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Introduction to Data Science

One of the reasons that data science is one of the most interesting fields to go into is that it doesn't limit you to a specific industry. Interested in airplanes? Work for a company on ticket pricing! Like fashion? Create a model that predicts the latest trends. 

*Domain Expertise* refers to the particular set of knowledge that someone cultivates in order to understand their data. You may be able to crunch the numbers, but in order to understand their meaning, you're going to need a lot of context. Sometimes this context comes from your research, your teammates, or the knowledge that you gain after working in a particular industry.

So take the time to think about where your passions lie (outside of data science, of course). Maybe you're an avid gardener. How can data science help you learn more about your passion for orchids? Or perhaps you're an all-star baseball player. Could you improve your stats if you crunch the numbers (remember that movie, Moneyball?).

When you are passionate about a particular field, your data science skills will make you extremely valuable in that field.

Domain Expertise

You've already seen how statistics and probability help us understand our data. But how do we actually interact with datasets? This is where programming comes in. 

With the right instructions, we can use the processing power of computers to analyze and visualize millions of data points. Once these instructions are written, we can then run them at any time, allowing us to replicate, update, and automate our analyses as we receive new data.

A set of instructions like this is called a **program** or an **algorithm**, and is written in a computer programming language, which we usually refer to as **code** for short. In future Codecademy courses, you will learn all the details of writing code to analyze data. For now, let's go over a common example of how computer programming can help us understand a dataset and begin to make predictions! The dataset we'll be looking at is a collection of flipper and bill measurements for three different species of penguins [collected by Dr. Kristen Gorman and the Palmer Station in Antarctica](https://allisonhorst.github.io/palmerpenguins/).

When you opened this exercise, some code running behind the scenes loaded this data and created a visualization of the flipper and bill measurements for three penguin species. Take a look at the visualization. You might notice that there isn't much overlap between species on the plot. For example, Chinstrap penguins seem to usually have longer bills than Adelie penguins, and so Chinstrap penguins appear above Adelie penguins in the visualization. Regions like these that predominantly feature one species over another are called **clusters** in data science, and are often used when building computer models to make predictions about data. 

For example, if we find a new penguin that has 180mm long flippers and a 35mm long bill, we might conclude (based on these clusters) that our penguin is more likely to be an Adelie penguin than either of the other two species. In our code running behind the scenes, we've built a computer model to do this kind of prediction automatically.

![Pictures of the three penguin species](https://static-assets.codecademy.com/Paths/data-science-career-path/lter_penguins.png)

Programming

A fundamental part of data science is *statistics*.

Some people say that data science is statistics dressed up for the 21st century - and there's some truth in that. Statistics has been practiced for centuries but with the advent of computing prowess in the middle of the 20th century, statistics has taken on a new form.

As a refresher, statistics is the practice of applying mathematical calculations to sets of data to derive meaning. Statistics can give us a quick summary of a dataset, such as the average amount or how consistent a dataset is.

There are two types of statistics: *descriptive statistics* and *inferential statistics*. Descriptive statistics describe a dataset using mathematically calculated values, such as the mean and standard deviation. For instance, the graph below from [FiveThirtyEight](https://fivethirtyeight.com/features/heres-what-the-gender-pay-gap-looks-like-by-income-level/) charts the wage gap between American men and women in 2014. An example of a descriptive statistic would be that at the 90th income percentile, women make 80.6% of what men make on average.

![alt text](https://content.codecademy.com/programs/code-foundations-path/ds-survey/lesson%201/538genderpaygap.png)

These values are useful when summarizing data collections. 

On the other hand, inferential statistics are statistical calculations that enable us to draw conclusions about the larger population. For instance,  from looking at the graph we can infer that at the 99th income percentile, women make less than 78% of what men make on average. We can also infer that the reason why the wage gap is smallest at the 10th income percentile is because the minimum wage for men and women is  the same.

Statistics

Venn-diagram describing the relationship between mathematics, domain expertise, and computer science to inform data science.

Review! You now know the basic parts that make up the field of data science.
* **Data Science**&mdash;the field of taking data and transforming it into meaningful information that can help us make decisions
* **Descriptive Statistics**&mdash;statistics that describe the data in objective terms
* **Inferential Statistics**&mdash;inferences for the overall population based on data
* **Probability**&mdash;the likelihood that an event will happen
* **Programming**&mdash;the act of giving the computer instructions to perform a task
* **Domain Expertise**&mdash;the particular set of knowledge that someone cultivates and brings with them in order to understand their data

Data science is in every field. Take a look at these cool <a href="http://flowingdata.com/2017/12/28/10-best-data-visualization-projects-of-2017/" target="_blank">data science projects</a> that range from politics to space travel.

In our next lesson, we will go over how the different aspects of data science make up the *data science process*. These building blocks will give you an overview of how exactly data science practitioners collect and use data.


Review

Our world contains massive amounts of data. In fact, more data exists in the world from the last ten years than in the last two thousand. But what does that mean and why should we care? 

Data is the building blocks for information. And information can carry meaning - from a click telling us what someone likes, to toxins in the water signaling a health concern. But data (as a collection of records) is meaningless unless we *do* something with it. That's where data science comes in. 

*Data science* enables us to take data and transform it into meaningful information that can help us make decisions. Data science is interdisciplinary and combines other well-known fields such as probability, statistics, analytics, and computer science. The work that you might do could range from writing up reports to building machine learning models. No matter what your interests are, data science is applicable - because these days, we have data on everything! 

In this lesson, we'll introduce you to building blocks of data science so you can get a sense of the different skills you will need to pursue a career in the field. 

Venn diagram with 3 circles.
The main categories are Domain Expertise, Mathematics, and Computer Science.
The intersection of Domain Expertise and Mathematics is Statistical Research. 
The intersection of Domain Expertise and Computer Science is Data Processing.
The intersection of Mathematics and Computer Science is Machine Learning. 
The intersection Domain Expertise, Mathematics, and Computer Science is Data Science.

Introduction

Another field that informs the field of data science is *probability*. 

Probability is the mathematical study of what could potentially happen. Fun fact: the study of probability came about as a method to decode secret messages. 

In data science, probability calculations are used to build models. Models are able to help us understand data that has yet to exist - either data we hadn't previously collected or data that has yet to be created. Data scientists create models to help calculate the probability of a certain action and then they use that probability to make informed decisions. 

For instance, the social networking company Facebook collects data on the likes and dislikes of its users. Data scientists use that data to create models to calculate the probability of a user liking a certain advertisement. So, if you like several facebook posts about football, then a model may calculate a high probability of you positively responding to an advertisement selling football tickets at a stadium near you.

Calculating the probability of an event is sometimes dependent on other factors. For instance, in the birthday problem. "What is the probability that two people in a room have the same birthday?" the probability is dependent on the number of people in the room. 

Other times, the probability of something is constant. For instance, the probability of flipping a coin and it landing heads will always be 50%.