class: center, middle, inverse, title-slide

.title[
# Welcome!
]
.subtitle[
## <br><br> Data Science in a Box
]
.author[
### <a href="https://datasciencebox.org/">datasciencebox.org</a>
]

---

layout: true
  
<div class="my-footer">
<span>
<a href="https://datasciencebox.org" target="_blank">datasciencebox.org</a>
</span>
</div>

---

```
## [1] '0.25'
```

class: middle

# Hello world!

---

## Data science

.pull-left-wide[
- Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge.

- We're going to learn to do this in a `tidy` way -- more on that later!

- This is a course on introduction to data science, with an emphasis on statistical thinking.
]

---

## Course FAQ

.pull-left-wide[
**Q - What data science background does this course assume?**  
A - None.

**Q - Is this an intro stat course?**  
A - While statistics `\(\ne\)` data science, they are very closely related and have tremendous of overlap. Hence, this course is a great way to get started with statistics. However this course is *not* your typical high school statistics course.

**Q - Will we be doing computing?**   
A - Yes.
]

---

## Course FAQ

.pull-left-wide[
**Q - Is this an intro CS course?**  
A - No, but many themes are shared.

**Q - What computing language will we learn?**  
A - R.

**Q: Why not language X?**  
A: We can discuss that over ☕.
]

---

class: middle

# Software

---

<img src="img/excel.png" width="75%" style="display: block; margin: auto auto auto 0;" />

---

<img src="img/r.png" width="50%" style="display: block; margin: auto auto auto 0;" />

---

<img src="img/rstudio.png" width="73%" style="display: block; margin: auto auto auto 0;" />

---

class: middle

# Data science life cycle

---

<img src="img/data-science-cycle/data-science-cycle.001.png" width="90%" style="display: block; margin: auto auto auto 0;" />

---

<img src="img/data-science-cycle/data-science-cycle.002.png" width="90%" style="display: block; margin: auto auto auto 0;" />

---

<img src="img/data-science-cycle/data-science-cycle.003.png" width="90%" style="display: block; margin: auto auto auto 0;" />

---

<img src="img/data-science-cycle/data-science-cycle.004.png" width="90%" style="display: block; margin: auto auto auto 0;" />

---

<img src="img/data-science-cycle/data-science-cycle.005.png" width="90%" style="display: block; margin: auto auto auto 0;" />

---

<img src="img/data-science-cycle/data-science-cycle.006.png" width="90%" style="display: block; margin: auto auto auto 0;" />

---

.pull-left[
<img src="img/google-trend-index.png" width="75%" style="display: block; margin: auto auto auto 0;" />
]
.pull-right[
<img src="img/data-science-cycle/data-science-cycle.006.png" width="90%" style="display: block; margin: auto 0 auto auto;" />
]

---

.pull-left[
<img src="img/google-trend-index.png" width="75%" style="display: block; margin: auto auto auto 0;" />
]
.pull-right[
<img src="img/data-science-cycle/data-science-cycle.006.png" width="90%" style="display: block; margin: auto 0 auto auto;" />

```
## # A tibble: 5 × 2
##   date             season
##   <chr>            <chr> 
## 1 23 January 2017  winter
## 2 4 March 2017     spring
## 3 14 June 2017     summer
## 4 1 September 2017 fall  
## 5 ...              ...
```
]

---

<img src="img/data-science-cycle/data-science-cycle.007.png" width="90%" style="display: block; margin: auto auto auto 0;" />

---

<img src="img/data-science-cycle/data-science-cycle.008.png" width="90%" style="display: block; margin: auto auto auto 0;" />

---

<img src="img/data-science-cycle/data-science-cycle.009.png" width="90%" style="display: block; margin: auto auto auto 0;" />

---

<img src="img/unvotes/unvotes.gif" width="85%" style="display: block; margin: auto auto auto 0;" />

---

class: middle

# Let's dive in!

---

background-image: url("img/unvotes/unvotes-01.jpeg")

---

class: inverse

<img src="img/unvotes/unvotes-02.jpeg" width="100%" style="display: block; margin: auto;" />

---

class: inverse

<img src="img/unvotes/unvotes-03.jpeg" width="100%" style="display: block; margin: auto;" />

---

class: inverse

<img src="img/unvotes/unvotes-04.jpeg" width="100%" style="display: block; margin: auto;" />

---

class: inverse

<img src="img/unvotes/unvotes-05.jpeg" width="100%" style="display: block; margin: auto;" />

---

class: inverse

<img src="img/unvotes/unvotes-06.jpeg" width="100%" style="display: block; margin: auto;" />

---

class: inverse

<img src="img/unvotes/unvotes-07.jpeg" width="100%" style="display: block; margin: auto;" />

---

class: inverse

<img src="img/unvotes/unvotes-08.jpeg" width="90%" style="display: block; margin: auto;" />

---

class: inverse

<img src="img/unvotes/unvotes-09.jpeg" width="90%" style="display: block; margin: auto;" />

---

class: inverse

<img src="img/unvotes/unvotes-10.jpeg" width="90%" style="display: block; margin: auto;" />

---

class: inverse

<img src="img/unvotes/unvotes-11.jpeg" width="90%" style="display: block; margin: auto;" />

---

class: inverse

<img src="img/unvotes/unvotes-12.jpeg" width="90%" style="display: block; margin: auto;" />

---

class: inverse

<img src="img/unvotes/unvotes-13.jpeg" width="100%" style="display: block; margin: auto;" />

---

class: inverse

<img src="img/unvotes/unvotes-14.jpeg" width="100%" style="display: block; margin: auto;" />

---

.center[
.large[
[minecr.shinyapps.io/unvotes](https://minecr.shinyapps.io/unvotes/)
]
]

<img src="img/unvotes/unvotes-15.png" width="65%" style="display: block; margin: auto;" />

Notes for current slide

Notes for next slide

Welcome!

Data Science in a Box

datasciencebox.org

1 / 38

## [1] '0.25'

class: middle

Hello world!

2 / 38

Data science

Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge.
We're going to learn to do this in a tidy way -- more on that later!
This is a course on introduction to data science, with an emphasis on statistical thinking.

3 / 38

Course FAQ

Q - What data science background does this course assume?
A - None.

Q - Is this an intro stat course?
A - While statistics $\neq$ data science, they are very closely related and have tremendous of overlap. Hence, this course is a great way to get started with statistics. However this course is not your typical high school statistics course.

Q - Will we be doing computing?
A - Yes.

4 / 38

Course FAQ

Q - Is this an intro CS course?
A - No, but many themes are shared.

Q - What computing language will we learn?
A - R.

Q: Why not language X?
A: We can discuss that over ☕.

5 / 38

Software

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7 / 38

8 / 38

9 / 38

Data science life cycle

10 / 38

11 / 38

12 / 38

13 / 38

14 / 38

15 / 38

16 / 38

17 / 38

## # A tibble: 5 × 2
##   date             season
##   <chr>            <chr> 
## 1 23 January 2017  winter
## 2 4 March 2017     spring
## 3 14 June 2017     summer
## 4 1 September 2017 fall  
## 5 ...              ...

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Let's dive in!

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minecr.shinyapps.io/unvotes

38 / 38

## [1] '0.25'

class: middle

Hello world!

2 / 38

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