Skills You Should Learn For Data Science In 2019.

Every once in a while, comes an era of change. New trends bloom and traditional methods face extinction. Technology is one such ecosystem of constant change.

In the 21st century, the top is full of cut-throat competition but Data Science has managed to make it to the top being coined as the sexiest job of the 21st century.

India alone has witnessed a 400% rise in the demand for Data Science professionals and according to a report over 50,000 jobs are vacant.

Read the entire report to know further.

But learning data science can cost an arm and a leg. The Google search for ‘data science courses’ displays 45,30,00,000 results. As much as we might be tempted to master this course, the cost is often a stepping stone.

So let us talk about the standard tools. Let us see what are the most in-demand skills you should know as a data scientist.

Machine learning, statistics, quantitative analysis, mathematics, and programming languages as well. With such a gamut of talked-about skills, one might be confused. But the skills in demand are structured by the work being done in the industry.

Referencing to reports, Analysis and Machine learning are the most sought-after skills by employers.

“It’s like a game, and it is all about deriving insights”.

Machine learning models are universally used to build models which are used for deriving insights from data. While it is quite evident that analysis and computing skills are prerequisites, it is interesting to see that a lot of emphases is given to communication skills.

Maybe it’s time you start reading some books by Dale Carnegie.

Deep learning doesn’t show up as often but we can predict deep learning to become congruent with machine learning algos in the future.

Moving on- the Technical Skills.

You can start honing your grip on these tools, libraries, and languages right away.

Python

The probable ‘lingua franca’ of coding, python is used quite unanimously. This open-source language with a host of open-source libraries is seemingly popular in the community, is easy to understand and learn, and is considered as the primary language for data scientists.

Read: How Boredom Led to The Creation of Python.

R

Most programmers are learning ‘R’ for Data Science and ML applications. Although Python has gained massive popularity amongst communities, R isn’t far behind.

R as a programming language burst into the frame owing to its robust support for statistics, clustering methods, regression techniques, and graphical methods; the language has become very popular within the statistician and data scientist community.

So knowledge of R, Python or both is among the prerequisites for a Data Scientist.

According to the report, the job market is witnessing an exciting situation- the number of job seekers is half of the total number of available jobs. So the opportunities are plenty.

Now we know the knowledge of R or Python is right, to begin with.

SQL

Considered as the primary way to interact with relational databases, Structured Query Language is popular among the employers. When you have a relationship between variables, SQL is used predominantly. It offers two advantages: you can access many records with one single command and it eliminates the need to specify the method to reach out for a specific record.

So SQL is a skill worth demonstrating when you step in the competition.

Tableau

This analytics and data visualization tool is sturdy, easy to use and highly in demand. If you are not familiar with this tool, you can opt for one of the quick online sessions like DATA VISUALIZATION USING TABLEAU.

Although Tableau offers a free public version, you will have to use the paid version to keep your data private.

“Tableau allows us to step out of the box and look at data in a totally different way.”Kevin King, Director, Reporting, and Analytics, Coca-Cola Bottling Company.

Hadoop and Spark

Both these open source tools from Apache, are used extensively for big data.

Apache Spark is an open-source, distributed, general-purpose, cluster-computing framework. Spark is primarily used for large-scale data processing, data engineering, and analytics.

‘The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.’

Although famous, a general observation tells that these tools have a fewer number of candidates using them as compared to Python, R, and SQL, but these tools will give you an upper hand in interviews.

To sum up, here are a few general tips:

Enhance your communication skills. There are a lot of hacks to do it. Choose the one which suits you the best.

Get your hands dirty with a deep learning framework. Although this is a superficial aspect of being proficient with machine learning, it might prove to be the deciding factor in interviews.

If you are confused between Python and R, choose Python. And for the whiz kids out there, learning both is always an option.

Employers don’t just look at a candidate who knows Python alone, you are expected to have an understanding of the standard python data science librariesnumpy, pandas, scikit-learn, and matplotlib.

You can try some online platforms to get started with Deep Learning like the Deep Learning Online Certificate Program.

You can start with Keras before jumping to TensorFlow or Pytorch.

All said and done, you can personally go through a myriad of other options available online and learn what interests you, and suits you the best.

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