10 Paramount Data Science Skills that will Get Your Hired
The CEO of Lattice, Shashi Upadhyay, once referred to data scientists as unicorns. In additional says, “Professionals with a diverse skill set that is not commonly found in a single individual.” This statement says it all when it comes to understanding and evaluating why data scientists are so valued, and what makes it a challenging job.
Presently, a plethora of companies have realized the value of a data-driven business strategy and need talented individuals to offer insight into a constant stream of collection of informative data. Indeed, there has been a tramp in data science jobs. According to a report from IBM, “openings for data-driven positions will rise to 2.7 million by 2020, and the demand for analysts will only grow as we continue to digitize our physical world.”
Leverage the use of big data, as an insight-generating engine has driven the demand for data scientists at enterprise-level, across all industry verticals. Whether it is to refine the process of product development, boost customer retention; analyze customer preference trends, or business development, data scientists are required to perform all these jobs.
Organizations are increasingly relying on the expertise of data scientists to sustain, outdo their competitions and grow. And, if you are also planning to pursue a career path as a data scientist, you need to understand the inside-out of data science.
Some online educational portals offer specialized and customized programs to candidates, who want to pursue a career as a data scientist.
Here is a list of technical skills that should be in the bucket of every aspiring data scientist
R is not far behind Python. It is an open-source statistical software package that streamlines the analysis of large data sets. Non-linear modeling, linear modeling, time-series analysis, and clustering are some features on the list. R continues to grow in popularity and along with Python, is one of the most common skills listed in data science jobs.
R also allows the data scientist to perform statistical and predictive analysis on real-time data, and then create interesting visuals to communicate that information in terms of business perspective, the report noted.
Python coding is known as one of the most common coding languages. Furthermore, it is an essential element of data science job demand, along with Perl, C++ or Java. The popularity of this open-source language has been widely observed and felt by professionals. Python is the primary language for data scientists.
Apache Hadoop is a reliable and connective open-source software platform for distributed processing of very large data sets on computer clusters built from commodity hardware. It is a fast, in-memory data processing engine with expressive and elegant development APIs to enable data workers to efficiently execute streaming, SQL workloads or machine learning that require fast iterative access to datasets.
In other words, it is a software framework that sustains and processes heavy volumes of data across clusters of computing devices. In addition to this, it is scalable, flexible, and allows companies to identify market trends and forecast outcomes to boost decision-making.
It is possible to grab an opportunity in the Data Science field with limited Hadoop experience, having a strong understanding of the framework is a key selling point that can lead to better opportunities and a better salary package.
“Virtually every industry is collecting data with the intent to drive value and growth. And data scientists with the right mix of skills can best interpret this information,” the report said. “So whether you’re breaking into the data science field or giving your tech skills a boost, it’s a good idea to tailor your learning based on employer demand.”
- SQL Database
SQL is also in high demand. SQL stands for Structured Query Language and is the primary way to interact with relational databases. SQL is sometimes overlooked in the data science world, but it’s a skill worth demonstrating mastery of if you’re planning to hit the job market.
According to the report, “Give data scientists a way to access and manipulate large amounts of information found in a relational database management system.” It can capture and break down data as well as edit database tables and indexes to boost accuracy. SQL is counted as one of the fundament skills.
5. Machine Learning and Artificial Intelligence
Machine learning is known as the subfield of artificial intelligence (AI) that comprises computer systems using data and algorithms to teach themselves to make predictions without getting programmed to do so.
There are certain field will be the prime key to advance technologies, including customizing the customer experience in various areas – retail, e-commerce platforms, and online businesses. Self-driving cars are another key point of AI.
Machine learning demands a blend of data science, software engineering, and math. Moreover, machine learning skills involve programming, data modeling and evaluation, system design, computer science fundamentals, probability and statistics, algorithms and libraries, and software engineering. In other words, it requires an extensive skill set.
Predictive learning and machine learning are turning out to be the hottest topics in terms of data science. Although not every analyst works with machine learning, the concepts and tools to go ahead in the field is an important aspect. But yes, you will require having your statistical programming skills down first to succeed in this area.
If you want to work for companies that deal with a colossal amount of data or an e-commerce business, you need to have familiarity with machine learning methods. Gigantic names like Google Maps, Uber, Zomato and Netflix require intelligent minds having data science skills.
- Data Visualization
Being able to narrate a persuasive story through data is vital to spread your point across and keep your targeted audience active. If you as a firm are unable to identify outcomes from data, it may get difficult to achieve things. Thus, data visualization has the power to make-or-break effect in terms of the impact of potential data.
Visualizing and communicating is quite essential, especially with promising companies that work around data-driven decisions for the first time or require talents that can help a firm make a data-driven decision. When it comes to communicating, this means describing your findings or the way techniques work to audiences, both in terms of non-technical or technical.
Analyzing the data and crafting strategies to use them in the best way possible is an art. Indeed, it demands an extraordinary set of skills. Fortunately, some online educational platforms offer engaging and career-centric courses as well as placement assistance. Identifying the data science career path to find a promising job and boost your career graph is the key. These portals can help in the struggle.
Do you know the most important technical skill needed to become a data scientist? It is the power to analyze statistics and leveraging computer frameworks. In addition to this, value out unstructured bulk of data is another skill required.
You must have skills such as a strong grip on mathematics; statistics and programming are also on the list. Having a strong foundation to connect with the technical aspects that form the core of the Data Science field is also need. Familiarity with statistical tests, maximum likelihood estimators and distributions of data is also significant. After all, statistics is imperative for data-driven companies where stakeholders depend on statistics.
Apart from these technical skills, two vital skills can be segregated as non-technical skills.
8. Strong Communication Skills
This is first on the list. As a data scientist, you can understand the data better than anyone else in a firm. But, for you to be successful in your role, you need to successfully communicate your thoughts to someone, who is a non-technical user of data. Indeed, you need communication skills to excel as a data scientist.
9. Great Data Intuition
This is perhaps one of the most significant non-technical skills that a data scientist requirements. Great data intuition means identifying patterns in a situation when none are observable on the surface and knowing the presence of where the value lies in the unexplored pile of data bits. There are a plethora of educational platforms that cater to data science projects, which are an incredible way of enhancing skills and achieving a better career
10. Programming Skills
Irrespective of the fact, what type of company you are searching for, they expect you to know how to use the tools of the trade. This implies a statistical programming language, such as Python or database querying language like SQL. It also plays a key role in crafting a data scientist salary.
In a nutshell, a data scientist is expected to know a lot such as computer science, machine learning, statistics, data visualization, deep learning, and communication. The demand for data scientists has increased in the past years and the entire credit to the functioning of gigantic companies that are data-driven now.
Often, scrutinizing data can turn out to be complicated or messy to work with. Because of this, it is essential to know how to manage things with imperfections in data.
Understanding market trends and forecasting the changes is an overwhelming task for companies. Therefore, they look for experienced data scientists, who can manage, analyze, and evaluate data in an explicit manner.