Intro to Data Analysis

A course in Udacity teaches you about data analyst and is very interactive and offers short quizzes. It's also free!

all these are free -- don't know how good they are

https://www.udacity.com/course/intro-to-data-analysis--ud170

https://www.udacity.com/course/data-analysis-with-r--ud651

https://www.udacity.com/course/intro-to-data-science--ud359

https://www.udacity.com/course/creating-an-analytical-dataset--ud977

https://www.udacity.com/course/problem-solving-with-advanced-analytics--ud976

https://www.udacity.com/course/intro-to-relational-databases--ud197

https://www.udacity.com/course/data-analysis-and-visualization--ud404

this is free -- you only pay if you want a certificate

https://www.edx.org/course/analytics-python-columbiax-bamm-101x-0

https://www.edx.org/course/python-for-data-science

Is Udacity a good place to start learning?

I'm new to this sub (I hope I'm in the right place!), so hi everyone!

I've decided to learn Python, mainly cause I'd like to use it to further my data analysis skills. I already have beginner's knowledge of Java because I took an intro CS course in college.

I've never used Udacity, but discovered it recently and it seemed like a good starting point. I'm mainly looking at these classes:

• [Intro to CS] (https://www.udacity.com/course/intro-to-computer-science--cs101)

• [Programming Foundations] (https://www.udacity.com/course/programming-foundations-with-python--ud036)

• [Algorithms] (https://www.udacity.com/course/intro-to-algorithms--cs215)

• [Design of Computer Programs] (https://www.udacity.com/course/design-of-computer-programs--cs212)

After having a Python foundation I'd take these courses:

• [Intro to Data Analysis] (https://www.udacity.com/course/intro-to-data-analysis--ud170)

• [Intro to Data Science] (https://www.udacity.com/course/intro-to-data-science--ud359)

• [Model Building and Validation] (https://www.udacity.com/course/model-building-and-validation--ud919)

Do you think this is a decent curriculum?

Or perhaps I should dive right in to the [Intro to Python for Data Science] (https://www.datacamp.com/courses/intro-to-python-for-data-science) course that's linked on the Wiki?? I'm scared all the Udacity courses might take forever to complete and I'll lose steam...

Thanks for any help and advice!

Interesting in getting into data science. Critique my game plan?

I am currently a medical coder who is interested in entering the realm of data science. My game plan is as follows:

** - Where I'm currently at

• **Take Udacity's Intro to Computer Science - Free, hopefully finish in 3 months
• Take Udacity's Programming Foundations with Python - Free, hopefully finish within 6 weeks
• Take Udacity's Intro to Data Analysis - Free, hopefully finish within 6 weeks
• Take Udacity's Data Analyst nanodegree - $999, hopefully finish within 4 months
• Apply for WGU's Masters in Data Analytics- $3,540/6 months, hopefully finish within 6-18 months

So with this goal, I will be able to hopefully get a data analyst/data science job within 28 months (2+ years) for only $11k+ and have more than sufficient skills to be an effective data scientist. I do hope I would be able to get at least a data analyst job after the nanodegree to start off and get out of the medical coding world.

Any criticism/advice? What else can I add/change in order to hopefully get a job when I finish either the Data Analyst nanodegree or the Master's?

Well you'll need a stats base before you get around to visualizing anything, so I suggest learning Python's Numpy, Scipy, Matplotlib stack here. That course is free, and there are many other ones free that you can search for here as well.

Well, you will likely have to collect this data yourself. You can definitely use various services from Google, Facebook, etc. to find information about businesses, but ultimately you'll have to survey these businesses yourself to find the meaningful insights you're looking for.

In terms of APIs, Google Places, Facebook's Graph API, and Yelp Fusion can help you find businesses and organizations for your search. This can get you things like location, categorization, ratings, etc. about various businesses in your area. From here, you will have to come up with a survey strategy to get more meaningful information, such as how likely they are to use your services, how much they'd be willing to pay, etc. It will most likely not be feasible to collect this information from every potential customer, so you'll have to collect as much data as possible as efficiently as possible (i.e., start with emails, then calls, then mail, etc.).

Once you've collected as much data as you can, you can try to extrapolate over the entire dataset. This isn't straight-forward, but the general idea is that if your data is a good representation of the population, then your inference should hold over the population. For example, if there are 100 businesses of some category in some location, and you surveyed 10 of them and 1 of them responded in such a way that you could see them as a strong candidate for a potential customer, then you can roughly say that 10 of samples in the entire population (i.e., out of the 100) may be strong candidates (of course, this is a gross simplification, so be sure to analyze your data properly).

You'll want to analyze this data across different dimensions to try to find trends to improve your findings and livelihood of success. For example, you may find that businesses in category A are significantly more likely to consider your services and businesses in location B are also significantly more likely to consider your services, and by examining these two pieces of information, you may find that businesses in category A in location B are very likely to consider your services. From here, you can perform further research (e.g., go to location B and talk to each business of category A to find more insight).

You'll also want to keep in mind some metrics that define success and compare your findings to these metrics. For example, if you expect your business to cost $1,000 a month to run and want to profit $100 per month, you know that you'll need to make $1,100 a month, otherwise your business will fail. If you can target your data discovery so that this information can be more accurately predicted, you'll be in a much safer spot to succeed as a business. I mean, if you're data says that you're likely to only be able to make $500 a month, you'll know that starting this business will probably be a bad idea.

Now, I don't know how much experience you have (I'm guessing very little considering the question), so you'll be doing yourself a huge favor by learning about these processes prior to starting into it. Something like this would be a good start. You can gauge how much you know versus how much you need to learn, and go from there (and don't let that 6 weeks timeline turn you off- it can be completed much quicker if you're not starting from scratch and not worried about the details).

All-in-all I'd say don't complicate it. You won't have enough data to be able to perform full-blown analysis, so frequently consider what this means for your potential business when working through this stuff. You may find that simply getting a list of businesses is enough to get you off the ground and start communicating with people, which may be sufficient for your needs. If you find yourself making precise predictions or extrapolating considerably, you've gone too far.