# Becoming a data scientist

2019-03-27 00:48|来源: 网路

# How do I become a data scientist?

Background: I recently finished my bachelor's degree in computer science at Berkeley. Although it may be a bit late, I am just now getting interested in learning more about statistics and "data science." Unfortunately, I don't have much of a math background (only took up to Linear Algebra) and the required probability/discrete math course for CS. Although I started working, I have the option of enrolling in an MS CS program in January. What courses should I be looking at and will a MS in Statistics be more useful? If so, is it possible to get into an MS in Statistics without a strong math background? I will probably be looking into taking machine learning and data visualization.

Strictly speaking, there is no such thing as "data science" (see  What is data science? ). See also: Vardi, Science has only two legs:  http://portal.acm.org/ft_gateway...

Here are some resources I've collected about working with data, I hope you find them useful  (note: I'm an undergrad student, this is not an expert opinion in any way).

Take the Computational Linear Algebra course (it is sometimes called Applied Linear Algebra or Matrix Computations or Numeric Analysis or Matrix Analysis and it can be either CS or Applied Math course). Matrix  decomposition algorithms are fundamental to many data mining applications and usually underrepresented in a standard "machine learning" curriculum. With TBs of data traditional tools such as Matlab become not suitable for the job, you cannot just run eig() on Big Data. Distributed matrix computation packages such as those included in Apache Mahout [1] are trying to fill this void but you need to understand how the numeric algorithms/LAPACK/BLAS routines [2][3][4][5] work in order to use them properly, adjust for special cases, build your own and scale them up to terabytes of data on a cluster of commodity machines.[6] Usually numerics courses are built upon undergraduate algebra and calculus so you should be good with prerequisites.  I'd recommend these resources for self study/reference material:

2) Start learning statistics by coding with R:
• Pick up some R manuals (see
What are essential references for R?) and experiment with some of these data sets:  http://www.datawrangling.com/som...
and UCI Machine learning repository:  http://archive.ics.uci.edu/ml/
• Here is a good reference to get started with regression analysis:
Gelman, Data Analysis Using Regression and Multilevel/Hierarchical Models:
http://www.amazon.com/Analysis-R...
•  Albert, Bayesian computation with R:
http://www.amazon.com/Bayesian-C...
• Spector, Data Manipulation with R:
http://www.amazon.com/Bayesian-C...

3)  Learn about distributed systems and databases:
• Note: this topic is not part of a standard Machine Learning track but you can probably find courses such as Distributed Systems or Parallel Programming in your CS/EE catalog. I believe it is important to learn how to work with a Linux cluster and how to design scalable distributed algorithms if you want to work with big data. It is also becoming increasingly important to be able to utilize the full power of multicore. (see http://en.wikipedia.org/wiki/Moo... , http://techresearch.intel.com/ar...)
What's the best way to come up to speed on MapReduce, Hadoop, and Hive? )
• Learn about Google technology stack (MapReduce, BigTable, Dremel, Pregel, GFS, Chubby, Protobuf etc). (See
What are the most interesting Google Research papers?
• Setup account with Amazon AWS/EC2/S3/EBS and experiment with running Hadoop on a cluster with large data sets (you can use Cloudera or YDN images, but in my opinion you can better understand the system if you set it up from scratch, using the original distribution). Watch the costs.
What are some promising open-source alternatives to Hadoop MapReduce for map/reduce? )
http://swtch.com/~rsc/
http://herpolhode.com/rob/
http://www.cs.princeton.edu/~bwk/
http://cm.bell-labs.com/who/dmr/
http://www.cs.columbia.edu/~aho/
http://plan9.bell-labs.com/who/ken/
http://www.informatik.uni-trier....

What are the hottest startups in the analytics space?
Who are the best VCs in the field of analytics / data mining / databases?
Which companies have the best data science teams?
What are the notable startups in the news space?
Does the US Census have a data team?
Why do so many data geeks join web companies instead of solving large scale data problems in biology?

6)  Learn about least-squares estimation and Kalman filters:

• This is a classic topic and "data science" par excellence in my opinion. It is also  a good introduction to optimization and control theory. Start with Bierman's LLS tutorial given to his colleagues at JPL, it is clearly written and is inspiring (the Apollo mission trajectory was estimated using these methods): http://www.amazon.com/Factorizat... , also see Curkendall & Leondes: http://adsabs.harvard.edu/full/1974CeMec...8..481C and Quarles: http://citeseerx.ist.psu.edu/vie....
• See Steven Kay's series on statistical signal estimation: http://www.amazon.com/Fundamenta..., also check out his short course outline at University of Rhode Island for a list of interesting topics to learn (this is usually part of EE curricula): http://www.ele.uri.edu/faculty/k...

7) Check out these Q&A:

What are the best blogs about data?
What are the best blogs about bioinformatics?
What is data science?
What are the best courses at MIT?
What are the best resources to learn about web crawling and scraping?
What are the best interview questions to evaluate a machine learning researcher?
What are the best resources for learning about distributed file systems?
What are some useful packages for working with large datasets in R?
What are some good books on stringology and pattern matching?
What's a good introductory machine learning text?
What is the best book to pick up working knowledge of theoretical statistics (assuming strong general math)?
Can anyone recommend a fantastic book on time series analysis?
What are the standard texts on linear regression?
What are some good books on random processes?
How has BigTable evolved since the 2006 Google paper?
What is a good source for learning about Bayesian networks?
What are the best data visualizations ever created?
What are some of the prediction and risk estimation models used by insurance companies?
How do scientists share data?
What are the best quant hedge funds?
What are the best books on econometrics?
What are the best introductory books on mathematical finance?
What is the best approach for text categorization?
What are the numbers that every engineer should know, according to Jeff Dean?

If you do decide to go for a Masters degree:

8) Study Engineering - I'd go for CS with a focus on either IR or Machine Learning or a combination of both and take some systems courses along the way. As a "data scientist" you will have to write a ton of code and probably develop distributed algorithms/systems to process massive amounts of data. MS in Statistics will teach you how to do modeling and regression analysis etc, not how to build systems, I think the latter is more urgently needed these days as the old tools become obsolete with the avalanche of data. There is a shortage of engineers who can build a data mining system from the ground up. You can pick up statistics from books and experiments with R (see item 2 above) or take some statistics classes as a part of your CS studies.

Good luck.

[1]  http://mahout.apache.org/
[2]  http://www.netlib.org/lapack/
[3]  http://www.netlib.org/eispack/
[4]  http://math.nist.gov/javanumeric...
[5]  http://www.netlib.org/scalapack/
[7]  http://www.r-project.org/
Peter Skomoroch, Sr. Data Scientist @ Linkedin - ... 19 endorsements
If you have the time to take courses, give it a shot.

1) Try to take some of the undergrad math courses you missed. Linear Algebra, Advanced Calculus, Diff. Eq., Probability, Statistics are the most important.  After that, take some Machine Learning courses.  Read a few of the leading ML textbooks and keep up with journals to get a good sense of the field.

2) Read up on what the top data companies are doing.  After 1 or 2 machine learning courses you should have enough background to follow most of the academic papers.  Implement some of these algorithms on real data.

3) If you are working with large datasets, get familiar with the latest techniques & tools (Hadoop, NoSQL, R, etc.) by putting them into practice at work (or outside of work).

Read these posts by Mike Driscoll:

* http://dataspora.com/blog/the-se...
* http://dataspora.com/blog/sexy-d...
I am currently working as a data engineer with a team of others and I can tell you what we all have in common:

1) MS or PhDs in Applied Mathematics or Electrical Engineering
2)  Fluency C++/Matlab/Python
3)  Experience building distributed systems and algorithms.

I agree with Anon that CS is probably not the way to go unless you are going to MIT, Caltech, Stanford, CMU, etc. The way I ended up in the field was working as a software engineer designing real-time systems and getting a MS in Applied Math part-time. After 4 years I had skills from both fields and was offered a position doing ML/DM. With that said, I can tell you that its an extremely interesting field, and it appears the skill set will only become more desirable in the future.
Gregory Piatetsky, analytics/data mining consultant... 1 endorsement
A good start for becoming a data scientist is to get MS (or PhD) in Machine Learning / Data Mining - along the way you will get plenty of experience in relevant math and use latest systems.  Stanford, UCI, CMU, MIT are top schools, but there are many others in USA - see
http://www.kdnuggets.com/educati... and in Europe
http://www.kdnuggets.com/educati...

Stanford has online courses in data mining / ML - check
http://www.kdnuggets.com/2010/06...
http://scpd.stanford.edu/
Russell Jurney, Data Viznik, Hack Historian 2 endorsements
The school route is well covered.  This is the autodidactic route:

Look at some common problems solved with machine learning.  Look at problems in your areas of interest with an abundance of available data. Intersect these sets, pick a problem to solve with ML. Learn whatever it takes to solve it poorly.  Get people using the output of your model. Iterate, learn more techniques.  Work on your maths as needed.  Find mentors to talk with about problems you're working on.  Keep them updated, collaborate, learn from them.

Get good at building things with data.  Update your LinkedIn profile - congratulations, you're a data scientist!
Paco Nathan, 45 years ago I couldn't even spe... 4 endorsements
Stanford has an interdisciplinary degree specifically for data science, called Mathematical and Computational Sciences (MCS). It's sponsored by the Stats department and overlaps with CS, Math, Operations Research, etc.  http://www.stanford.edu/group/ma...  The BS degree dovetails particularly well with a co-term program to get an MS in Computer Science -- say, with a distributed systems specialization.

+1 to both Pete's and Russ' wise words above.
Yaniv Goldenrand, Fraud and credit modeling
3 votes by Alex Kamil, Kevin Li and Seb Paquet
Get a job doing it, this way you'll learn what really matters and get paid in the process.
The standard way to become a data analyst is master's in math/statistics + internship.

Other ways are:
- PhD in some empirical subject (economics, psychology).
- Get an engineering position in some data-intensive company and convert.
Some of the best modelers I know are ex-programmers.
1 vote by Alex Kamil
Reading data mining related blogs is also important to understand the wide application areas of data mining. You have a list of data mining blogs here: http://www.dataminingblog.com/li...
1) infrastructure of data processing, such as Hadoop/MapReduce,  Pig/Hive, and automation/cron.
2) simple stats about data, such as mean, correlation, and p-value.
3) algorithms for data modeling,  such as logistic regression, and SVM.
4) visualization of data, such as chart and table.

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