Most In-Demand Data Scientist Skills

Top Skills Required for Data Scientist

Introduction

Data science is a highly lucrative field ever since data became important to the global corporate world. As a matter of fact, organizations hire Data Scientists to help them interpret raw data so that they can formulate business policies and implement marketing strategies to boost their revenue. Some of these organizations are even ready to pay a premium on any Data Scientist that can proffer a solution to their problems.

For you to be hired and retained by reputable organizations as a Data Scientist, you need to possess both technical and soft data science skills and also display a high level of meticulousness on the job. So, ride with us as we explore these skillset.

Technical Skills for Data Scientist

1. Python Coding

Python coding is an important skill required to become a successful data scientist. It is very common and works along with C/C++, Perl, or Java. According to a survey by O’Reilly, more than 40 percent of those that responded asserted that Python was their major coding language.

As an entry-level Data Scientist, Python would make you versatile, as you would be able to engage in different data science processes. Plus, this skill would make it easy for you to import SQL tables as you write your programs.

2. Hadoop Platform

According to a study by CrowdFlower on over 3000 data science-related jobs on Linkedin, Hadoop is the second most important technical skill demanded by employers or recruiters.

While this skill is not a heavy requirement for entry-level positions, most reputable firms require that their prospective Data Scientists possess Hadoop skills, at least at an intermediate level.

Data Scientists leverage Hadoop to convey and distribute big data to different points in a system. They also use this skill to explore, filter, and sample data.

As such, reflecting Pig or Hive in your resume or cover letter would stand you out from the crowd. Also, a strong knowledge of cloud tools would make your resume more professional

3. SQL Database/Coding

While Hadoop and NoSQL are an integral part of data science, prospective job seekers are required by employers or recruiters to not only write but implement complex queries in structured query language (SQL).

Possession of this skill will give you a robust understanding of different data science operations like delete, add, subtract, and extract. The skill will also help you transform different structures of databases and help you understand relational databases.

4. Apache Spark

Apache Spark is one of the most sought-after data science technical skills. Possession of this skill could land you a job in one of the big firms in the country.

Apache Spark is very similar in operation to Hadoop, only that it is faster than the former and caches its computations directly to a memory.

Apache Spark helps data scientists to run and handle complex algorithms even when dealing with unstructured data sets. That’s not all; Apache Spark is also very useful when preventing data loss.

This skill will boost your profile and will help you carry out data analytics from the intake of data to a point where you can successfully distribute the data to different systems.

5. Machine Learning and AI

Let’s face it; so many Data Scientists lack machine learning and artificial intelligence skills; hence their difficulty in securing a good job.

If you want your resume to be outstanding as a Data Scientist, you need both machine learning techniques like decision trees, supervised machine learning, time series, Outlier detection, natural language processing, computer vision, survival analysis, recommendation engines, adversarial learning, reinforcement learning, as well as logistic regression. These skills are in high demand and experienced professionals are smiling to the bank each year.

6. Data Visualization

Corporate organizations generate huge data daily, as such; they need Data Scientists to translate the data into meaningful insights.

Data Visualization skills will enable you to handle data using tools like Tableau, Matployylib, d3.js, and ggplot. These tools are designed to enable you to convert complex data into a form where a novice can comprehend.

Employers would not understand what p values or serial correlation represents; it is your job to explain the business angle of the result to them so that they act when a business opportunity props up.

7. Unstructured data

Data Scientists are expected to handle unstructured data, as such; this skill is also in high demand like machine language and artificial intelligence. Basically, unstructured data are not defined and may not align with database systems.

Examples of unstructured data are blog posts, videos, social media posts, customer reviews, an audio file, and video feeds, among others.

Sorting unstructured data may prove difficult without the technical know-how to handle them. As a fresh graduate who is seeking a good job in the field of data science, you need this skill to enable you to manipulate complex and raw data from several platforms.

Soft Skills for Data Scientist

Let’s take a look at some of the non-technical skills required by employers or recruiters.

1. Intellectual curiosity 

The English dictionary defines curiosity as the desire to seek more knowledge. Intellectual curiosity is a virtue that should be incorporated into every sphere of our lives.

As a data scientist, you would be spending most of your time preparing data, as such; you need to be curious about seeking more knowledge in the field of data science.

How will you seek more knowledge? Simple, you read relevant books online and participate in the current trend in the industry. You can also join professional organizations where people like you meet to discuss the way forward in the industry.

2. Business acumen 

The business world is very vast. Whether agriculture, healthcare, solid mineral or the information technology industry, you need business acumen to succeed as a Data Scientist.

Strive to know everything about the industry you would be delivering your services and pay attention to details. As an entry-level data scientist, you can learn this skill on the job and as you climb the corporate ladder.

3. Communication skills 

The need for data scientists to possess communication skills cannot be overemphasized. Remember companies hire Data Scientists to translate unstructured data into meaningful insights.

How will a data scientist who doesn’t possess communication skills report technical findings to his/her employers at the end of a project? A Data Scientist must be able to communicate clearly (both written and oral) so that employers can make a business decision.

When communicating your findings to your employers, ensure you pay attention to the values and results of the data you analyzed.

A lot of business owners are not really interested in the type of data you collected and analyzed, their interest is how the result of the data would positively impact their business. As such, sharpen your communication skills so that your resume will stand out.

4. Teamwork

Another great skill you should possess as someone who’s looking to carve a niche in the field of data science is teamwork.

You will not be a solo worker regardless of the industry you work in; you need other professionals and customers to succeed in your job. Collaborating with other professionals will give you an understanding of the required data to address critical issues.

If you have any questions, please ask us here – we answer every single query of students.

Final Thoughts

There you have it! Data science is an interesting and lucrative field if you have the required skill sets. From Python coding, Hadoop, SQL coding Apache Spark to Machine learning, artificial intelligence, and data visualization, all these skills are in high demand by employers or recruiters.

If you are lacking in any of these skills, try and horn your horizon so that you can compete favorably with others seeking the same position as you. If you have any concerns, feel free to get in touch with us via the comment box below.

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