Top Skills That a Data Scientist Should Possess!

Introduction

A data scientist’s role is of utmost value in today’s world. They are one of the most sought after people.

The value that they bring to a firm is unparalleled and that is why they are paid top dollar. A few skills that every data scientist should possess are listed below:

1. Learning and Education

Data scientists and highly educated are synonyms.  Nearly 90% of them have at least a masters and approximately 50% of them have PhDs.

There may be a few exceptions to the case but a very strong educational background is definitely a must due to the depth of knowledge that is required.

The most important fields of study required to possess the skills to do data analytics are – Math and Statistics, Computer Science and Engineering.

However, it does not with finishing a bachelor’s degree. Most data scientists have a masters or a PhD to their name.

Apart from this, special training courses like learning to use Hadoop or big data querying is also required. 

A master’s in data science, mathematics or a related field would be ideal. Apart from classroom learning, putting the learning into practice is the best form of learning. Probably build an app, explore data analysis by reading whitepapers and case studies.

2. Programming with R

Solid knowledge of analytical tools is a must. When it comes to data science, R is what is preferred. R is designed with data science needs in mind.

Today, nearly 45% of data scientists use R to solve their statistical problems. However, the learning curve for R is very steep.

Mastering R after learning another programming language may be difficult but, there are plenty of learning options available online.

3. Coding with Python

Out of the many coding languages like C++, Java and Perl, Python is the most important and most common as is seen in the various data science roles.

A survey conducted by O’Reilly revealed that Python was the most preferred programming language.

Python’s versatility lends itself to be used in all steps involved in a data science process. Various formats of data can be accommodated and SQL tables can also be imported in the code. Datasets can also be created in Python.

4.Knowing the Hadoop Platform

Knowing the Hadoop platform is not necessity as such. However, it is preferred in many cases and most job roles.

Knowing Hive and Pig is also an added advantage. Having cloud tools such as Amazon S3 in your arsenal I also a good thing.

In a recent study, from data gathered from a sample size of 5000 linkedin jobs, it was revealed that Apache Hadoop was the second most important skill for data scientist.

Hadoop comes in especially when the volume of data is huge and exceeds the memory of your data or when your data needs to be sent to different servers.

5. SQL Database/Coding

Knowing that NoSQL nd Hadoop constitute a large component of data science, it is still expected from a candidate that they should be able to write and execute complex queries in SQL. SQL proficiency is always required for a data scientist.

SQL is used in particular to access, communicate and work on data. Adding SQL to your arsenal will help you better understand relational databases and will boost your profile as a data scientist.

6. Learning Apache Spark

Apache Spark is one of the most popular big data technologies and some say it has eclipsed Hadoop in this aspect.

Spark is faster than hadoop in terms of functionality. It is faster because, Hadoop reads and writes to dis whereas the computations deduced by Spark are cached in its memory.

Apache Spark’s boon to data science is that it helps run complicated algorithms faster.

Complex unstructured data sets are can be easily handled with Spark. Spark can be used on one machine or a cluster of machines. Apache Spark also prevents loss of data.

7. Machine Learning and AI techniques

Many of the data scientists are not very proficient with machine learning techniques. To stand out from the crowd, knowing machine learning techniques like decision trees, supervise machine learning, logistic regression etc. is an absolute must.

The use of machine learning will allow you to make use of prediction to solve problems. A survey revealed that only 15% of the data scientist were capable in time series, NLP, outlier detection, survival analysis, recommendation engines and supervised machine learning.

Learning these techniques will definitely help you earn top dollar. With data sets so huge, machine learning will definitely help.

8.Data Visualization Tools

The data that is produced by a business is vast and requires to be translated into an easily comprehendible format.

The entire picture in the form of graphs and charts are the requirement. A data scientist should be able to visualize the data that he has analyzed.

To aid in this, visualization tools like Shiny, Power BI, D3js and Tableau should be mastered. Serial correlation or p values are not understood by everyone, it must be shown in terms of graphs and charts to be understood.

Data visualization allows you to glean insights from the data that you have analyzed.

9. Unstructured data comprehension

The most crucial part of a data scientist’s role is that they should be able to work with unstructured data.

Data that doesn’t fit in with database tables is unstructured data. These may be videos. Blogs, audio, social media posts etc.

Sorting these types of data is a huge challenge as it is not streamlined. Unstructured data is also known as ‘dark analytics’ due to its complexity.

A data scientist should be able to find patterns and manipulate unstructured data from various platforms.