Getting into Data Science FAQs
I often see similar questions in my inbox or asked in webinars. I'd like to set the record straight with data. However, I didn't need to start from scratch, there was an excellent article on KD Nuggets by Jeff Hale. Here is the link to his article: "The Most in Demand Skills for Data Scientists". He had already scoured multiple job search site and aggregated data on the demand for different skills for data scientist. I recreated some of his analysis myself, so that I could come up with some points for this article, and to make sure his numbers matched mine before posting. The charts I created below are based on data from searches on indeed.com only. A search for "Data Scientist" was the denominator, and the numerator would be "Data Scientist" plus another term I was looking to see results for. I'm not sure how many job descriptions listed on indeed.com might be duplicates, so this is not gospel, but still interesting.This article will cover a couple of "Frequently Asked Questions" using the methodology above (that was adopted from Jeff).
Questions I'm frequently asked:
Should I learn R or Python?
As a Computer Science major, can I get into data science?
How important is SQL?
Should I learn R or Python?This would most likely be the most frequently asked question (although I've never analyzed the questions that I'm asked). In Jeff's article, you were able to see that Python has the edge in terms of coming up in job listings. I recreated the methodology for myself to look at this a little further.55% of the job listings actually list both tools, as in the company would like to see that you have experience with "Python and/or R". That should make those who have a preference for one tool feel better. If you're looking to pick up either R or Python and you're just getting your hands dirty, I'd suggest python. For listings that only specify one tool, Python is almost 5x more likely to be listed as the tool of choice compared to R.I was happy to see this, as I've mentioned in a number of webinars and comments on social media that it "feels like" Python is slightly more popular. It would have been a bummer if I had been giving misinformation this whole time.
% of Data Science Positions Mentioning a Particular Skill on Indeed.com
Pulled this data by doing a search on indeed.com 11/2018
As a Computer Science major, can I get into data science?I'm always surprised when I see this question, because as someone who's been in the field for a long time, it just seems clear that this is a fantastic skill as your foundation for moving into data science. Data science requires a number of different skills to be successful, and being able to program is definitely one of the core pillars. Analytics and Statistics are coming in first, but Analytics and Statistics could easily be mentioned somewhere in the job description other than specifically where preferred degrees are mentioned. If a job description says "computer science" they're most likely speaking to the degrees they would prefer from candidates. More than 50% of job descriptions mention "computer science". There you have it, a degree in computer science is something "in demand" for getting into data science.
% of Data Science Positions Mentioning a Particular Skill on Indeed.com
Pulled this data by doing a search on indeed.com 11/2018
How important is SQL?I'm frequently asked this question, and I was honestly surprised that SQL came in third behind Python and R in terms of skills. However, 51% of jobs do mention SQL. So it is certainly desired for the majority of positions, but I expected it to rank higher. Is it possible this skill is becoming assumed as a prerequisite? Or are companies figuring that SQL is easily learned and therefore not necessary to list on the job description? I wouldn't mind a job where all the datasets were aggregated for me before data cleaning and applying machine learning, I'm just not sure how many of those jobs exist. If you're a data scientist, and you haven't had to understand relational databases at any point, let me know. I'd love to hear about it.Conclusion:We saw that Python is preferred over R, but that either tool will allow you to apply to the majority of data science jobs in the US. Computer science degrees are a great stepping stone to getting into data science, and the majority of listings will expect you to know SQL.I also want to point out that "communication" was very much in the top list of skills. 46% of job descriptions listed communication in the job description. This means I'll continue to keep writing about how I use softer skills to be effective in my job. I think we sometimes do not talk about communication enough in data science, it's really imperative to delivering models and analysis that are aligned with what the business is looking for. If you'd like to see how Jeff used the data from the job search websites to discuss most in demand skills, here is the link one more time. Link.
Trying to Change Careers or Get Your Start in Data Science?
If you’re someone who is looking to make a move to data science, there are some ways that you can polish your approach to get noticed during your job search.
Assuming that you've built up the skills required for the job see if you're able to leverage some of these tips:
Optimize your resume (as best you can) for the job you WANT not the jobs you’ve HAD.
Try to gain experience at your current job (if you’re a career changer), or work on your own data science projects at home. (continuous learning is a big plus).
Develop a killer elevator pitch.
Optimizing your resume for the job you want:
Describe your projects in a way that shows you’re results-focused.
The points you’re going to want to demonstrate on your resume need to both:
Demonstrate that you understand general corporate culture, and showcase your collaborative, result achieving, problem solving and self-managing competencies.
Show that you have the technical chops as a data scientist.
The first bullet takes a lot of thought - it is really easy to list job duties, it’s another thing to reword them effectively to highlight your true strengths and demonstrate how what you've done has improved the business. Your bullet points should be full of action verbs and results, even if you need to stretch yourself mentally to identify these.
Did you automate a process that saved hours of time manually doing a task? That time saved is business value.
Demonstrating that you've worked cross-functionally or presented results to the business are again, things that are desirable for the new job you want (data scientist).
It is helpful to read job descriptions and see what companies are looking for, you'll find consistent themes. If you look closely, you'll see there are a lot of skills listed that aren't necessarily technical. Make sure you shine when speaking to those softer skills. But of course, these softer skills need to be demonstrated in a way that still demonstrates an action and result. Do not just put a "soft skills" section on your resume and list a bunch of words with no context.
"Show you have the technical chops as a data scientist". This is pretty straight-forward. Try to use the verbiage from the actual job description for the job you're applying to. You might want to sound fancy, but “empirical bayesian 3-stage hierarchical model” probably isn’t on the job description. Having this specifically listed on your resume isn’t going to help you pass ATS (the applicant tracking system), and the person in human resources who doesn’t have a data science background is not going to know whether that is relevant or not. Again, looking at multiple job descriptions and trying to gauge what type of language to use on your resume is helpful.
Gain experience at your current job or work on a project:
If you currently have a job, do you have access to SQL? Does your company have a data warehouse or database? Can you file a ticket with the service desk to get SQL? Can you then play with data to make your own project?
You could even go a step further and bring data from the database into R or Python. Maybe you make a nice decision tree that answers a business questions then wonderfully and concisely place your results of your project on your resume.
Try to automate a task that’s repeatable that you do on a regular cadence. That’s next level resume content. You’re increasing efficiency in this scenario.
If you’ve done data science projects on your own to round out your resume, make sure those bullets are full of action verbs and results, action verbs and results. I almost want to say it a third time.
SQL Lite is open source, R is open source, Python is open source, there is tons of free data out there. The world can really be your oyster, but you’ll need to market these go-getter skills effectively.
Develop a killer elevator pitch:
A strong, well-targeted resume might open the door, but you need to keep that door open and keep the conversation going once the door has been opened. The resume does nothing more than open the door, that’s it.
Getting your resume into the right hands can sometimes be difficult. Leveraging LinkedIn effectively can help bridge that gap. How do we begin the conversation if you’re reaching out to someone on LinkedIn to ask about opportunities?
Important note: When cold reaching out to people on LinkedIn, this should be after you have visited the company website, found a job that you’re interested in and (pretty much) qualified for, and then you reach out to a relevant person with a well-targeted message.
It is impossible to be well-targeted if you are reaching out to someone who works at a company that doesn’t have any positions available. Because you didn’t read a job description. So you wouldn’t be able to infer the needs of the business. Data Science is a large field, with many specializations, a blanket approach will not work.
Back to the pitch. You’re results-focused, you’re innovative, and you view things from the business’ perspective.
I'd suggest starting with something conversational, this will help if the person you're messaging is already being inundated with requests. A comment about a post they made recently makes your connection come across as more authentic.
Why you’re messaging: you’re interested in the open position, and you’re trying to get your resume to the correct person.
Then mention a number of things concisely that are specifically mentioned on the job description. Basically saying “hi, look at me, I’m a fit.”
Let them know that you’d really appreciate it if they’d simply forward you to the correct person (hopefully the person you’re messaging is the correct person, but there is also a chance it’s not the right person, so don’t assume).
Close strong. You’re here to add value for the company, not to talk about your needs; imply you’re aware that you’re here to talk about how you can fit the needs of the business.
Hi [name],
I enjoyed your recent post on [topic] and I look forward to reading more of your posts.
I noticed [company] is hiring for [position title], and I’m hoping I can get my resume in the right hands. I have an MS in Statistics, plus 7 years of real-world experience building models. I’m a wiz at SQL, modeling in R, and I have exposure to Python.
I’d appreciate the opportunity to speak with the appropriate person about the open position, and share how I’ve delivered insights and added value for companies through the use of statistical methods.
Thanks, Kristen
Now you may have a very different background from me. However, you can talk about the education that you do have (concisely), the exposure that you do have to building models, about your technical chops, and that you want to deliver value.
I hope that you’ll be able to use some of these suggestions. And I wish you a successful a rewarding career in data science. If you have additional suggestions for trying to make a change to data science, I’d love to hear your thoughts! The next article I post will be covering how to write crisp content for your resume that makes an impact, that article is here.
Favorite MOOCs for Data Scientists
I had asked on LinkedIn recently about everyone’s favorite MOOCs in data science. This post started a lot of great discussion around the courses (and course platforms) that people had experience with. Certain courses were mentioned multiple times and were obviously being recommended by the community.Here was the post:Biggest takeaway:
Anything by Kirill Eremenko or Andrew NG were highly regarded and mentioned frequently.
So I decided to revisit this post, and aggregate the information that was being shared so that people who are looking for great courses to build their data science toolkit can use this post as a starting point.You’ll notice that below Coursera had the most mentions, this is mostly driven by Andrew Ng’s Machine learning course (11 mentions for that course alone) and Python For Everybody (6 mentions, also on Coursera). Similarly, Kirill has a number of courses on Udemy that all had multiple mentions, giving Udemy a high number of mentions in the comments as well. (Links to courses are lower in this article).The 2 blanks were due to one specific course. “Statistical Learning in R” it is a Stanford course. Unfortunately I wasn’t able to find it online. Maybe someone can help out by posting where to find the course in the comments?
Update! Tridib Dutta and Sviatoslav Zimine reached out within minutes of this article going live to share the link for the Stanford Course. There was also an Edx course that was recommended that is not currently available, “Learning From Data (Introductory Machine Learning)" so I won’t be linking to that one.
If you’re familiar with MOOCs, a number of platforms allow you to audit the course (i.e. watch the videos and read the materials for free) so definitely check into that option if you’re not worried about getting graded on your quizzes.To make the list, a course had to be recommended by at least 2 people (with the exception of courses covering SQL and foundational math for machine learning, since those didn’t have a lot of mentions, but the topics are pretty critical :).I've organized links to the courses that were mentioned by topic. Descriptions of courses are included when they were conveniently located on the website.
Disclaimer: Some of these links are affiliate links, meaning that at no cost to you, I’ll receive a commission if you buy the course.
SQL:
“Sabermetrics 101: Introduction to Baseball Analytics — Edx”“An introduction to sabermetrics, baseball analytics, data science, the R Language, and SQL.”
“Data Foundations” — Udacity“Start building your data skills today by learning to manipulate, analyze, and visualize data with Excel, SQL, and Tableau.”
Math:
“Mathematics for Machine Learning Specialization” — Coursera“Mathematics for Machine Learning. Learn about the prerequisite mathematics for applications in data science and machine learning.”
Tableau:
“Tableau 10 A-Z: Hands-On Tableau Training for Data Science!” — Udemy (This is a Kirill Eremenko course)
R:
“R Programming” — Coursera “The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code.”
"R Programming A-Z™: R For Data Science With Real Exercises!" — Udemy (This is a Kirill Eremenko course)"Learn Programming In R And R Studio. Data Analytics, Data Science, Statistical Analysis, Packages, Functions, GGPlot2"
If you're looking for the best R course that has ever existed, read about my favorite R programming course. I wouldn't call it a MOOC, because you have direct access to the instructor through Slack. But if you're serious about learning R, check this out. Link
Python:
“Python for Everybody Specialization” — Coursera“will introduce fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language.”
Python for Data Science:
“Applied Data Science With Python Specialization” — Coursera
“Python for Data Science” — Edx “Learn to use powerful, open-source, Python tools, including Pandas, Git and Matplotlib, to manipulate, analyze, and visualize complex datasets.”
Machine Learning:
“Machine Learning” — Coursera (This is an Andrew Ng course)
“Machine Learning A-Z™: Hands-On Python & R In Data Science” — Udemy (This is a Kirill Eremenko course)
“Python for Data Science and Machine Learning Bootcamp”— Udemy “Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!”
Deep Learning:
“Deep Learning Specialization” — Coursera (This is an Andrew Ng course)" In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more.”No one had anything bad to say about any particular course, however, some people did have preferences in terms of platforms. You can read the original post yourself here.I hope these courses help you widdle down the plethora of options (it’s overwhelming!) and I hope you learn some great new information that you can apply in your career. Happy learning!
Favorite MOOCs for Data Scientists. Data science courses. Data Science resources. Data analysis, data collection, data management, data tracking, data scientist, data science, big data, data design, data analytics, behavior data collection, behavior data, data recovery, data analyst. For more on data science, visit www.datamovesme.com
Beginning the Data Science Pipeline - Meetings
I spoke in a Webinar recently about how to get into Data Science. One of the questions asked was "What does a typical day look like?" I think there is a big opportunity to explain what really happens before any machine learning takes place for a large project. I've previously written about thinking creatively for feature engineering, but there is even more to getting ready for a data science project, you need to get buy in on the project from other areas of the business to ensure you're delivery insights that the business wants and needs.It may be that the business has a high priority problem for you to solve, but often you'll identify projects with a high ROI and want to show others the value you could provide if you were given the opportunity to work on the project you've come up with.The road to getting to the machine learning algorithm looks something like:
Plenty of meetings
Data gathering (often from multiple sources)
Exploratory data analysis
Feature engineering
Researching the best methodology (if it's not standard)
Machine learning
We're literally going to cover the 1st bullet here in this article. There are a ton of meetings that take place before I ever write a line of SQL for a big project. If you read enough comments/blogs about Data Science, you'll see people say it's 90% data aggregation and 10% modeling (or some other similar split), but that's also not quite the whole picture. I'd love for you to fully understand what you're signing up for when you become a data scientist.
Meetings: As I mentioned, the first step is really getting buy in on your project. It's important that as an Analytics department, we're working to solve the needs of the business. We want to help the rest of the business understand the value that a project could deliver, through pitching the idea in meetings with these stakeholders. Just to be clear, I'm also not a one woman show. My boss takes the opportunity to talk about what we could potentially learn and action on with this project whenever he gets the chance (in additional meetings). After meetings at all different levels with all sorts of stakeholders, we might now have agreement that this project should move forward.
More Meetings: At this point I'm not just diving right into SQL. There may be members of my team who have ideas for data that I'm not aware of that might be relevant. Other areas of the business can also help give inputs into what variables might be relevant (they don't know they database, but they have the business context, and this project is supposed to SUPPORT their work).There is potentially a ton of data living somewhere that has yet to be analyzed, the databases of a typical organization are quite large, unless you've been at a company for years, there is most likely useful data that you are not aware of.
The first step was meeting with my team to discuss every piece of data that we could think of that might be relevant. Thinking of things like:
If something might be a proxy for customers who are more "tech savvy". Maybe this is having a business email address as opposed to a gmail address (or any non-business email address), or maybe customers who utilize more advanced features of our product are the ones we'd consider tech savvy. It all depends on context and could be answered in multiple ways. It's an art.
Census data could tell us if a customers zip code is in a rural or urban area? Urban or rural customers might have different needs and behave differently, maybe the extra work to aggregate by rural/urban isn't necessary for this particular project. Bouncing ideas off other and including your teammates and stakeholders will directly impact your effectiveness.
What is available in the BigData environment? In the Data Warehouse? Other data sources within the company. When you really look to list everything, you find that this can be a large undertaking and you'll want the feedback from others.
After we have a list of potential data to find, then the meetings start to help track all that data down. You certainly don't want to reinvent the wheel here. No one gets brownie points for writing all of the SQL themselves when it would have taken you half the time if you leveraged previously written queries from teammates. If I know of a project where someone had already created a few cool features, I email them and ask for their code, we're a team. For a previous project I worked on, there were 6 different people outside of my team that I needed to connect with who knew these tables or data sources better than members of my team. So it's time to ask those other people about those tables, and that means scheduling more meetings.
Summary: I honestly enjoy this process, it's an opportunity to learn about the data we have, work with others, and think of cool opportunities for feature engineering. The mental picture is often painted of data scientists sitting in a corner by themselves, for months, and then coming back with a model. But by getting buy in, collaborating with other teams, and your team members, you can keep stakeholders informed through the process and feel confident that you'll deliver what they're hoping. You can be a thought partner that is proactively delivering solutions.
Tips for starting a data science project. Data analysis, data collection , data management, data tracking, data scientist, data science, big data, data design, data analytics, behavior data collection, behavior data, data recovery, data analyst. For more on data science, visit www.datamovesme.com.
The Successful Data Science Job Hunt
The point of this article is to show you what a successful Data Science job hunt looks like, from beginning to end. Strap-in, friends. I’m about to bring you from day 1 of being laid-off to the day that I accepted an offer. Seriously, it was an intense two months.I have an MS in Statistics and have been working in Advanced Analytics since 2010. If you’re new to the field, your experience may be different, but hopefully you’ll be able to leverage a good amount of this content.We’re going to cover how I leveraged LinkedIn, keeping track of all the applications, continuing to advance your skills while searching, what to do when you receive an offer, and how to negotiate.
Day 1 Being Laid-off
dyed my hair bright pink before job hunting
Vistaprint decided to decrease it’s employee headcount by $20 million dollars in employee salary, I was part of that cut. I was aware that the market was hot at the moment, so I was optimistic from day 1. I received severance, and this was an opportunity to give some real thought about what I would like my next move to be.I happened to get laid-off 4 days after I had just dyed my hair bright pink for the first time, that was a bummer.I actually went to one job interview with my pink hair, and they loved it. However, I did decide to bring my hair back to a natural color for the rest of my search.
Very First Thing I Did:
I am approached by recruiters pretty frequently on LinkedIn. I always reply.Although if you’re just getting into the field, you may not have past messages from recruiters in your LinkedIn mail, but I mention this so that you can start to do this throughout the rest of your career.Now that I was looking, my first action was to go through that list, message everyone and say:“Hi (recruiter person), I’m currently looking for a new opportunity. If there are any roles you’re looking to fill that would be a good fit, I’d be open to a chat.”
There were a number of people that replied back saying they had a role, but after speaking with them, it didn’t seem like the perfect fit for me at the moment.In addition to reaching out to the recruiters who had contacted me, I also did a google search (and a LinkedIn hunt) to find recruiters in the analytics space. I reached out to them as well to let them know I was looking. You never know who might know of something that isn’t on the job boards yet, but is coming on soon.
First Meeting With the Career Coach
As part of the layoff, Vistaprint set me up with a career coach. The information she taught me was incredibly valuable, I’ll be using her tips throughout my career. I met with Joan Blake from Transition Solutions. On our first meeting, I brought my resume and we talked about what I was looking for in my next role.Because my resume and LinkedIn had success in the past, she did not change much of the content on my resume, but we did bring my skills and experience up to the top, and put my education at the bottom.
They also formatted it to fit on one page. It’s starting to get longer, but I’m a believer in the one page resume.I also made sure to include a cover letter with my application. This gave me the opportunity to explicitly call out that my qualifications are a great match with their job description. It’s much more clear than having to read through my resume for buzzwords.I kept a spreadsheet with all of the companies I applied to. In this spreadsheet I’d put information like the company name, date that I completed the application, if I had heard back, the last update, if I had sent a thank you, the name of the hiring manager, etc.This helped me keep track of all the different things I had in flight, and if there was anything I could be doing on my side to keep the process moving.
Each Application:
For each job I applied to, I would then start a little hunt on LinkedIn. I’d look to see if anyone in my network currently worked for the company. If so, they’d probably like to know that I’m applying, because a lot of companies offer referral bonuses. I’d message the person and say something like:Hey Michelle,I’m applying for the Data Scientist position at ______________. Any chance you’d be willing to refer me?
If there is no one in my network that works for the company, I then try and find the hiring manager for the position. Odds are it was going to be a title like “Director (or VP) of Data Science and Analytics”, or some variation, you’re trying to find someone who is a decision maker.This requires LinkedIn Premium, because I’m about to send an InMail. My message to a hiring manager/decision maker would look something like:
Hi Sean,I’m interested in the remote Data Science position, and I’m hoping I can get my resume in the right hands. I have an MS in Statistics, plus 7 years of real-world experience building models. I’m a wiz at SQL, modeling in R, and I have some exposure to Python.I’d appreciate the opportunity to speak with the appropriate person about the open position, and share how I’ve delivered insights and added value for company’s through the use of statistical methods.Thanks, Kristen
Most people actually responded, Joan (the career coach) was surprised when I told her about my cold-calling LinkedIn success.
I Started Applying to Jobs, and Started Having “Phone Screens”
Phone screens are basically all the same. Some were a little more intense and longer than others, but they were all around a half hour, and they’re typically with someone in HR. Since it’s HR, you don’t want to go too deep in the technical stuff, you just want to be able to pass this stage, follow up with a note thanking them for their time, and try to firm up when you’ll be able to speak with the hiring manager :)Tell me about yourself:People just want to hear that you can speak to who you are and what you’re doing.
Mine was some variation of:
I am a Data Scientist with 7 years of experience using statistical methods and analysis to solve business problems across various industries. I’m skilled in SQL, model building in R, and I’m currently learning Python.
What are you looking to do?I’d make sure that what I’m looking to do ties directly to the job description. At the end of the day, it was some variation of:
“I’m looking to continuously learn new tools, technologies and techniques. I want to work on interesting problems that add business value”.
Then I’d talk about how interesting one of the projects on the job description sounded.What are you looking for in terms of salary?Avoid this question if you can, you’ll be asked, but try to steer in a different direction. You can always reply with “I’ve always been paid fairly in the past, I trust that I’ll be paid fairly working for [insert company name]. Do you have an idea of the salary range for the position”.They’ll know the range for the position, but they’ll probably tell you that they don’t. Most of the time I’d finally concede and give them my salary, this doesn’t mean that you won’t be able to negotiate when you receive an offer.
All The While, I’m Still Learning, And Can Speak to This in Interviews:
If I was going to tell everyone that I was very into learning technologies, I better be “walking the walk” so to speak. Although I am constantly learning, because it’s in my nature. Make sure that if you say you’re learning something new, you’re actually studying it.
The course I took was: Python for everybody
Disclaimer: This is an affiliate link, meaning that at no cost to you, I will earn a commission if you end up signing up for this course.
This course goes over your basic lists, arrays, tuples, defining a function.. but it also goes over how to access and parse web data. I had always wanted to know how to access Twitter data for future analysis, so this was super cool. The specialization (that’s the name they give for a series of courses on Coursera) also gives a brief overview in how to construct a database. This was a super bonus for me, because if I want to operationalize a model, I’m going to want to know how to write from Python to a database table. All-in-all, I found this course to be a great use of my time, and I finished it being able to speak to things intelligently, that I was not able to speak to prior to taking the course.
In Person Interviews:
I've written a whole article on in person interviews: here
At some point, you might receive a call saying they plan on putting an offer together for you, if you're still interested.Great! You’ve got an offer coming. At this point, you want to call all the other companies that you would consider an offer from and say “I’ve been informed that I am expecting an offer, is there anything you can do to accelerate your process?”I mentioned this to 2 companies. One of them did speed up their process and it resulted in an additional offer. The other company said that they would not speed up their process, I thanked them for their time and said I'd hope to cross paths in the future.
Negotiating:
The phone rings, and you answer. This is it, you’re getting your first offer. It’s time to negotiate. Only a relatively small percentage of people ever negotiate their salary, the percentage is even smaller when we’re talking about women.Ladies! Negotiate! I’m here rooting for you, you got this.Joan from Transition Solutions had coached me on this. She said “Don’t try and solve the problem for them”.When they call, let them know how excited you are that they called, and that you’re interested in hearing their offer.
Once you’ve heard the salary, vacation time, and that they’re going to send over the benefits information, you can say something along the lines of:
"Thank you so much for the offer, I really appreciate it. You know, I was hoping that you could do more on the salary."
Then wait for a response, and again be positive. They’ll most likely say that they need to bring this information back to the hiring manager."
Great! I look forward to hearing back from you. I’ll take some time to look over the benefits package. Want to speak again on ____. I’m feeling confident that we can close this."
Then you’d be walking away from the conversation with a concrete time that you’ll speak to them next, and you let them know that you were happy to hear from them, all of this is positive!I successfully negotiated my offer, and started a week later. I couldn’t be happier with where I am now and the work I’m doing. It took a lot of applying and a lot of speaking with companies who weren’t “the one”, but it was worth it.To sum up my job search. I learned that a targeted cover letter and directly applying on a company website greatly increase the response rate on your applications.
I learned that you can effectively leverage LinkedIn to find the decision maker for a position and they’ll help keep the process moving if you’re a good fit. I also gained a ton of confidence in my ability to articulate my skills, and this came with practice. I wish you lots of success on your hunt, and I hope that there was a couple of tips in this article that you are able to use :)
The successful data science job hunt. Data analysis, data collection , data management, data tracking, data scientist, data science, big data, data design, data analytics, behavior data collection, behavior data, data recovery, data analyst. For more on data science, visit www.datamovesme.com.