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Data Science Introduction


Data Science is a combination of multiple disciplines that uses statistics, data analysis, and machine learning to analyze data and to extract knowledge and insights from it.


What is Data Science?

Data Science is about data gathering, analysis and decision-making.

Data Science is about finding patterns in data, through analysis, and make future predictions.

By using Data Science, companies are able to make:

  • Better decisions (should we choose A or B)
  • Predictive analysis (what will happen next?)
  • Pattern discoveries (find pattern, or maybe hidden 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, and manufacturing.

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

How Does a Data Scientist Work?

A Data Scientist requires expertise in several backgrounds:

  • Machine Learning
  • Statistics
  • Programming (Python or R)
  • Mathematics
  • Databases

A Data Scientist must find patterns within the data. Before he/she can find the patterns, he/she must organize the data in a standard format.

Here is how a Data Scientist works:

  1. Ask the right questions - To understand the business problem.
  2. Explore and collect data - From database, web logs, customer feedback, etc.
  3. Extract the data - Transform the data to a standardized format.
  4. Clean the data - Remove erroneous values from the data.
  5. Find and replace missing values - Check for missing values and replace them with a suitable value (e.g. an average value).
  6. Normalize data - Scale the values in a practical range (e.g. 140 cm is smaller than 1,8 m. However, the number 140 is larger than 1,8. - so scaling is important).
  7. Analyze data, find patterns and make future predictions.
  8. Represent the result - Present the result with useful insights in a way the "company" can understand.

Where to Start?

In this tutorial, we will start by presenting what data is and how data can be analyzed.

You will learn how to use statistics and mathematical functions to make predictions.