### Statistical Tools

There are a number of statistics data professionals use to reason and communicate information about their data. These are some of the most basic and vital statistical tools to help you get started.

**Correlation**

Correlation is the measure of how much one set of values depends on another. If values increase together, they are positively correlated. If one values from one set increase as the other decreases, they are negatively correlated. There is no correlation when a change in one set has nothing to do with a change in the other.

**Mean (Average, Expected Value)**

A calculation that gives us a sense of a “typical” value for a group of numbers. The mean is the sum of a list of values divided by the number of values in that list. It can be deceiving used on its own, and in practice we use the mean with other statistical values to gain intuition about our data.

**Median**

In a set of values listed in order, the median is whatever value is in the middle. We often use the median along with the mean to judge if there are values that are unusually high or low in the set. This is an early hint to explore outliers.

**Normalize**

A set of data is said to be normalized when all of the values have been adjusted to fall within a common range. We normalize data sets to make comparisons easier and more meaningful. For instance, taking movie ratings from a bunch of different websites and adjusting them so they all fall on a scale of 0 to 100.

**Outlier**

An outlier is a data point that is considered extremely far from other points. They are generally the result of exceptional cases or errors in measurement, and should always be investigated early in a data analysis workflow.

**Sample**

The sample is the collection of data points we have access to. We use the sample to make inferences about a larger population. For instance, a political poll takes a sample of 1,000 Greek citizens to infer the opinions of all of Greece.

**Standard Deviation**

The standard deviation of a set of values helps us understand how spread out those values are. This statistic is more useful than the variance because it’s expressed in the same units as the values themselves. Mathematically, the standard deviation is the square root of the variance of a set. It’s often represented by the greek symbol sigma, σ.

**Statistical Significance**

A result is stasticially significant when we judge that it probably didn’t happen due to chance. It is highly used in surveys and statistical studies, though not always an indication of pratical value. The mathematical details of statistical significance are beyond the scope of this post, but a fuller explanation can be found here.

**Summary Statistics**

Summary statistics are the measures we use to communicate insights about our data in a simple way. Examples of summary statistics are the mean, median and standard deviation.

**Time Series**

A time series is a set of data that’s ordered by when each data point ocurred. Think of stock market prices over the course of a month, or the temperature throughout a day.

**Residual (Error)**

The residual is a measure of how much a real value differs from some stastical value we calculated based on the set of data. So given a prediction that it will be 20 degrees fahrenheit at noon tomorrow, when noon hits and its only 18 degrees, we have an error of 2 degrees. This is often used interchangably with the term “error,” even though, technically, error is a purely theoretical value.

**Variance**

The variance of a set of values measures how spread out those values are. Mathematically, it is the average difference between individual values and the mean for the set of values. The square root of the variance for a set gives us the standard deviation, which is more intuitively useful.

### Parts of a Workflow

While every workflow is different, these are some of the general processes that data professionals use to derive insights from data.

**Data Exploration**

The part of the data science process where a scientist will ask basic questions that helps her understand the context of a data set. What you learn during the exploration phase will guide more in-depth analysis later. Further, it helps you recognize when a result might be surprising and warrant further investigation.

**Data Mining**

The process of pulling actionable insight out of a set of data and putting it to good use. This includes everything from cleaning and organizing the data; to analyzing it to find meaningful patterns and connections; to communicating those connections in a way that helps decision-makers improve their product or organization.

**Data Pipelines**

A collection of scripts or functions that pass data along in a series. The output of the first method becomes the input of the second. This continues until the data is appropriately cleaned and transformed for whatever task a team is working on.

**Data Wrangling (Munging)**

The process of taking data in its original form and “taming” it until it works better in a broader workflow or project. Taming means making values consistent with a larger data set, replacing or removing values that might affect analysis or performance later, etc. Wrangling and munging are used interchangeably.

**ETL (Extract, Transform, Load)**

This process is key to data warehouses. It describes the three stages of bringing data from numerous places in a raw form to a screen, ready for analysis. ETL systems are generally gifted to us by data engineers and run behind the scenes.

**Web Scraping**

Web scraping is the process of pulling data from a website’s source code. It generally involves writing a script that will identify the information a user wants and pull it into a new file for later analysis.

### Machine Learning Techniques

The field of machine learning has grown so large that there are now positions for Machine Learning Engineers. The terms below offer a broad overview of some common techniques used in machine learning.

**Clustering**

Clustering techniques attempt to collect and categorize sets of points into groups that are “sufficiently similar,” or “close” to one another. “Close” varies depending on how you choose to measure distance. Complexity increases as the more features are added to a problem space.

**Decision Trees**

This machine learning method uses a line of branching questions or observations about a given data set to predict a target value. They tend to over-fit models as data sets grow large. Random forests are a type of decision tree algorithm designed to reduce over-fitting.

**Deep Learning**

Deep learning models use very large neural networks — called deep nets — to solve complex problems, such as facial recognition. The layers in a model start with identifying very simple patterns and then build in complexity. By the end the net (hopefully) has a nuanced understanding that can accurately classify or predict values.

**Feature Engineering**

The process of taking knowledge we have as humans and translating it into a quantitative value that a computer can understand. For example, we can translate our visual understanding of the image of a mug into a representation of pixel intensities.

**Feature Selection**

The process of identifying what traits of a data set are going to be the most valuable when building a model. It’s especially helpful with large data sets, as using fewer features will decrease the amount of time and complexity involved in training and testing a model. The process begins with measuring how relevant each feature in a data set is for predicting your target variable. You then choose a subset of features that will lead to a high-performance model.

**Neural Networks**

A machine learning method that’s *very* loosely based on neural connections in the brain. Neural networks are a system of connected nodes that are segmented into layers — input, output, and hidden layers. The hidden layers (there can be many) are the heavy lifters used to make predictions. Values from one layer are filtered by the connections to the next layer, until the final set of outputs is given and a prediction is made. A nice video explanation can be found here.

**Supervised Machine Learning**

With supervised learning techniques, the data scientist gives the computer a well-defined set of data. All of the columns are labelled and the computer knows exactly what it’s looking for. It’s similar to a professor handing you a syllabus and telling you what to expect on the final.

**Unsupervised Machine Learning**

In unsupervised learning techniques, the computer builds its own understanding of a set of unlabeled data. Unsupervised ML techniques look for patterns within data, and often deal with classifying items based on shared traits.

Hope, this glossary will be of a great help to you guys, when needed!

#### HAPPY LEARNING!!