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4-Data-Science-Lifecycle/14-Introduction/README.md
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## Pre-Lecture Quiz
[Pre-lecture quiz]()
## [Pre-Lecture Quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/26)
At this point you've probably come to the realization that data science is a process. This process can be broken down into 5 stages:

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4-Data-Science-Lifecycle/15-analyzing/README.md
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## Pre-Lecture Quiz
[Pre-lecture quiz]()
## [Pre-Lecture Quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/28)
Analyzing in the data lifecycle confirms that the data can answer the questions that are proposed or solving a particular problem. This step can also focus on confirming a model is correctly addressing these questions and problems. This lesson is focused on Exploratory Data Analysis or EDA, which are techniques for defining features and relationships within the data and can be used to prepare the data for modeling.
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## Data Profiling, Descriptive Statistics, and Pandas
How do we evaluate if we have enough data to solve this problem? Data profiling can summarize and gather some general overall information about our dataset through techniques of descriptive statistics. Data profiling helps us understand what is available to us, and descriptive statistics helps us understand how many things are available to us.
In the few of the lessons, we have used Pandas to provide some descriptive statistics with the [`describe()` function]( https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.describe.html). It provides the count, max and min values, mean, standard deviation and quantiles on the numerical data. Using descriptive statistics like the `describe()` function can help you assess how much you have and if you need more.
In a few of the previous lessons, we have used Pandas to provide some descriptive statistics with the [`describe()` function]( https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.describe.html). It provides the count, max and min values, mean, standard deviation and quantiles on the numerical data. Using descriptive statistics like the `describe()` function can help you assess how much you have and if you need more.
## Sampling and Querying
Exploring everything in a large dataset can be very time consuming and a task that’s usually left up to a computer to do. However, sampling is a helpful tool in understanding of the data and allows us to have a better understanding of what’s in the dataset and what it represents. With a sample, you can apply probability and statistics to come to some general conclusions about your data. While there’s no defined rule on how much data you should sample it’s important to note that the more data you sample, the more precise of a generalization you can make of about data.
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## 🚀 Challenge
## Post-Lecture Quiz
## [Pre-Lecture Quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/27)
[Post-lecture quiz]()
## Review & Self Study

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quiz-app/src/assets/translations/en/group-4.json
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"title": "Data Science LifeCycle - Analyzing Pre-Quiz",
"quiz": [
{
"questionText": "quiz 2 question 2",
"questionText": "Analyzing can refer to analyzing models or data",
"answerOptions": [
{
"answerText": "True",
@ -140,35 +140,39 @@
]
},
{
"questionText": "q2",
"questionText": "What is important about exploring data before using it in a model or further analysis?",
"answerOptions": [
{
"answerText": "classical ML was invented first",
"answerText": "To remove data",
"isCorrect": "false"
},
{
"answerText": "the use of neural networks",
"answerText": "Identify challenges in the data",
"isCorrect": "true"
},
{
"answerText": "deep learning is used in robots",
"answerText": "Exploring data isn't important",
"isCorrect": "false"
}
]
},
{
"questionText": "q3",
"questionText": "What are some things a data scientist will do when exploring data? ",
"answerOptions": [
{
"answerText": "to automate the solving of multi-dimensional problems",
"answerText": "Queries",
"isCorrect": "false"
},
{
"answerText": "to customize a shopping experience based on the type of customer",
"answerText": "Sampling",
"isCorrect": "false"
},
{
"answerText": "both of the above",
"answerText": "Visualizations",
"isCorrect": "false"
},
{
"answerText": "All of the above",
"isCorrect": "true"
}
]
@ -180,48 +184,48 @@
"title": "Data Science LifeCycle - Analyzing Post-Quiz",
"quiz": [
{
"questionText": "quiz 2 question 2",
"questionText": "Visualization is never part of exploratory data analysis (EDA)",
"answerOptions": [
{
"answerText": "True",
"isCorrect": "true"
"isCorrect": "false"
},
{
"answerText": "False",
"isCorrect": "false"
"isCorrect": "true"
}
]
},
{
"questionText": "q2",
"questionText": "Which Panda functions provides basic data profiling?",
"answerOptions": [
{
"answerText": "classical ML was invented first",
"answerText": "pandas()",
"isCorrect": "false"
},
{
"answerText": "the use of neural networks",
"isCorrect": "true"
"answerText": "isnull()",
"isCorrect": "false"
},
{
"answerText": "deep learning is used in robots",
"isCorrect": "false"
"answerText": "describe()",
"isCorrect": "true"
}
]
},
{
"questionText": "q3",
"questionText": "What is the purpose of sampling data?",
"answerOptions": [
{
"answerText": "to automate the solving of multi-dimensional problems",
"answerText": "Sampling is better than data profiling when looking for errors",
"isCorrect": "false"
},
{
"answerText": "to customize a shopping experience based on the type of customer",
"answerText": "Sampling doesn't serve a purpose",
"isCorrect": "false"
},
{
"answerText": "both of the above",
"answerText": "Sampling is used to analyze data in a large data set because it's hard to analyze all of it",
"isCorrect": "true"
}
]

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