PYTHR/unit-6-pandas/07-next-steps/.ipynb_checkpoints/07-next-steps-slides-checkpoint.md

<!--
---
title: Next Steps in Data Science
type: lesson
duration: "0:45"
creator: Joseph Nelson
---
-->

<h1>Next Steps in Data Science</h1>

<!--

## Overview
This lesson recaps what students have achieved, contextualizes that into the broader data science ecosystem, and recommends libraries and resources to further their journey.

## Learning Objectives
*After this lesson, you will be able to:*

- Identify core libraries in the data science ecosystem, and their purpose
- Determine how to learn more about which area is most interesting to you!
- Discuss hiring in the data science job market, and strategies to support a search

## Duration
45 minutes.


## Suggested Agenda

|    Time     | Activity | Purpose |
|-------------|----------|---------|
| 0:00 - 0:03 | Welcome |
| 0:03 - 0:18 | Introspection and Review |
| 0:18 - 0:43 | Establishing Yourself |
| 0:43 - 0:45 | Summary |

## Materials and Preparation:
- Give out the link to the slides.
- This lesson, more than any other in this unit, gives the instructor the ability to inject their own perspective throughout the lesson. Do not hesitate to do so!

## Differentiation and Extensions

- Add your own experience throughout the lesson.
- If you're teaching this on campus to students, feel free to add several interview prep questions towards the end.

-->

---

## Learning Objectives
*After this lesson, you will be able to:*

- Identify core libraries in the data science ecosystem.
- Determine how to learn more about which area is most interesting to you!
- Discuss hiring in the data science job market and strategies to support a search.

---

## Celebrate

Reflect for a moment - you've:

- Learned the fundamentals of Python, from data types to object oriented programming.
- Used your first API to build a simple application.
- Applied Pandas to synthesize insights from datasets.

That's a lot! It deserves a huge congratulations.

<aside class="notes">

**Teaching Tips**:

- Pause here and check that everyone understands what they've done so far.
- Make sure they feel accomplished!

</aside>

---

## Discussion: Introspection

- What did you enjoy most?

- What did you find most intriguing?

- What do you want to know more about?

- What caused the most struggle?

This isn't an all-frills exercise. It helps inform your future data science growth!

<aside class="notes">

**Teaching Tips**:

- Stress that the point of this slide is to help students figure out what avenues they should explore next, so they should really think about these questions.

</aside>

---

## Revisiting the data science process

It's important to place our Pandas work into the broader picture of data science.

To do so, recall our data science workflow:

![](https://s3.amazonaws.com/ga-instruction/assets/python-fundamentals/Data-Framework-White-BG.png)

<aside class="notes">

**Teaching Tips**:

- Recap what each of these steps consists of and where they've seen it applied.
- Describe why we focus on the problem-framing portion of data science, consider personal experience.
- Data science is incredibly nascent, and its impact will be fully felt when problems are well-framed in advance of applying techniques

</aside>
---

## Discussion: Condensed Workflow

1. **Identify** the problem
2. **Acquire** the right data
3. **Parse** the data
4. **Mine** our data
5. **Refine** our data
6. **Build** a model
7. **Present** our work

**Class Question**: Where have we focused our work?

<aside class="notes">

**Teaching Tips**:

- Note that this is the same workflow in a more brief wording, and also something they'll see referred to.
- Encourage discussion (answers on next slides, so when discussion seems to be wrapping up, turn the slide).

</aside>

---

## Where we focused


1. Identify the problem
2. Acquire the right data
3. **Parse the data. We did this!** Remember the Adventure Works Production.Product dictionary? Did you revisit IMDB's source to understand any columns?
4. **Mine our data. We did this!** Checked subpopulation analyses and, perhaps, feature creation. We filtered to a specific county; potentially creating our own IMDB v Rotten Tomato metrics.
5. **Refine our data. We did this!** We mutated our data using the .apply() method to modify prices and color of products.
6. Build a model
7. Present our work

<aside class="notes">

**Teaching Tips**:

- Go through these three and check for understanding / agreement.

</aside>

---

## Where we did a bit

1. **Identify the problem. We did a bit!** Identify your own question about IMDB data, and answer it.
2. **Acquire the right data. We did a bit!** Using the OMDBApi to obtain Rotten Tomato data for our IMDB dataset.
3. Parse the data
4. Mine our data
5. Refine our data
6. Build a model
7. **Present our work. We did a bit!** Maintaining clean Jupyter Notebooks (right?) and creating takeaway visualizations.

***Whew***! We did cover a lot of ground!


<aside class="notes">

**Teaching Tips**:

- Go down these three and check for understanding.

</aside>

---

## Where we didn't Focus

1. Identify the problem
2. Acquire the right data
3. Parse the data
4. Mine our data
5. Refine our data
6. **Build a model. We never did this!**
7. Present our work

> "Hey! I thought that's all data science is! Machine learning artificial intelligence neural networks [on the blockchain]!"

<aside class="notes">

**Teaching Tips**:

- This is a quick slide - it's elaborated on in the next slide.

</aside>

---

## The truth about data science (sh)


- Exploratory data analysis is typically **80%** of a data science problem.
- Modeling is **20%**.

What's more:

- The steps you take to set up your models in EDA, ultimately have a outsized impact on the result you will achieve.


<aside class="notes">
**Talking Points**:

- Many, many businesses are sitting on latent and rich ($$$) relationships in their data that a Pandas expert can unlock.
</aside>

---


## Apologies in advance for this one

![](https://s3.amazonaws.com/ga-instruction/assets/python-fundamentals/what-i-really-ds.png)

<aside class="notes">
**Teaching Tips**:

- This is just a quick slide to lighten up the room! Give students a chance to read it and laugh before moving on.

</aside>

---


## Exceptions


- Many companies will structure teams such that some individuals focus 100% of their time on the 20% of the problem which is solved by modeling.

- We've focused on Pandas EDA.
	- The area you can make the greatest impact with.


<aside class="notes">
**Talking Points**:

- Of course, rules are meant for exceptions. Many companies will structure teams such that some individuals focus 100% of their time on the 20% of the problem which is solved by modeling.
- In giving you an "Intro," we focused on the area you can make the greatest impact with Python: Pandas EDA.
- In addition, there are more pre-requisites to discuss when it comes to learning modeling.
</aside>

---

## Python Data Science Package Ecosystem


We know Pandas!

- Awesome!
- Reads in data.
- Exploratory data analysis.
- Munging.
- Wrangling.
- Visualization via matplotlib


What else is there?


<aside class="notes">
**Talking Points**:

- We know the steps to solving a problem, and we know what **Pandas** can do. What about those other steps in the process?
- Many packages support our endeavors throughout the problem solving workflow.
- It would be a fool's errand to outline *every* Python library, so let's highlight the big ones. And all of these are **open source:** free to use and constantly improving.
</aside>

---

## Recommend Libraries for DS

Once you're comfortable with Pandas...

- **Seaborn:**
	- Creates visualizations (of greater complexity than Pandas)
	- With a few lines of code via `matplotlib`
- **NumPy:**
	- Numerical computation, particularly linear algebra.
- **SciPy:**
	- Scientific computation, especially statistics.
- **Requests:**
 	- Making web requests -  calling APIs!


<aside class="notes">
**Teaching Tips**:

- Go quickly down these bullets - add your own thoughts on what you recommend students to look in to.
- If any students earlier expressed interest in something here, call them out to it.

</aside>

---

## Other DS Libraries

Not as ubiquitous or popular, but still good:

- **BeautifulSoup:**
	- Easily parse HTML.
- **Statsmodels:**
	- Traditional statistic inference techniques, like linear regression.
- **Scikit-learn:**
	- All-purpose machine learning model construction.
- **NLTK** | **SpaCy**
	- Natural language processing.
- **TensorFlow** | **PyTorch** | **MxNet**
	- Neural network research and model construction.
- **PySpark**
	- Interacting with big data.


<aside class="notes">
**Teaching Tips**:

- Go quickly down these bullets - add your own thoughts on what you recommend students to look in to.
- If any students earlier expressed interest in something here, call them out to it.

</aside>

---

## Discussion: What-for-what?

At what step would each library be most helpful?

The data science steps:

- **Identify** the problem
- **Acquire** the right data
- **Parse** the data
- **Mine** our data
- **Refine** our data
- **Build** a model
- **Present** our work

---

## Discussion: What-for-what?

Match up these libraries:

- **Pandas:** for reading in data, exploratory data analysis, munging, wrangling, and visualization via matplotlib
- **Seaborn:** creates visualizations (of greater complexity) with a few lines of code via matplotlib
- **Requests:** for making web requests
- **NumPy:** for numerical computation, particularly linear algebra
- **SciPy:** for scientific computation, especially statistics


<aside class="notes">
**Teaching Tips**:

- Have students discuss and try this as a group. Then, match them up and talk students through why (e.g. **requests** would be used to **acquire** the data; **seaborn** would be used to **model** the data)

</aside>

---

## Learning More - How?

- Learn by doing.
	- Learning requires consuming and producing. (Perhaps even in 50/50 balance)

- Consume relevant content about what you want to learn (videos, books, etc).

- Have frequent **projects** and **exercises** to practice.

<aside class="notes">
**Teaching Tips**:

- Encourage students to learn on their own!
- Give specific suggestions if you can.
- Encourage students to identify a singular learning goal.
- Encourage students to make mistakes (seriously), and discuss why talking through problem solving is key.

</aside>

---

## Learning More - Where?

There's an abundance of resources, which can seem overwhelming, but it's actually a huge benefit.

For self-paced and online programs about a specific area, consider:

- DataCamp
- DataQuest
- Coursera

For instructor-led and guided education, come on back to General Assembly!

- We have expert-led workshops and courses in data science:
 	- A 10-week part-time data science (60hrs).
	- The Data Science Immersive, a full-time, three month program (480hrs).

These classes walk through the full data science lifecycle.


<aside class="notes">
**Teaching Tips**:

- Encourage students to learn more!
- Give specific suggestions if you can.
- There are an abundance of resources, but a lack of discipline and support when it comes to learning. To remedy this, encourage students to work together in study groups, attend Meetups, and pair their learning with immediate application

</aside>

---

## Stretchhhh

![](https://s3.amazonaws.com/ga-instruction/assets/python-fundamentals/panda-lying-down.jpeg)

- Stand up, stretch a bit.

- Or lie down!

- I'm not a cop.


<aside class="notes">
**Teaching Tips**:

- It's been a long course. Give them a second for a break and a smile.

</aside>

---

## What Do You Really Need?

Data scientists need three core skills:

- **Analytical thinking**
- **Mathematics and statistics proficiency**
- **Coding ability**

Let's break these down.

<aside class="notes">
**Teaching Tips**:

- This is just a quick overview - details on following slides.

**Talking Points**:

- We now have the coding ability, so in the next slides, let's talk about what else you might need.

</aside>

---

## Analytical thinking

- How well can you structure a data science problem / target an analysis for high impact output?

- Do you select metrics that align with those goals?

- Do you break a big problem into manageable, component parts?

**Class Question:**

- Imagine you are a data scientist at Facebook.
- Users list high schools they attended - some real, some fake.

How could you verify that a given high school a user listed is the one they attended? How would you measure success?


<aside class="notes">
**Teaching Tips**:

- This is a discussion! Encourage students to suggest answers.
- When they've finished, give the best answer.

**Answer**:
- Referencing a full list of true high schools, and text matching against it.
- Plotting the occurrence of various high school names, and assume that names that are more common (Jefferson, Lincoln, Roosevelt) are more likely -- consider scoring against a Z-score or Normal Distribution to determine legitimacy of various names
</aside>

---

## Mathematics and statistics proficiency

Can you apply fundamental maths and stats to problem solving? Do you have a firm understanding of probability? Linear algebra?

**Class Question:**

- There are 52 cards in a deck.
- 26 are red, and 26 are black. The 52 cards make up four suits (hearts, diamonds, spades, clubs).
- There are 13 of each suit (ace-10, jack, queen, king).
- It is a fair deck of cards.

What is the probability of drawing the 4 of spades OR a club? What is the probability of drawing any 3 OR a spade?


<aside class="notes">
**Teaching Tips**:

- This is a discussion! Encourage students to suggest answers.
- When they've finished, give the best answer.

**Answer**:
-  What is the probability of drawing the 4 of spades or a club? *These two separate probabilities are 1/52 and 13/52, and they're mutually exclusive so you just add them: 5/52*
- - What is the probability of drawing a 3 or a spade? *There are 4 threes and 13 spades, but one of the spades is a 3. To satisfy the 'event' described in this problem, there are 16 possible draws, out of the 52 cards; P(event 'e') = 16/52 which reduces to 4/13.*

</aside>


---

## Coding ability

- Can you write readable, maintainable, efficient code?
- Can you translate your thinking skills into programmatic thinking?
- Do you know Python, R, SQL, and/or Scala? *(Yes, you do!)*

**Question:**

Do you recall Fizzbuzz? Try writing it again here from scratch.

Open a new Python file, `fizz.py`.

- Write a program that prints the numbers from 1 to `n` (passed in).
- But, for multiples of three, print “Fizz” instead of the number.
- For multiples of five, print “Buzz”.
- For numbers which are multiples of both three and five, print “FizzBuzz”.


<aside class="notes">
**Teaching Tips**:

- Have each student try to do this in their own time. Allow 5-10 minutes.
- When they've finished, give the best answer with explanation.

```python
def fizzbuzz(n):

		if n % 3 == 0 and n % 5 == 0:
				return 'FizzBuzz'
		elif n % 3 == 0:
				return 'Fizz'
		elif n % 5 == 0:
				return 'Buzz'
		else:
				return str(n)

print "\n".join(fizzbuzz(n) for n in xrange(1, 21))
```

</aside>


---

## Establishing Yourself as a Data Scientist

1. Start a blog.
		- Blogs are incredibly common in technology.
		- They demonstrate your learning process.

2. Share with your network.
		- Keep your friends and coworkers engaged on what you're doing and learning.
		- Opportunities are sometimes spurious.

3. Attend Meetups and other networking opportunities to learn, meet, and share.


<aside class="notes">
**Teaching Tips**:

- Encourage students to learn more!
- Give specific suggestions if you can.
- Emphasize the importance of blogging to prove retention of information.

</aside>


----

## Summary:

- There are many paths you can go!
- Check the Additional Reading for links to libraries.
		- You probably want Seaborn, NumPy, or SciPy.
- Work on your core skills!
	- Analytical thinking.
	- Mathematics and statistics proficiency.
	- Coding ability.


<aside class="notes">
**Teaching Tips**:

- Do a quick recap for understanding.
- See if any students have questions about their potential next steps.

</aside>


---

## Additional Reading

- [Pandas docs](https://pandas.pydata.org/pandas-docs/stable/#)
- [Seaborn docs](https://seaborn.pydata.org/)
- [Requests docs](http://docs.python-requests.org/en/master/)
- [NumPy tutorial](https://docs.scipy.org/doc/numpy-1.13.0/user/index.html)
- [SciPy tutorial](https://docs.scipy.org/doc/scipy/reference/tutorial/index.html)