Week 1 was all about brainstorming ideas and gathering resources – dreaming up what you’d love to learn, and finding content that will help you learn it.

Week 2 (which started yesterday, but don’t worry, jump in any time even if you see this blog post a month from now) is all about goal-setting.

You should set a #SoDS18 goal that’s lofty enough to excite and motivate you, but not so out of reach that you’ll never complete it and only get disheartened when halfway through the summer you realize you are only 10% of the way there.

Make sure to keep goals attainable! Last year my goals were too expansive and by week 4 there was zero chance of accomplishing all of them which was super demotivating https://t.co/uXiwUFNYU5

— Nick Heitzman (@NickDoesData) May 28, 2018

I also want to make sure you know what makes a good goal. I like the definition used by the SMART approach:

Your goal should be

• Specific
• Measurable
• Achievable
• Relevant
• Time-Bound

Instead of explaining each of these in detail (you can read more about it elsewhere on the internet), I’m going to give an example of things you can jot down for yourself for each of these, then an example summary tweet for 2 different #SoDS18 goals.

Let’s say the idea you had for what to learn this summer is “Start learning Python”, and the resource you found is DataQuest. Let’s turn that into a SMART goal:

Specific – Learn how to import, clean, and visualize data using python and pandas

Measurable – Complete all courses in the DatQuest Data Scientist Path

Achievable – I can spend at least 6 hours on this project every weekend, plus occasional weekday evenings, so I have enough time available to do the work [Note from Renee: I have not actually researched how long this course series would take to complete]. I have joined the #py4ds Slack community and will ask for help there and on DataQuest if I get stuck so I don’t get set far behind.

Relevant – I want to add python and pandas to my resume, and it’s my first step on my new path to becoming a data scientist, so it’s relevant to my career goals and I’m motivated to accomplish it.

Time-Bound – the Summer of Data Science ends on September 3, so I will finish this first goal by August 3 in order to have time to complete a small project during the last month of #SoDS18.

Example tweet to share this goal with the world:

My 1st#SoDS18 goal: I will learn to import, clean, and visualize data with python & pandas by spending 6-8 hours per week on the Data Scientist Path on DataQuest, and will complete it by August 3. I’ll ask in #py4ds Slack if I need help.

Or, if your idea is to “do a machine learning project using at least 2 different algorithms on some kind of dataset that could help people”. That can be converted to a SMART goal like:

Specific – Learn how to use random forest and logistic regression in R by experimenting with data from the Kaggle DonorsChoose.org Dataset to develop a list of donors to email about a particular type of project request

Measurable – I will complete exploratory data analysis on the available DonorsChoose data files and write a blog post about my findings that includes at least 3 visualizations. Then I will find out what it means to submit a Kaggle Kernel, build 2 machine learning models using random forest and logistic regression algorithms and compare their model evaluation metrics to each other, submit the Kernel (even if the contest period is over), and find and study at least 2 other people’s submissions to understand different approaches to the problem. Then I will write another blog post summarizing my results and findings.

Achievable – I have read about random forest and logistic regression online, and my friend gave me the Introduction to Statistical Learning book so I can better understand these machine learning algorithms. I have a bunch of resources bookmarked online in case I need extra references to understand the book. I will tweet using the #rstats hashtag or talk to my friend if I need help. If I find out the dataset I found isn’t great for learning these 2 algorithms, I will search for another dataset as needed. I can dedicate 2 hours a day 4 days per week to working on the project and researching these topics.

Relevant – I started learning R over the last year and have used it to complete labs at school, but want to expand my machine learning capabilities and apply my skills to a real-world dataset before I start applying for jobs in the fall.

Time-Bound – I have 12 weeks to complete the project this summer.

Example tweet

My #SoDS18 goals are to:
-explore the DonorsChoose Kaggle dataset
-use ISL book & online resources to learn to build random forest and logistic regression models
-create and submit a Kaggle Kernel to help DonorsChoose
-write at least 2 blog posts about it over the next 12 weeks

I think you get the idea!

I should also mention that you don’t want to over-plan. Notice the note about switching datasets if one doesn’t work out – plan to be flexible! You don’t yet know what you’re getting into, and you might need to find more time finding good resources to learn, getting help, or pivoting if your original plan doesn’t work out. That’s OK! Just go with the flow and try to achieve something comparable to your initial goal. But, you need an initial goal in order to figure out where you are relative to it!

So, finish brainstorming your learning ideas and finding resources this week, then narrow it down to a SMART goal, and tweet about it with the #SoDS18 hashtag so we know what you plan to learn during the Summer of Data Science 2018!

And if you’re still looking for project ideas, check out Mara Averick’s post, browse the #SoDS18 hashtag, or join a data science learning community! (More about this in another blog post later this week!)

But I’m sure the main thing you’re here to find out is how to get involved yourself! So, here are the basics:

How to participate in the Summer of Data Science:

• Pick a thing or a short list of things related to data science that you want to learn more about this summer (or this winter if you’re in the southern hemisphere!)
• Make a plan to learn it (like an online course, a practice project, etc.).
• Share that plan on social media, then post updates as you make progress, with the hashtag #SoDS18.

Here’s a twitter moment with a bunch of entries from #SoDS17 for reference!

We’ll run this one from today – May 28, 2018 – through Labor Day in the U.S. – September 3, 2018. What you can realistically get done in that time depends on where you are in your data science learning journey, what your work schedule and family obligations are like, and many other factors – so think about what’s realistic for you to accomplish during this time.

Week 1 will be about brainstorming and researching possibilities and resources for summer projects, courses, etc. And in Week 2 we’ll set specific goals for the rest of the summer. So, start thinking of ideas now!

If you would like some ideas for beginners, here’s a list of beginner content on my site DataSciGuide:
Recommended Resources for Beginners
You might want to pick a book or course and go through it, trying out the exercises this summer.

I also have a Flipboard where I have collected a bunch of Data Science Tutorials you might want to check out (note: these aren’t all aimed at beginners).

There are also a whole bunch of online communities where you can join others in a project, or ask questions if you get stuck on yours. I’ll be writing another post highlighting those this week!

Follow me on twitter @becomingdatasci, and tweet with the hashtag #SoDS18 when you post updates about your progress! (It’s a good idea to “thread” your tweets throughout the summer, or add them to a Twitter Moment, so others can easily follow along!)

I’ll be retweeting a bunch of people’s ideas and resources, so keep an eye out there for more ideas if you aren’t sure where to start!