Intro to Descriptive Statistics

Intro to Descriptive Statistics

>Mathematically, I can do ANOVAs but have had virtually no opportunity to practice them and have not done linear regressions.

Economics and sociology use a decent amount of statistics in their research. You may or may not need to know how to conduct statistical tests, depending on how the level of sophistication you wish to achieve. Instead of textbooks, here are two links to descriptive and inferential statistics. These courses are free, and will allow you to practice stats through an online platform.

This was a better introduction to statistics than any book I found (they're free):

https://www.udacity.com/course/intro-to-descriptive-statistics--ud827
https://www.udacity.com/course/intro-to-inferential-statistics--ud201

Those prepared me for a master's level course in statistics so if it starts to be way more info than you need, Udacity has come out with this course since I took the ones above and it may be more useful:

https://www.udacity.com/course/intro-to-statistics--st101

this one is pretty good for statistics https://www.udacity.com/course/intro-to-descriptive-statistics--ud827

and this for linear algebra but you'd need some basics in python first. https://www.coursera.org/learn/linear-algebra-machine-learning

I would say start with Udacity's Descriptive and Inferential Statistics courses. And if those don't click, you're in the wrong field.

https://www.udacity.com/course/intro-to-descriptive-statistics--ud827

https://www.udacity.com/course/intro-to-inferential-statistics--ud201

How to become a data scientist

“Today’s world is drowning in data and starving for insights. Our digital lives have created an overwhelming flood of information. In the last 5 years, data scientists have come to the rescue by trying to make sense of it all. The sexy job in the next 10 years will be statisticians, and I’m not kidding.” – Hal Varian, chief economist at Google

Until the end of the last decade, the word “data scientist” hardly existed. However, new possibilities have opened up new frontiers owing to the huge volumes of data that keeps piling up. And, irrevocable changes in the way businesses are run have spawned loads of analysts and number crunchers to “manage” data and predict successful future strategies and outcomes.

Organizations are still falling over themselves trying to hire data experts who can harness the power of data to hasten the data-to-action process. Although not as many companies as should be are relying on data-driven decision making, by the turn of this decade, analytics will have taken over. Just ask early adopters such as Facebook, Amazon, and LinkedIn.

Rest assured, automated programs aren’t going to make data scientists obsolete anytime soon.

In this article, you’ll find the most recommended learning path to become a data scientist. In addition, we’ve added links for best tutorials to get started on your data scientist path.

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Who is a Data Scientist?

Here’s an interesting definition of a data scientist on the web: “A data scientist is someone who is better at statistics than any software engineer and better at software engineering than any statistician.”

You can read two conflicting views in Larry Wasserman’s Data Science: The End of Statistics?and Andrew Gelman’s Statistics is the least important part of data science.

It is far “safer” to say a data scientist wears many hats!

Data scientists typically uncover commercially valuable information hidden in tons of unstructured and structured data. They apply a formidable skillset of programming, statistics, math, business acumen, great communication, and some psychology on huge data sets to provide actionable insights.

These big data wranglers need to have contextual understanding and intuition to come up with magic. Identifying whether the data is meaningful requires an excellent blend of technical and business skills. And that’s what aspiring data scientists should build on.

Before you organize, package, and deliver data, you need to know how.

What skills do you need to become a consummate data scientist?

Looking at social scientist Drew Conway’s famous Data Science Venn diagram, hacking skills, math and stats knowledge, and substantive expertise (commonly assumed to be domain knowledge) portray the interdisciplinary nature of a data scientist’s strengths.

https://i.redd.it/57gcc4mr9lc31.png

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• A data scientist needs hacking skills to collect and munge e-data, math and stats knowledge to apply the right tools and techniques to glean key insights, and substantive expertise to ask motivating questions and make predictions. Conway says a major part of the data science cycle lies in hacking skills, which is focused on tools such as Python, R, and Hadoop.
• Modeling follows exploring data. This is where math and stat come into play. The trick lies in finding the most suitable technique to apply on big data to identify the least error-prone predictive model.
• The final step would involve a data scientist knowing how to interpret the results and ask interesting questions.

There is a series of Venn diagrams modeled on Conway’s version.

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The 2016 version by Gartner perhaps makes more sense with its specific call-outs.

In the video below, Jeff Hammerbacher, Cloudera Co-founder and a prominent data scientist, calls data scientists “data rats.” Hammerbacher, who coined the term data scientist, says there is no perfect background to becoming a data scientist. In practice, he believes there are five components you need to be trained on to do your job properly:

• Data collection and integration
• Data visualization (dashboard design)
• Large-scale experimentation
• Causal inference and observational studies
• Data products (fitting machine learning models, deploying in production, setting up a regular refresh cycle, and evaluating performance)

So what’s in a data scientist’s technical toolkit?

A data scientists has to be more than proficient in the following tools and techniques. We’ve provided a few useful links to help you get an idea about the specific topics.

• Mathematics—linear algebra, calculus
• Statistics—probability, Bayesian stats, inferential and descriptive stats, game theory, optimization
• Programming Languages (R, Python, C++, Java etc)—algorithms and data structures, distributed computing
• Machine learning in R or Python—random forests, neural nets, k-nearest neighbors, decision trees, SVM, etc.
• Databases and querying languages— MySQL, MongoDB, Cassandra
• Big Data technologies—Apache, Hadoop, Pig, Spark, Hive, Flume, Cloudera
• Cloud services like Amazon S3

Check out Top Data Science Skills by Job Role here.

How can you become a data scientist?

Watch this great webinar by Jesse Steinweg-Woods on “How to become a data scientist in 2017” to get answers to some really specific questions.

For the professional:

If you want to switch to a career in data science, then taking free MOOCs (Coursera, Udacity, EdX, Khan Academy) or enrolling for online classes could be your best bet. People from diverse backgrounds can find themselves doing really well in analytical jobs because of their amazing talent for problem-solving, curiosity, and communication skills.

For the student:

Universities world over offer graduate courses in data science, business intelligence, analytics, and big data technologies. For math, statistics, or computer science undergraduates, this could be a fantastic option. If you want to study on your own, that’s fine too because there are lots of free e-books to help you master the skills you need.

Joining competitions, attending data science meet-ups, doing projects for experience, and updating your repertoire of skills will ensure that you are near-perfect for the job. It is really all about practice. And tons of it.

Before you go all out, you can get an internship or join a bootcamp with companies such as Amazon, Zipfian, and Twitter just to be sure that this is the right career choice for you.

Why become a Data Scientist?

You don’t need to be sold the idea. Really?

You love numbers. You love data. But truthfully, aren’t the big bucks and the job security great incentives?

Data scientists make about $130,000 a year on average. Since 2013, job postings for data scientists have grown by 108%. Research says that the career path for a data scientist is expected to touch almost 19% this decade. And, Glassdoor says that data science jobs have great average scores for work-life balance. Data scientists are critical assets for any organization today.

With studies saying that demand is expected to outpace supply and top companies all over looking for the brightest analysts, you can figure out the answer for yourself quite easily.

Source: How to become a data scientist

I haven't actually done them yet but planning to do the following courses

Statistics fundamentals: https://www.udacity.com/course/intro-to-descriptive-statistics--ud827 https://www.udacity.com/course/intro-to-inferential-statistics--ud201

UX experiments; https://www.udacity.com/course/ab-testing--ud257

All are free and pretty well structured. I feel like you should start with free courses then do the paid ones if you still feel the need.

By the way, I'd be interested if anyone knows of any quantitative usability testing courses.

you can look through some of these
https://www.reddit.com/r/WGU/search?q=msda&restrict_sr=on

some of those posts give great details about the courses

you could look at doing a couple of these free courses

https://www.udacity.com/course/intro-to-descriptive-statistics--ud827

https://www.udacity.com/course/intro-to-inferential-statistics--ud201

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

https://www.udacity.com/course/sql-for-data-analysis--ud198

https://www.edx.org/course/introduction-to-python-for-data-science-1

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

https://www.edx.org/course/probability-and-statistics-in-data-science-using-python

https://www.mathsisfun.com/data/standard-deviation.html

https://www.mathsisfun.com/data/index.html

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

https://www.udacity.com/course/intro-to-descriptive-statistics--ud827

https://www.udacity.com/course/intro-to-inferential-statistics--ud201

just the free course, not the nanodegrees
https://www.udacity.com/course/intro-to-statistics--st101

https://www.youtube.com/playlist?list=PL8dPuuaLjXtNM_Y-bUAhblSAdWRnmBUcr

http://patrickjmt.com/#probability-statistics

Hello! While I'm also aiming to get into data science, I don't have personal experience to draw from as I'm starting a bit behind you in terms of qualifications (I have an AS and I'm currently working on my BS in comp sci/cog sci). However, in researching to chart my own path, I've found resources that may be of use to you.

• "5 Steps To Actually Learn Data Science" is an article that was recommended to me by a friend who works in the field. The five steps are fleshed out with lots of actionable advice (also, you should read kdnuggets regularly!)
• David Venturi transitioned from a career in chemical engineering into data science largely through self-study. The courses he took and his rationale for choosing them are outlined here.
• This article is by the program director of Insight Data, with advice specifically geared at helping scientists transition into industry.
• This thread from /r/datascience (which you should definitely subscribe to, if you haven't already)
• "The Four Data Science Skills I Didn't Learn in Grad School"

In terms of math, since MechE has a full calculus sequence, you can take heart in knowing that you're off to a strong start. I would suggest you work on your statistics (Udacity has decent courses on descriptive and inferential stats, which you can supplement with other resources) and begin programming with Python (there is a two-course sequence on EdX that you can follow: part 1 part 2)

Yes, there are many free courses that you can use to learn the prerequisite mathematics. KhanAcademy would be my first recommendation, but you can also try some of these:

Inferential Statistics https://www.coursera.org/learn/inferential-statistics

Bayesian Statistics: From Concept to Data Analysis https://www.coursera.org/learn/bayesian-statistics

Inferential Statistics Intro https://www.coursera.org/learn/inferential-statistics-intro

Bayesian Statistics https://www.coursera.org/learn/bayesian

Basic Statistics https://www.coursera.org/learn/basic-statistics

Introduction to Probability https://www.edx.org/course/introduction-probability-science-mitx-6-041x-2

Introduction to Linear Models and Matrix Algebra https://www.edx.org/course/introduction-linear-models-matrix-harvardx-ph525-2x-2

Intro to Descriptive Statistics https://www.udacity.com/course/intro-to-descriptive-statistics--ud827

Intro to Inferential Statistics https://www.udacity.com/course/intro-to-inferential-statistics--ud201

Mathematics for Computer Science https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/index.htm

An Intuitive Introduction to Probability https://www.coursera.org/learn/introductiontoprobability

Statistical Inference https://www.coursera.org/learn/statistical-inference

College Algebra and Problem Solving https://www.edx.org/course/college-algebra-problem-solving-asux-mat117x

Precalculus https://www.edx.org/course/precalculus-asux-mat170x

https://www.udacity.com/course/intro-to-statistics--st101

https://www.udacity.com/course/intro-to-descriptive-statistics--ud827

https://www.udacity.com/course/intro-to-inferential-statistics--ud201

These courses on Statistics by Udacity are nice enough for most purposes. If you finish the first one, or the other two combined you can take a look at this specialization from Coursera too.
https://www.coursera.org/specializations/mathematics-machine-learning?

It s the one I finished, because I was not as rusty on Statistics back then, and I found it amazing. I like particularly the intuitive approach they took, and not bore you till death with meaningless proofs, however, the last course on PCA is a glaring exception to this rule.

Depends on your level. I'm planning on making the first half of 2021 a time for "Maths & Stats". Here's some suggestions:

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1. Descriptive Stats - Udacity - Can be done in like a week, even though it recommends 11 weeks. However spend some time making sure you really understand each concept (e.g. Central Limit Theorem). I'd advise testing yourself and doing a complete review of what you've learned each week.
2. Inferential Stats - Udacity - Same as above.
3. StatsQuest - JoshStarmer YouTube - This is a great resource for stats if you struggle with any concepts. Use it throughout your learning.
4. Statistics Course - FreeCodeCamp - Never done this, but it's a cool idea covering this much stuff in a single video.
5. Stats & Probability - Khan. - Khan Academy is a really good resource for this kind of stuff. Once you've completed the above, trying blasting yourself through this course and testing yourself. Use other resources or courses for understanding probability if need be.
6. Essence of Linear Algebra - Watch this before learning Linear Algebra. Watch it again after.
7. Linear Algebra Course - Khan - Plenty of online resources for learning LA, but this is often recommended.
8. PreCalculus - FreeCodeCamp - Depends on your knowledge, but you might want to blast through this course over a weekend or two before jumping into calculus.
9. Essence of Calculus - Watch this before learning Calculus. Watch it again after.
10. Calculus 1 Course - Khan - This is Calc 1. You'll probably want to work your way up to multivariate. It's debatable how important this is for DS. Probably varies between jobs. I would get Calc 1 done - but don't stress too much about it.

You don't need to do all of the above, and there's possibly some stuff I've missed.

Before/During the above, read both "The Art of Statistics" and "How to Lie with Statistics". These will give you good, high-level overviews of stats. Also use All of Statistics (a textbook) to supplement your learning. In fact, in might be a good idea to read 1 chapter a week (preferably a chapter related to what you'll be learning that week). Reading over what you'll be going on to learn is a great way to learn.

Of course, this is all just covering some uni level stats. You'll want to eventually start focusing on machine learning and things like that. For this, use Intro to Statistical Learning. I don't know if I'd recommend reading page to page. Just read a chapter on whatever topic you're currently learning, then use it as a reference point.

If the prospect of learning all this is daunting, read "A mind for numbers". It'll help you learn how to learn.