Top 15 Data Science Skills in 2020

6 Min Read. |

Healthcare and pharmaceuticals, the internet, the telecommunication sector, and the automotive industry are some of the top industries in and around for data science skills. But what data science is? What are the top 11 data science domains to learn in 2020?

The key to the lock of any ideal innovation is data science. It is because of the data science skills that researchers can anyhow get data insights using tools.

The alarming rate of how popular and necessary data science skills are becoming among us, the youth, in particular, asks us to stay up to the mark with the latest info regarding the field. 

Top 15 Data Science Skills in 2020

Below are the top 15 data science skills to learn in 2020:

1) GitHub

Given that you are familiar with data science courses, you must already know that GitHub is a widely used IT tool among developers at present.

Prime functions of GitHub data science skills for both individual and business groups include impactful control over code in multiple channels. Also, the smooth flow of codes and their accessibility anytime is because of GitHub.

Github’s special attributes of this data science skills are that it lets any collaboration, particularly within the team, with secured access. 

2) Agile

Agile is among the top data science skills for the year, where software development is focused on a base ground. It is a betterment field for projects and their management.

In this particular data science training, students are taught to evaluate minute changes while the software is in development.

Rather than building a whole new software program at once, Agile takes a step by step algorithm. 

Right before the project is approached with a final touch, the developers take reviews and alter the parts asked for a change. 

3) Programming(Python/R)

One of the most popular data science courses in Python, widely used by data scientists to create anything innovative from scratch.

Even after enhancing other programming languages such as Scala and Swift, Python/R  data science skills still retain the first position in the priority chart. Only able to do so because of its potential to ease the developer’s task.

4) SQL

SQL, the acronym of Structured Query Language, is the direct language that processes in the silos of data, flipping them into something like useful information. The developers then use this information. 

Any data expert with data science skills in both SQL and Python can let both programmings collide and function so that because of Python, SQL gives out the outcome in a faster phase. 

When SQL is in action, a meaningful insight with possibilities to reframe the data, provided that the developer was induced in a strong and effective data science training.

5) NLP, Neural Networks and Deep Learning

Natural Language Processing (NLP), Deep Learning, and Neural networks are the core data science skills before any data amateur turns into a data scientist. For the AI world has spiked higher now, the need for the three has simultaneously begun.

The function of Natural Language Processing is to manage interactions between computer data and humans. The chatbox, Google assistant, Siri, translation tools, and more similar features have NLP data science skills.

Neural Networks works as a solution to complex data. Best examples of artificial neural networks are reading image compression techniques, evaluating stock values beforehand, face, and speech distinction. 

This neural network is then used by Deep learning to find further data that are way more complex. This is the program that brings out the fraud and scam into notice. 

6) Math and Statistical Skills

To begin with, data science skills, a basic knowledge of maths and statistics are needed.

While looking forward to attaining the area full of data science skills and being a data scientist, a basic yet clear knowledge of maths, statistics particularly, is a much needful arena for data science training and so on. 

Because many of the algorithms need statistics, they are categorized as a prerequisite that makes sure that you have a clear vision of insights.

7) Machine Learning

Machine learning is a much supportive block of data science skills.

The pulling out of information from data is a task performed by Data Science. This information is later used as a data input for Machine learning programs. 

Approaching Machine learning is pretty convenient, with certain libraries that are already stored in Python/R. As a data scientist who came down from strong data science training, the objectives are high: you have a transparent understanding of what problems ask for what solution.

8) AutoML

Machine Learning has come through the modern internet world only to be much more advanced than before. But surely that doesn’t count out of the humans. Regardless of whether the innovations have reached whichever phase, a human with data science skills is mandatorily needed for evaluation.

To cut down any challenges faced at first hand, AutoML is used.

It reduces the complexity, but it helps the data scientists and the learners of various data science courses save a hefty quantity of time.

9) Data Visualization

Because Data Visualization provides a fresh look at data portrayed in a graphical style- charts, graphs, etc., is the first step of any data science training. 

It is widely used for deliverance to organizations’ stakeholders for the least prerequisites and least data science constricting to that of Python or R programming language.

10) Database Management

Data science courses have the students learning about the Database Management System about how it supports the SQL from the school level.

Such programming languages are handpicked for this kind of data science skills only for its feature of fresh creation or restructure and viewing status. But it’s the DBMS that manages and re-files the database- tables with data storage.

The function of DBMS further goes down to availing the users a better and user-friendly interface.

11) Cloud and Big Data

Attaining cloud data science skills can present you with a nominal priced computing resource for AI Programming, Machine Learning, etc.

Big data plays a big role in the remote management of data anywhere with no deadline for accessibility. 

12) Data Wrangling

The data that a data scientist is supposed to analyze will most of the time be messy and befuddling to use.  Data wrangling skills are used for transforming and mapping data from one “raw” data form into another format.

The motive behind doing this is to make data more appropriate and valuable. It resolves issues of data imperfections like inconsistent string formatting or missing values. 

13) Data Integration

Such data science skills revolve around the ability to combine data residing in different sources for offering their unified view.

Data scientists need to have such skills for adeptly analyzing data for business intelligence purposes. 

Being equipped with this skill will enable you to land a Data Science job in a top-performing company. 

14) Data Ingestion

This skill revolves around the importing and transferring along with loading and processing related procedures of data for their database storage or later use. 

The data ingestion process deals with the loading of data from different sources. 

A data scientist who knows how to perform Data Ingestion will for sure be able to move ahead in the career and enjoy exponential career growth. The most popular tools for data ingestion are Apache Flume and Apache Sqoop. 

15) Knowledge of data science tools

For being an expert-level data scientist, you need to have hands-on experience in some of the most crucial data science tools.

Some of the common tools you should be aware of are-

  • SAS
  • Apache Spark
  • BigML
  • D3.js
  • Excel
  • Ggplot2
  • Tableau
  • Jupyter
  • Matplotlib
  • NLTK
  • Scikit-learn
  • TensorFlow
  • Weka

Final Thoughts!

It’s the data experts who calm any barrier of low-quality coding for the innovation of something ‘better.’

The demand for the field has grown to an unprecedented level and is said to approach more levels. As such, I am having induced myself with data science skills with the best consequences. 

Learning has no end number, and with the arena that’s more focused on AI, going forward to know it is a must. 

To muggle self with the latest insights of data science skills is, of no vain, but an enduring (for some) or fun process for a better career. The face-off to challenges at the initial state is very clear and big. But that is something you should be scared of. Rather, be optimistic, and once you pass the stage of an amateur, you will see the perks of it.

Enroll yourself in Data Science Course now to learn and master top data science skills.

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