Getting first Job as Data scientist

As you start to consider which career path in data is the right one for you, it’s important to establish those data fundamentals that are going to apply no matter which route you take.

The skills required for Data Science jobs depend on the specific focus of their work, as well as on the targeted goals of individual companies and the imperatives of different industries. An analytics profession might, for instance, deploy various techniques to mine data, use advanced statistical models to analyze that data, and then employ computer programming skills to design a predictive or interpretive algorithm.

“The most important takeaway I’ve gotten from this role is that it increasingly isn’t enough to just know SQL [programming], or to just know statistics, or to just know software engineering.”

When describing key data analytics skills, experts often cite the following:

Statistical and Quantitative Analysis

Learn Regression Analysis in 2 min | EduGradApplied statistics is a baseline skill in data analysis and business intelligence. Professionals commonly conduct trend analysis, A/B testing, correlation analysis, profiling, and analysis of maximum likelihood estimators. Interpreting large data sets often calls for advanced statistical models and techniques, like time series predictive analysis, Regression Analysisand decision trees, text analytics, and more. The online analytics publication Data Nami reports that quantitative analytics professionals were once recruited primarily into the finance sector, but companies from many different industries have since followed suit.

Advanced Mathematics

Analytics is grounded in mathematical methods and concepts. While statistics is perhaps the primary discipline used in data analysis. Linear algebra and multivariable calculus are the basis for a lot of the machine learning techniques data scientists and analysts are now using to mine and analyze very large and complex data sets.

Data Mining, Scraping, and Munging

Analytics professionals can scrape data from a myriad of sources, but it is not always useful or structured in a usable way. Data munging is the process of sorting and optimizing large data sets before they are analyzed, while data mining involves examining that data to generate new information. Analysts who mine data using machine learning can build and train predictive analytic applications for classification, recommendation, and system personalization.

Machine Learning

If you’re at a large company with huge amounts of data or working at a company where the product itself is especially data-driven, it may be the case that you want to be familiar with Machine Learning Techniques. This can mean things like k-nearest neighbors, random forests, ensemble methods —all of the machine learning buzzwords.

It’s true that a lot of these techniques can be implemented using R or Python libraries—because of this, it’s not necessarily a deal-breaker if you’re not the world’s leading expert on how the algorithms work. More important is to understand the broad strokes and really understand when it is appropriate to use different techniques.

Multivariable Calculus and Linear Algebra

If you are asked to derive some of the machine learning or statistics results you employ elsewhere in your interview. Even if you’re not, your interviewer may ask you some basic multivariable calculus or linear algebra questions, since they form the basis of a lot of these techniques. You may wonder why a data scientist would need to understand this material if there are a bunch of out of the box implementations in sklearn or R. The answer is that at a certain point, it can become worth it for a data science team to build out their own implementations in-house.

Data Visualization

Data Visualization tools and start creating your own Dashboards | EduGradIt is important for Data Analysts to be able to communicate data findings to colleagues and managers so they can make more informed decisions. Data visualizations built with Python libraries like Matplotlib, Pandas, Seaborn and ggplot can convey technical information in an accessible way. Programs such as Tableau and QlikView allow data analysts and scientists to explore data visually by creating 2- and 3-dimensional graphics.

Software Engineering

If you’re interviewing at a smaller company and are one of the first data science hires, it can be important to have a strong software engineering background.

You’ll be responsible for handling a lot of data logging, and potentially the development of data-driven products.

Think Like a “Data Scientist”

Companies want to see that you’re a data-driven problem solver. This means, at some point during your interview process, you’ll probably be asked about some high-level problem—for example, about a test the company may want to run or a data-driven product it may want to develop. It’s important to think about what things are important, and what things aren’t.

Data Science Job Scenarios

“Data Scientist” is often used as a blanket title to describe jobs that are drastically different. To help you navigate through multiple opportunities, let’s get an understanding of different types of data science jobs by looking at some common scenarios:

You’re the First Data Hire

The number of companies get to the point where they have a lot of traffic (and an increasingly large amount of data), and they’re looking for someone to set up a lot of the data infrastructure that the company will need moving forward. They’re also looking for someone to provide analysis. You’ll see job postings listed under both “Data Scientist” and “Data Engineer” for this type of position. In a situation like this, you’re liable to be one of the first data hires, so it’s likely less important that you’re a statistics or machine learning expert.

A data scientist with a software engineering background might excel in a role like this, where it’s more important that a data scientist make meaningful data-like contributions to the production code and provide basic insights and analyses.

Mentorship opportunities for junior data scientists may be less plentiful, so as a result, you’ll often have great opportunities to shine and grow via trial by fire.

You’re the Scientist and the Analyst

There are many companies where being a data scientist is synonymous with being a data analyst. Your job might consist of tasks like pulling data out of MySQL databases, becoming a master at Excel pivot tables, and producing basic data visualizations (e.g., line and bar charts).

You may on occasion analyze the results of an A/B test or take the lead on your company’s Google Analytics account. A situation like this is a great opportunity for an aspiring data scientist to learn the ropes. Once you have a handle on your day-to-day responsibilities, a company like this can be a great environment to try new things and expand your skillset.

The Company is Data-Driven

A lot of companies fall into this bucket. In this type of role, you’re probably joining an established team of other data scientists.

Generally, these companies are either looking for generalists or they’re looking to fill a specific niche where they feel their team is lacking, such as data visualization or Machine Learning. Some of the more important skills when interviewing at these firms are familiarity with tools designed for ‘big data’ and experience with real-life datasets.

Despite that, all of these job postings would likely say “Data Scientist,” so look closely at the job description for a sense of what kind of team you’ll join, what kinds of challenges you’ll face, what kind of opportunities for growth you’ll have, and what sorts of skills you’ll need to develop.

As you start to consider which career path in data is the right one for you, it’s important to establish those data fundamentals that are going to apply no matter which route you take.

The skills required of analytics professionals depend on the specific focus of their work, as well as on the targeted goals of individual companies and the imperatives of different industries. An analytics profession might, for instance, deploy various techniques to mine data, use advanced statistical models to analyze that data, and then employ computer programming skills to design a predictive or interpretive algorithm.

“The most important takeaway I’ve gotten from this role is that it increasingly isn’t enough to just know SQL [programming], or to just know statistics, or to just know software engineering.”

Let’s start by examining why a portfolio is effective in the first place.

1. What are the most important data scientist skills and tools and how can you get them?

The skills that they teach you at the universities in 90% of the cases are not really useful in real-life data science projects. In real projects these 4 data coding skills are needed:

  • bash/command line
  • Python
  • SQL
  • R
  • (and sometimes Java)

It really depends on the company you are going for. But if you’ve learned one, it will be much easier to learn another.

So the first big question is: how can you get these tools? Here comes the good news! All of these tools are free! It means, that you can download, install and use them without paying a penny for them. You can practice, build a data pet-project or anything!

2. How to learn?

There are 2 major sources of learning data science — easily and cost-efficiently.

1st: Online courses and webinars.

Data science online complete courses are fairly priced and they cover various coding rounds and proper assignment and project engagement from basic coding to Advanced Data Analytics.

2nd: Books.

Kind of old-school, but still a good way of learning. From books, you can get very focused, very detailed knowledge of online data analysis, statistics, data coding, etc.

3. How to practice, and how to get real-life experience?

Every company wants to have people with at least a little bit of real-life experience… But how do you get real-life experience, if you need real-life experience to get your first job? The answer is pet projects.

We at EduGrad train freshers and professionals in Data Science and Machine Learning specific to industry requirements. We have curated courses in collaboration with ex-Google team to deliver best in quality video lectures, tied up with experienced SME’s to help our learners to get a good mentor support. We do have collaborated with well-known startups to get their Data set for our projects.

Be creative! Find a data science-related pet project for yourself and start coding! If you hit the wall with a coding problem — that can happen easily when you start to learn a new data language — just use Google and/or StackOverflow.

4. Where and how to send your first job application?

If you haven’t managed to find a mentor, you can still find your first one at your first company. This is going to be your first data science-related job, so the suggestion is not to focus on big money, rather focus on any Data science startup where you can groom your skills and improve yourself.

Taking your first data science job at a multinational company might not fit in this idea, because people there are usually too busy with their things so they won’t have the time or/and motivation to help you improve. LinkedIn may be a key that you should take to kick-start your career in Data Science. Build-up your LinkedIn profile and try to connect with human resource professionals from the Data Science Organization. You can apply for jobs on LinkedIn.

Starting at a tiny startup as a first data person on the team is a good idea either in your case because in these companies you can explore the things by yourself rather than relying on some senior data guys to learn from.

Hopefully, this would land you a job interview, where you can chat a little bit about your pet projects, your cover letter suggestions, but it will be mostly about personality fit-check and most probably some basic skill-test. If you had practiced enough, you will pass this.

5. Let employers know about your Portfolio

A portfolio is an extremely effective way of acting as a replacement for experience when looking for your first Data Science job. For your portfolio to be effective, however, you need to put some thought and effort into how you construct and present your work.

A common mistake is to place some projects on GitHub, and then simply add your GitHub profile URL to the top of your resume. Remember that the hiring process is hard, so you need to make it easy for those looking at your application to find and evaluate your portfolio.

Rather than just ‘adding the URL’ and hoping someone finds it, explicitly mention your portfolio and specific projects in your cover letter. If you get someone on the phone, mention your portfolio and how it shows how you can provide value to the business. Take every opportunity you can to put your portfolio forward.

Another effective approach is to list your portfolio projects on your resume as if they were short-term contracts (although be careful not to be deceptive). Give a short summary of the aim and the skills it demonstrates, and provide an easy-to-follow link.

It also helps to remember that generally speaking, you will encounter less technical people early in the hiring process and more technical people later on.

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