for flight/ship/train etc.
(through predictive analysis)

To create promotional offers

To find the best suited time to deliver goods

To forecast the next years revenue for a company

To analyze health benefit of training

To predict who will win elections


Data Science can be applied in nearly
every part of a business
where data is available.

Examples are:

Consumer goods
Stock markets
Industry
Politics
Logistic companies
E-commerce

What is Data Science?

Data Science is simply about data gathering,
analysis and decision-making.
Data Science is absolutely about finding patterns in data,
through analysis, and using
those patterns to make future predictions.

companies use Data Science to make:
Better decisions ( choose A or B or C)
Predictive analysis (what next?)
Pattern discoveries (find pattern, or maybe hidden
or unseen information in the data)

Where is Data Science Needed?

Data Science is used in many industries in the world today,
e.g. banking, consultancy, healthcare, manufacturing,
Agriculture, Weather reports, etc.

How Does a Data Scientist Work?

A Data Scientist requires expertise in:
Machine Learning
Statistics
Programming (Python or R)
Mathematics
Databases

how a Data Scientist works:

Ask the right questions -

To understand the business problem.
Explore and collect data -

From database, web logs, customer feedback, etc.
Extract the data -

Transform the data to a standardized format.
Clean the data -
Remove erroneous values from the data.
Find and replace missing values -
Check for missing values and replace
them with a suitable value (e.g. an average value).
Normalize data -

Scale the values in a practical range- so scaling is important).
Analyze data,

find patterns and make future predictions.
Represent the result -
Present the result with useful insights in a way
the "company" can understand.

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Getting Start

In this tutorial, we will cover the following Topics:(below)

how data can be analyzed.
we will learn how to use statistics and
mathematical functions to make predictions.

DS- Data Science Introduction

Topics you need to know
on road to Data Science:
Keep Learning...

-- Data Science - What is Data?

-- Database Table

-- Data Science & Python

-- Data Science - Python DataFrame

-- Data Science Functions

-- Data Science - Data Preparation

Data Science Math -

- Data Science - Linear Functions

- DS - Plotting Linear Functions

- DS - Slope and Intercept

- Data Science Statistics -

- Data Science - Intro to Statistics

- Data Science - Percentiles


- DS - Statistics Standard Deviation

- DS - Statistics Variance

- Data Science - Statistics Correlation

- Data Science - Statistics Correlation Matrix

- DS - Statistics Correlation vs. Causality

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    Coming Soon:

DS Advanced

DS Linear Regression

DS Regression Table

DS Regression Info

DS Regression Coefficients

DS Regression P-Value

DS Regression R-Squared

DS Linear Regression Case

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programming with C++

Data Science

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