Skills In Data Science That Will Get You Hired

GUPTA, Gagan       Posted by GUPTA, Gagan
      Published: June 10, 2021
        |  

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In no particular order, let's get to know the Top Skills in the field of Data Science. Together these skills will put you foremost in the job market, ahead of everybody. These skills will make sure that you are ready for the new technology trends and more significant challenges in the field of Data Science.

Programming Skills

No matter what type of company or role you're interviewing for, you're likely going to be expected to know how to use the tools of the trade. This means a statistical programming language, like R or Python, and a database querying language like SQL. Even though NoSQL and Hadoop have become a large component of data science, it is still expected that a candidate will be able to write and execute complex queries in SQL. One need to be proficient in SQL and advanced Database concepts. Very often the Data Science programs runs for many hours, if not days. Often, the data to be processed is in Terabytes or Petabytes.

Statistics

Good understanding of statistics is vital as in all fields of Data Science. You should be familiar with statistical tests, distributions, maximum likelihood estimators, etc. This will also be the case for machine learning, but one of the more important aspects of your statistics knowledge will be understanding when different techniques are (or aren't) a valid approach. Statistics is important at all company types, but especially data-driven companies where stakeholders will depend on your help to make decisions and design / evaluate experiments. Probability with the help of statistical methods helps make estimates for further analysis. Statistics is mostly dependent on the theory of probability.

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 (e.g. Netflix, Google Maps, Uber), it may be the case that you'll want to be familiar with machine learning methods. This can mean things like k-nearest neighbors, random forests, ensemble methods, and more. It's true that a lot of these techniques can be implemented using R or Python libraries-because of this, it's not necessary to become an 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 & Linear Algebra

Understanding these concepts is most important at companies where the product is defined by the data, and small improvements in predictive performance or algorithm optimization can lead to huge wins for the company. In an interview for a data science role, you may be asked to derive some of the machine learning or statistics results you employ elsewhere. Or, 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 when there are so many out of the box implementations in Python 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.

Big Data

Although this isn't always a requirement, it is heavily preferred in many cases. Having experience with Hive or Pig is also a strong selling point. Familiarity with cloud tools such as Amazon S3 can also be beneficial. A study carried out by CrowdFlower on 3490 LinkedIn data science jobs ranked Apache Hadoop as the second most important skill for a data scientist with 49% rating.
As a data scientist, you may encounter a situation where the volume of data you have exceeds the memory of your system or you need to send data to different servers, this is where Hadoop comes in. You can use Hadoop to quickly convey data to various points on a system. That's not all. You can use Hadoop for data exploration, data filtration, data sampling and summarization.

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Skills In Data Science That Will Get You Hired
Skills In Data Science That Will Get You Hired

Data Wrangling

The data you're analyzing is going to be messy and difficult to work with. Because of this, it's really important to know how to deal with imperfections in data. Some examples of data imperfections include missing values, inconsistent string formatting (e.g., 'New York' versus 'new york' versus 'ny'), and date formatting ('2017-01-01' vs. '01/01/2017', unix time vs. timestamps, etc.). This will be most important at small companies where you're an early data hire, or data-driven companies where the product is not data-related (particularly because the latter has often grown quickly with not much attention to data cleanliness), but this skill is important for everyone to have.
Unstructured data are undefined content that does not fit into database tables. Examples include videos, blog posts, customer reviews, social media posts, video feeds, audio etc. They are heavy texts lumped together. Sorting these type of data is difficult because they are not streamlined.

Model Building and Deployment

Model building is at the core of executing data science initiatives. Data Science jobs require understanding of multiple modeling techniques, model validation, and model selection techniques. They also need to know how to deploy a validated model and monitor it to maintain the accuracy of results.

Some specific types of skills associated with model building include:

- A predictive mindset
- An understanding of predictive techniques (regression, classification) and why to use them.
- Critical thinking about attributes
- Understand how to interpret results and validate a model (K fold, leave one out)

Top-performing data scientists are differentiated by their ability to understand the use of different modeling methodologies to obtain insights from data that translate into value for the business.

They are also able to confidently defend their analysis and explain what they did and how their technique works.

Data Visualization & Communication

Visualizing and communicating data is incredibly important, especially with young companies that are making data-driven decisions for the first time, or companies where data scientists are viewed as people who help others make data-driven decisions. When it comes to communicating, this means describing your findings, or the way techniques work to audiences, both technical and non-technical. Visualization-wise, it can be immensely helpful to be familiar with data visualization tools like matplotlib, ggplot, or d3.js. Tableau has become a popular data visualization and dashboarding tool as well. It is important to not just be familiar with the tools necessary to visualize data, but also the principles behind visually encoding data and communicating information.

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.

DevOps

DevOps is a set of methods that combines software development and IT operations that aims to shorten the development life cycle (SDLC) and provide uninterrupted delivery with high software quality.

DevOps teams closely work with the development teams to manage the lifecycle of applications effectively. Data transformation demands close collaboration of data science teams with DevOps. DevOps team is expected to provide highly available clusters of Apache Hadoop, Apache Kafka, Apache Spark, and Apache Airflow to tackle data extraction and transformation.

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Data Intuition

Companies want to see that you're a data-driven problem-solver. At some point during the 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. How should you, as the data scientist, interact with the engineers and product managers? What methods should you use? When do approximations make sense?

Communication

Along with being able to create great visualizations to communicate results to end users, Data Scientists must possess persuasive communication skills and strong interpersonal skills to see a project from start to finish.

In their role, they may have to interact with a variety of personalities and stakeholders from technical IT and software engineers to marketing managers and other functional staff to C-suite managers. Certainly, to progress in the ranks as a Data Scientist, communication skills need to be strong.

Problem Solving Skills

Data Scientists should have a rigorous data-driven problem-solving approach to their thinking. Top Data Scientists are able to discern which problems are important to solve and then model what is critical to solving the problem.

There's no template for solving a data science problem. The path to solving a business problem changes with every new dataset.
In addition, the practice of data science is riddled with challenges like missing data values, uncooperative stakeholders and coding bugs.
Data Scientists need to be comfortable with this uncertainty of the job.

Business acumen

To be a data scientist you'll need a solid understanding of the industry you're working in, and know what business problems your company is trying to solve. In terms of data science, being able to discern which problems are important to solve for the business is critical, in addition to identifying new ways the business should be leveraging its data.
To be able to do this, you must understand how the problem you solve can impact the business. This is why you need to know about how businesses operate so you can direct your efforts in the right direction.

59% of all Data Science jobs are in BFSI and IT industry.

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