Data Analyst

What's a good pathway to developing data science skills?

My background is a B.S. in EE, and this fall I'll be re-starting a Masters in CogSci. I know basic Python, and I'm currently working through "Discovering Statistics with R" by Andy Field. I don't know SQL, and my LinAlg and Calc needs brushing up on.

Frankly, I'm lost in all the online resources. I've spent the better part of a day trying to compare courses like Udacity's Data Analyst Course and Coursera's Data Science Course keeping in mind other things people continue to mention like Peter Ng's Machine Learning Course and free resources like DataQuest. I can't figure out where my time would be best spent.

I realize for job searching the best thing to have is a portfolio of projects, but it'd be nice to have a structured approach for the moment. Any and all advice is appreciated. Thanks!

https://www.udacity.com/course/data-analyst-nanodegree--nd002

Looking for guidance on how to break into the field

Former journalist here - recently got laid off, and to be honest the field continues to shrink so I've been eyeing making a transition into the field of data analytics for a while. From a journalism perspective, being able to play around with and pull data could give my stories considerable heft, and intertwining that with my editorial skills it would be something maybe even fun that I'd use to establish a blog on. I've looked into the field and it seems to be described as the intersection of business acumen, presentation/visualization skills, and analytical skills. First two I have substantial experience in, but I'm concerned about the analytical side.

As I come from a journalism major, I haven't touched any sort of math since.. I can't even remember. Been too damn long. But I'm willing to put in the effort, just want a bit of guidance to help me course correct and feel more confident in this undertaking.

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Basically, I want to take this time when I'm receiving unemployment to spent all day developing the skills for an entry level position. I've read that for an entry-level data analyst position I should be comfortable with SQL, Tableau, and some statistics. Anything else I should be looking into? I always like to err on the side of being overprepared, so here's my gameplan:

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1. Study python, learn about scraping data and visualizing it and the like. I'm a week on this and seeing some pretty good progress. Might as well begin with the hardest part out the way, especially since I need a cursory understanding of it to go into:
2. Does anyone have any thoughts on the Udacity Data Science Nanodegree? (link: https://www.udacity.com/course/data-analyst-nanodegree--nd002) I've searched extensively for some sort of course that will give me projects and some guidance and so far this one seems like a good choice, as I'll walk out of it with a portfolio and ready to expand and add some more stuff into it. Udacity has received some flack recently and they've laid off staff and IDK so, would love some advice.
3. Do a couple freelance projects. Maybe set up a blog to talk about the findings in detail which can help showcase my storytelling aspects as well.

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So far I'm thinking I should be ready for an entry-level position in about three months of doing this full-time (remember, I'm unemployed). Is this realistic? How competitive is the job market? I'm 27 and in NYC, if it matters.

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Any advice is appreciated!

I'd transition first, then look for work, but most large multinational European financial services companies have US branches and need people to work with data: https://en.wikipedia.org/wiki/List_of_European_financial_services_companies_by_revenue

The syllabus for a program like this will give you some idea: https://www.udacity.com/course/data-analyst-nanodegree--nd002

Data Analyst Nanodegree

Anyone notice the Data Analyst Nanodegree switched from self-paced to a fixed schedule with 2 Terms?

https://www.udacity.com/course/data-analyst-nanodegree--nd002

Does anyone have experience with this new layout? Any feedback? Seems more difficult to meet deadlines, compared to the self-pace style before.

Also, the start date for Term 1 seems to be 9/12/17. Anyone know when Term 2 starts?

Here you go.

The link is from a mobile browser but I don't think that should cause any problems. I do know that Udacity's programs and costs differ slightly depending on the country you're in... That may be why you weren't able to find it?

in my opinion the B.S. Data Management/Data Analytics is better than the B.S. Software Development

it has a "Data Structures" class, which the SD degree does not have

it covers Python and Java and R

you get the Udacity Data Analyst Nanodegree - https://www.udacity.com/course/data-analyst-nanodegree--nd002 (included in the base WGU price -- so basically free)

you cover a lot more Oracle -- you get two more database courses

• Database Server Administration
• Data Wrangling with MongoDB

the only advantage of the SW degree is that is has these two courses

• Mobile Application Development
• Operating Systems for Programmers

and the SD degree let's you choose between Java and C#

the Data degree only requires Algebra (just like the SD degree)

Too many courses, confusing terminology! Where to begin with NLP?!

Problem

I want to learn Machine Learning, specifically NLP (Natural Language Processing) for a news analysis project I am working on.

For a person with intermediate programming knowledge and basic knowledge of working with databases, what would be the correct beginning point? There are so many courses available online on different platforms that it's confusing to identify where I should begin.

Existing Skill

I learned programming through the Python specialization on Coursera which taught me about data structures, extracting data from the web, analyzing it and visualizing it. The course established a pretty strong programming foundation but left much to desire when it came to analysis... There was little to nothing about statistics, and from what I've come to know till now Machine Learning requires one to have solid basics in Statistics.

To give you a more granular idea of my current skill level, here's the paper I wrote for my capstone project: https://paper.dropbox.com/doc/News-Analysis-Methodology-fXyowV7zSRAxKA70kxAwP

Options

I am currently looking at Udacity to further my skill but I am getting confused by their different courses on Data Science, Machine Learning, Deep Learning and Artificial Intelligence. There appears to be so much overlap in these courses that it's hard for me to decide what exactly I need.

I don't want to waste time going down the wrong path.

Sirven los MOOCs en Argentina?

Buenas a todos!

Estuve viendo algunas páginas de MOOCs como Coursera y Udacity y tienen cursos que se ven interesantes, en Data Science y Data Analytics por ejemplo (que son los que me interesan a mi). La cuestión es, sirven en Argentina los títulos que entregan esos MOOCs para conseguir trabajo? O es preferible pagar un curso en la UTN u otra institución local (que valen más o menos lo mismo)?

EDIT: hago este edit, para indicar que me refiero a cursos como éste, éste o éste, que son bastante largos y parecen estar bien organizados

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?

I have not taken the nanodegree

but its $399 PER MONTH
https://www.udacity.com/course/data-analyst-nanodegree--nd002

they have a special right now where you can get 4 months for $1020

but the nanodegree is INCLUDED in the WGU tuition
so once you enroll in WGU the nanodegree is free
you just have to set it up with your mentor

which I have not done yet either

Fanduel MLB Data Exploration

Hey all,

I'm currently enrolled in Udacity's Data Science nanodegree. As part of that, one of the projects was to pick an interesting dataset to perform some exploratory data analysis on using the R programming language. I chose to explore some MLB statistics in relation to Fanduel Scores. Thought some of you might be interested. Posted here.

This analysis doesn't even come close to exploring all the available relationships, but was a fun project and I discovered some useful information to aid in building a predictive model.

Hope others enjoy it.

-Cole

Regarding Extracurricular activities and Udacity Nanodegree programs?

I'm in Finance, and Learning online things like machine learning from udacity and the certificates from udacity can be considered as extra curricular activities?

and also, There are two nanodegree programs related to Data. Data Analyst Nanodegree program(https://www.udacity.com/course/data-analyst-nanodegree--nd002) and Machine Learning engineer Nanodegree program(https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009). My question is, does Machine learning program covers the same material as in Data Analyst program and also goes more advance?

Udacity's nanodegree in data analytics is well reputed: https://www.udacity.com/course/data-analyst-nanodegree--nd002

There are also a handful of online Master's programs that are under $10k. That might take a year or two to complete, but once you've taken the intro level classes, you should have the skills to start working an analytics job, and you can put progress towards a Master's on your resume.

Lol. Part of the reason why I asked the question. But there are paths. I think that it would be best to choose one and move towards that. But there are commonalities that you could leverage that work in both.

Before getting into the specific 'roles' I would like to talk about how industry experience gives you an edge. For example, day you have worked 3 years in insurance. Then even if you have a little less experience on the data or analysis side, they might still hire you if you had enough experience on the data /analysis side because you already have industry experience and that is less training that they'll require. (So this tells you the path of least resistance is moving to a role that is in your same industry. Also, you can try to find a role that is a hybrid between your current experience and what you want to eventually do. Many jobs nowadays are a little broader. They may split time between hard accounting and finance /planning / budgeting. A role like that is closer to where you want to be. So moving to a role like that gets you in the neighborhood...to eventually get to where you want to go.)

The other thing to note before I get into specifics is that since you have extra time during the month at work you can try doing side projects with a team that you eventually would want to move to. Extra analysis to help them. Maybe a task force. Something like that. So if you have those extra hours you can volunteer those to do that extra work. So I might just ask if there is a project you could work on. Or keep an eye out for a project you can volunteer for. Or just meet people on those teams and learn about what they do. Just keep in mind politics. Also keep in mind your boss might get mad if they notice you do have extra time. So you might make it seem like you're staying late to do this 'extra work.' (Or just spend some extra time on that project)

Let's start with the data one:

First it used to be you could do a data analysis role without coding or much visualization experience. But those days are over. You have to know some sort of programing for many roles and some sort of visualization software to make the data sing.

You also need to understand databases and analytics. As well as having to be able to understand the business questions and interpret the results. So this means stastical analysis as well.

What I would strive for is a low level data science capability. By going in that direction you can become a data analyst. If you really want to do this and go back to school for it you can try something like Udacity. (Or a formal graduate program if you really want to spend the money and time)

Personally I might choose Udacity. But you can always just learn on your own as well but it would take some time.

https://www.udacity.com/course/data-analyst-nanodegree--nd002

You could also start reading some of the data science blogs or check out some of the data related subreddits. r/data and others.

Now onto the finance one:

As for the financial analyst route, you'll still need to understand data and presentations and the business, but the coding and statistics will not be as important. The understanding of financials will be more important.

This route feels like you are going into eventually a CFO or VP of FP&A kind of role.

Budgeting, reporting, financials, all of these things are relevant.. working with senior leadership across functions is important as well.

So this route is possible as well.

They would probably want you to have an MBA at some places while other places won't care.

Again, it's really about the company and the opportunity.

Some places really value a person with a CPA background but they do want them to be able to understand the business and think through things in that lens.

So an MBA is nice. Big 4 is nice. Consulting background. Investment Banking. Or really good financial experience with analysis outside of just the general ledger.

So for this role you can either look to get more training with a degree or find a job where it's a hybrid and work your way over to more and more finance work. (So maybe a non traditional accounting role at a smaller company...say mid size...that is looking for a good accounting person that can do analysis as well.)

I hope this helps if you have any other questions let me know.

Good luck.

Have you try doing the udacity nanodegree for data analyst? https://www.udacity.com/course/data-analyst-nanodegree--nd002

I think the course will only cost 1k to get a "certificate". I imagine it can only help and is not a huge investment. Unless you are about to be homeless, 2 months is relatively short for not working imo. Maybe starting worrying after 1 year...

You might consider looking into Udacity's Data Analyst Nanodegree for developing the technical skills for data analytics. I believe WGU incorporates it into their BS Data Management/Data Analytics degree.

Thanks for the response, I have the option of four classes. Is there anything else that may be useful? I've been mostly supplementing my learning through Udacity's Data Analytics nanodegree (the free version).

If you've been given a decent budget for training you could look at the Udacity courses, I've taken a few myself and they are really good.

It's almost like a module at university/college, you'll do several assignments and submit them. You'll have a tutor you can 1 on 1 ask, and there's a community forum for you to reach out to.

They typically take 2-4 months (they say it takes that but if you put more hours in you could do them in 1-2 months easy).

https://www.udacity.com/course/data-analyst-nanodegree--nd002

Is one that might interest you: "Python, SQL, and statistics to uncover insights, communicate critical findings, and create data-driven solutions."

They're not cheap, its £329 a month. But, if you've been given money from the business to train they're very high quality and you will have several completed projects at the end of it.

Help critique my ML curriculum!

Hi everyone! I've been doing some research on how to get started on machine learning as a beginner and have come up with the curriculum below for myself to follow. It would be great if those proficient in ML, or even those currently learning, help to critique or provide suggestions. Additional courses, literature, programs, or advice would be greatly appreciated! It may be difficult to critique without having firsthand experience of each course or program, but I'm more concerned the with subject matter included than the courses themselves. As I go through each course, I may decide to change them if I feel that it is not pertinent to the goal or lacking in quality. There's no way I will become an expert after this, but I'm simply looking for a good starting point. I hope this will help others as well.

 

As a point of reference, I've graduated with a B.S. in Mechanical Engineering, have taken several math courses (calculus, linear algebra, differential equations), and have taken a few programming courses (CS50, Intro to Comp Sci on eDX, CS 101 at University). I'm currently working part-time as a mechanical designer / energy analyst and have 40-50 hours a week to spare.

 

For the curriculum, I've broken it up into 5 sections (programming, statistics, linear algebra, data science, and machine learning) which will be overlapping. I plan on taking 3 courses at a time starting with Udacity's Intro to programming, Stanford's probability and stats, and Udacity's linear algebra refresher while reading "An Introduction to Statistical Learning."

 

Here it is!

 

Curriculum:

Programming

1. Udacity Introduction to programming https://www.udacity.com/course/intro-to-programming-nanodegree--nd000
2. Learn the basics of programming through HTML, CSS, and Python.

Statistics

1. Stanford Online: Probability and Statistics https://lagunita.stanford.edu/courses/course-v1:OLI+ProbStat+Open_Jan2017/about

2. Broken into four sections: exploratory data analysis, producing data, probability, and inference.

3. Stanford Online: Statistical Learning http://online.stanford.edu/course/statistical-learning-winter-2014

4. Introductory-level course in supervised learning, with a focus on regression and classification methods.
5. Lectures cover all the material in An Introduction to Statistical Learning, with Applications in R.

Linear Algebra

1. Udacity Linear Algebra Refresher Course with Python: https://www.udacity.com/course/linear-algebra-refresher-course--ud953

2. UAustinX Linear Algebra - Foundations to Frontiers edx.org/course/linear-algebra

3. Connections between linear transformations, matrices, and systems of linear equations.
4. Partitioned matrices and characteristics of special matrices.
5. Algorithms for matrix computations and solving systems of equations. Vector spaces, subspaces, and characterizations of linear independence. *Orthogonality, linear least-squares, eigenvalues and eigenvectors

Data Science

1. Udacity Introduction to Data Science https://www.udacity.com/course/intro-to-data-science--ud359

2. Focuses on Data Manipulation, Data Analysis with Statistics and Machine Learning, Data Communication with Information Visualization, and Data at Scale -- Working with Big Data

3. Udacity Data Science Nano-degree https://www.udacity.com/course/data-analyst-nanodegree--nd002

4. Learn to organize data, uncover patterns and insights, make predictions using machine learning, and clearly communicate critical findings.

5. (optional) UCSD Data Science Micro-masters Program (4 course program) https://www.edx.org/micromasters/data-science

6. Four courses: Python for Data Science, Statistics and Probability in Data Science using Python, Machine Learning for Data Science, and Big Data Analytics Using Spark.

Machine Learning

1. Udacity Intro to Machine Learning https://www.udacity.com/course/intro-to-machine-learning--ud120

2. How to extract and identify useful features that best represent data, learn a few of the most important machine learning algorithms, and how to evaluate the performance machine learning algorithms.

3. Coursera Machine Learning by Andrew Ng https://www.coursera.org/learn/machine-learning

4. This course provides a broad introduction to machine learning, data-mining, and statistical pattern recognition.

5. (with job guarantee?) Udacity Machine Learning Nano-degree https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009

6. Apply predictive models to massive data sets in fields like finance, healthcare, education, and more.

 

Literature:

1. An Introduction to Statistical Learning http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Seventh%20Printing.pdf

2. The Elements of Statistical Learning: Data Mining, Inference, and Prediction https://web.stanford.edu/~hastie/ElemStatLearn/printings/ESLII_print12.pdf

 

Other (after curriculum):

https://www.reddit.com/r/MachineLearning/wiki/index

 

Let me know your thoughts! Have you taken one of these courses before? Think there's too little programming or know of a better course/program? Chime in! Would be really grateful for any feedback.

Big Thanks!

-sleepyowl

Gracias por tu respuesta.

He hecho estos cursos:

Curso de Python en Codecademy

https://www.codecademy.com/learn/learn-python

Automate the Boring Stuff with Python

https://automatetheboringstuff.com/

Me he estudiado partes del Natural Language Tool Kit

https://www.nltk.org/

Ahora quiero estudiar:

Text Mining with Python

https://www.coursera.org/learn/python-text-mining

Y después quiero obtener el NanoDegree de Data Analyst

https://www.udacity.com/course/data-analyst-nanodegree--nd002

Aun así, creo que una Ing. me daría bases sólidas y teoría de la computación y me ayudaría bastante además de darle más seriedad a mi objetivo.

>aormiston

Linkedin and upwork seem like good places to me, do you agree? Also, what does everyone think about udacity's paid courses I noticed they added new carreer services like linkedin and resume reviews. Do you think it may be a good investment for me? $400 for the whole thing apparently.

How much statistical analysis with SPSS did you do? If you're willing to do a lot of self-teaching and if you're willing to hack at problems until they're complete, try checking out the material in this Data Analyst nanodegree from Udacity: https://www.udacity.com/course/data-analyst-nanodegree--nd002

The skills from the Data Analyst nanodegree are in demand, and very importantly - you get to leverage SOME of the skills you learned in school.

The sad truth, however, is that a bootcamp course, or a udacity nanodegree, or a coursera certification alone won't help you break into any one of these fields. I, personally, am a Computer Science Undergrad who is graduating this semester and I had to interview with many companies before I got any offer for full-time entry level work. The job market isn't as great for entry level roles as many people might have you think.

What worked for me is what works for most people to break into entry level roles - have real projects that you can show off to prospective employers. If you choose the Data Analysis route, it'd be worth your time to even do some pro-bono work (or otherwise severely under-priced) for local non-profits or charitable causes, if possible.

I am currently in the program, but it's actually in the IT College. I just went to the business college website and it's not listed there (only MBA, MSML, and MSA).

As for your requirement to get into the program with Business Management, I'm going to assume no because managerial isn't very quantitative and I don't think there are much IT courses in that major. It's not really a STEM degree based on the list that they provided. I got accepted with a BS in Economics. >Possess a bachelor’s degree in a STEM field, Business degree (Quantitative Analysis, Accounting, Economics, Finance, or degree with similar quantitative focus).

>Possess any bachelor’s degree PLUS one of the following: Two years of related work experience Relevant and current IT certification Related IT coursework

The program itself is VERY statistic heavy--if you had a BS in Business Statistics (as shown on the STEM degree list), then yes. However, I think your best bet is to to contact the enrollment counselor and see what they say after submitting your transcript.

If you're serious about being accepted into this program, I've read that if you do Udacity nanodegree for Data Analyst, then that would help you cover to be accepted. But again, I would consult with the enrollment counselor. Good luck!

Have you looked at Udacity's Data Analyst Nanodegree Program - https://www.udacity.com/course/data-analyst-nanodegree--nd002

Not university based, but I found it comprehensive and challenging. There are assignments and case studies that you can work on. It is a bit on the pricier side compared to udemy and others but I did the course and I liked their structure and the course material. They also have resume services to help you write a DA resume.

Please bear in mind, while it is comprehensive it is learning more from a business / work side of things. It omits quite a few statistical concepts and python coding if you're after an in-depth learning

Working in data migration; What careers could I pursue given my situation / background?

Hello,

I am seeking advice on what career I can work towards, that I have some hope of getting hired in.

Current Situation:

I have been working at a small software company doing "data migration". The data migration involves moving the data of our clients' into our CRM / non-profit management software. The tools I use are excel and the import functionality of the software. (I have looked up data migration, and the pros seem to use ETL tools, python scripting and a lot of the transfer seems to be done over the backend (i.e. through MySQL, say) and not the GUI.

The upside is, that I like my job. I get to:

• Manage the relationships with clients
• Problem solve (I have to understand our software and it's functionality, which is vast, and understand the client's needs and processes / use cases and put the data in a place that will provide them with that)
• Multi-task - at any given time, I am working on at the very least 10 migrations at various stages, on my own; I'm coordinating scheduling meetings with multiple clients; as well as working on improving our processes
• Developing policy, processes and training - I have been put in a unique position, where I was brought on when 2 of my predecessors had left abruptly. (We're a small company, that doesn't pay well). I have been contributing heavily towards developing the department from the ground up.
• Designing a piece of software - I have proposed a pieces of software that would help reduce the time migrations take, by a large amount. My bosses have liked it and will be hiring developers to build it.
• Management / Project Management - I have two colleagues in my department, but they are overseas, and they are undertrained and about as new to the company as me (they started a couple months earlier). I have been partly managing them by instituting (upon my own initiative) various things like daily scrum-style meetings, some protocols for our tasks, reviews / discussion of our work and a lessons-learned process (this coming week). I'm also fairly certain that data migrations count towards hours to achieve a project management certification (PMP).
• Not exactly relevant, but I also enjoy the: Flexibility to work from home; I get to walk to work every day, 35 mins each way, which is perfect and good for my health; Work in a casual work environment with co-workers who are the right mix of professional (personally responsible) and personable / easy to get along with. Including my managers. My superiors seem to like me...I think.

I feel really happy because I think I am getting a gold-mine of experience, even in just the ~ 6 months I have been at this role.

Background:

• 30 M
• BSc in Physics w/ 2.3 GPA on a 4.3 scale - Started the degree at 18, graduated at 25, took two years off to work at Tim Horton's in between...sigh.
• I took 4 semesters of programming / data structure classes (1 class per) which is relevant
• Spotty work history - this is my first real job. Before this, I was a volunteer coordinator and spent a year renovating my parents' house / building a kitchen from scratch. This is two years' worth of "work" with years of unemployment.
• Took a fair few independent study courses during these years in financial services, business admin and one in project management. I also volunteered.

I mention the above not as a sob-story, but to highlight my un-employability / summarize my resume. Getting this job was a miracle to say the least.

Future Prospects?

I am trying to seize the opportunity I've been given. I'm also hoping I can purvey this into a career for myself. I'm just not sure what that is. Some options I am considering (don't know how realistic...that's part of why I'm posting):

• Project Management Certification (PMP): I will be getting this no matter what, because I am getting the experience I need, as we speak.
• Data migration: learn python, MySSQL / (maybe some real-time updating database system too, like Cassandra), teach myself how to use ETL tools.
• Data sciencey / Big data / Data analytics stuff: This the one that is most unlikely, I guess, but I also feel like it is something I should consider with the way things are going in the industry and my background in math (from physics, and as my minor). I'm considering this certificate, but I don't have the 70% minimum average required. I also found this certificate, but I know it's not the best, really. It might give me the knowledge, but not the prestige/connections. I don't know if one needs a degree in data science, maybe even graduate degree to get in, but I'm thinking that if I stay at my company, and bring this skill-set to them, I might be able to gain some experience in a growing company. They don't really need a data scientist yet, but they might. If I can get over some personal hang-ups regarding my problem solving skills as far as math is concerned, this is the option I am most interested in.
• Programming - the least likely, and least attractive of the options.

Question

• Based on my background and current situation, do you think any of the above options are reasonable / something that I could look into pursuing?
• Based on the same, do you have any other suggestions / ideas for career paths I might consider pursuing?

Thank you for your time.

TL;DR: In my first real job at 30, after a stagnant and rather shameful youngadulthood, doing data migration. Currently embracing my job and doing reasonably well. Should I pursue data migration, project management, data science / analytics, programming or any combination of the above? Do you have any suggestions for something else I could consider?

EDIT: changed a word and formatting.

Best online data science/analytics non-degree program?

Looking for recommendations. I am a Data Analyst II proficient in Tableau and SQL. Looking to strengthen and expand my existing skills set. In my opinions these seem to be the best programs offered right now....thoughts?

Udacity: https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009 https://www.udacity.com/course/data-analyst-nanodegree--nd002

FutureLearn: https://www.futurelearn.com/programs/big-data-analytics

Beginner to completion time?

Hi all,

I’m hoping to matriculate into medical school next fall but am seeking a a stable job for the next year or so. I have a BA in economics that included advanced stats coursework and projects. I also have a bit of experience using Python and SPSS in my research. I’m seeking advice on how to get started and assume it will be easier to start as a data analyst. I’ve been sending out my resume with no luck but figure COVID is at least playing a role. Does anyone have an estimate for how long it will take to get hired? Also, how long after completing intro courses like these can I get hired? I’m currently a PT tutor but am looking for more hours and higher pay. I’m wondering if I should take a customer FT service job or just dedicate all my time to learning these tools. All advice is greatly appreciated!

https://www.udemy.com/course/complete-python-bootcamp/?LSNPUBID=JVFxdTr9V80&ranEAID=JVFxdTr9V80&ranMID=39197&ranSiteID=JVFxdTr9V80-rKxfnF1FoCy3TcLlaIGzfg

https://www.udemy.com/course/the-complete-sql-bootcamp/

https://www.udacity.com/course/data-analyst-nanodegree--nd002

Without knowing any of your interest, I would suggest going back to school since it will help you long-term. In my opinion, I don't know if a full four year program is required so hear me out on the following idea, and let me know if all of these sound completely boring to you. I suggest you look into a nanodegree in:

• Data Analytics: This is one of the hottest jobs in the market right now. I am a data scientist and we are in high demand. You're using a company's data (financial, HR related, web analytics, survey data, etc.) and pulling insights, building predictive models, or creating visualization with it. There is a ton that you can do and learning to code for data analytics is a great skill. These nanodegrees are ~$1,000 and there are also FREE options at codeacademy and other websites.
• Digital Marketing: Google/social media has turned marketing around on it's head. In the past marketers would do campaigns to the general public to find customers, now websites use SEO and social media to target people looking at their products. This will help you kick start all of that information.
• Front End Development: All the websites you've ever interacted with, have all of the code in order to make it look nice and be functional. This program will give you the skills in HTML and CSS in order to be able to do front end development work and build websites. If you're really into girls, you can try out the free version (no nano degree from this website though, but as the lesson progresses and you get answers correct the instructor losses articles of clothing hahaha) called Code Babes
• Full Stack Web Development: Websites and webapps have two components to them, the front and back end. Think about a restaurant there is the kitchen area (which you generally don't see) and the dining room/bar (which you do see). Web apps/sites have both what you interact with and what happens in the background in order for your requests on the site to be successful. If you hit "submit" on a form, there is a backend process that occurs before you get a confirmation message. This will teach you to do both front and backend work.

Udacity isn't the only place that offers these, do some research and see what options there are (and make sure it's a degree of high demand). If you learn some of this on your own you may be able to use it to help your current role, and put that on your portfolio/resume in order to land a job that requires some sort of experience.

These skills in digital are in very high demand, and the pay can be very lucrative. Just an option/idea for you to consider.

Hope it's helpful.

It's the "Data Analyst" nanodegee: https://www.udacity.com/course/data-analyst-nanodegree--nd002

Getting that nanodegree won't get you any course credit, though -- an oracle cert will. Transfer guidelines here: https://partners.wgu.edu/Pages/MSDA.aspx

thanks for your positive words.

I have been applying crazily as well.

but I am unable to get any response from these big companies.

is it not possible to catch their eyes, with my Udacity projects?

because , they are the only data science/data analytics stuff I have to show on my resume.

and frankly, even any other company its not getting much positive result as to hiring.

again, so do you think its impossible to get the big companies call up with Udacity projects?

FYI, these are the courses i am talking about.

1 )udacity data analyst

2) udacity data science

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also , with respect to white board coding,

i have found some options.

1) interviewkickstart ----expensive + they say its for people who are having some good experience.

2) outco ---they had a interview with me on algorithms, but I wasnt able to crack it to get enrolled for the course.

3) interviewcamp.io ---this one seems fine with pricing, and also , the guy is usually active on quora

his quora site --https://www.quora.com/profile/Harsh-Goel

his linkedin --- https://www.linkedin.com/in/harsh-goel-076756b

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any other suggestions as well, please shoot at me if u can :D

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