Tools you can master as data science beginners

We're entering a new world in which data may be more important than software.” – Tim O'Reilly, founder, O'Reilly Media

Data is proving to be more influencial nowadays. The reason behind it is that, it helps the organizations and tech industries to decide their strategies for deploying products or services in the market which can give them huge revenue. Data gives us some very meaningful and important insights which helps an individual to figure out the pattern or mindset of the general public indirectly without asking them.

Data has not only became important for IT sector but also other ones like entertainment, healthcare, banking, automobiles etc. Almost everyone is in need of it. But in order to make the data useful and arrive at particular outcome, it has to go particular procedure or approach to arrive at outcome. Surely, all of this is gonna require performing some complex tasks and is very time consuming.

But to overcome these constraints and to reach to accurate result, tools always helps, no matter whatever may be the technology stack.

So here you can find some tools you can get over if you are starting off your journey as a data scientist.

1. Excel

The Tool that every child knows when he/she was in school. It is very simple yet very important tool for data scientists. Almost every organization, whether it is big or small is using it. You can say that it has terrible respect. Everyone before can know it as simple spreadsheet tool, but it can be a very powerful weapon for data scientist and here are some reasons.

• One can make and view datasets which are the heart of data science
• Extension available in python language
• Data analysis for getting quick insights
• Dashboard can be generated
• Availability of mathematical operations

2. Tableau
Tableau is a powerful and fastest growing data visualization tool used nowadays almost in all industries. It acts as an data visualization tool for data scientist. It is very easy to learn and can be mastered if practiced thoroughly. It is available as tableau public and tableau desktop and has many exciting courses and offers for students. The tool has its qualities like

• Perfect for data analysis as it has many rich features that can make visuals more attractive.
• No coding knowledge required
• best when working with big data
• Many options to secure data without scripting
• has its own server

3. Apache Spark
Apache Spark™ is a unified analytics engine for large-scale data processing. It has many features and subtools which can ease the work of data scientists resulting in time saving and efficient coding.

Advantages:

• Currently provides APIs in Scala, Java, and Python, with support for other languages (such as R) on the way
• Integrates well with the Hadoop ecosystem and data sources (HDFS, Amazon S3, Hive, HBase, Cassandra, etc.)
• Run workloads faster
• Combine SQL, streaming, and complex analytics

4. Jupyter
Jupyter is development environment platform for data scientists. Jupyter provides multi-language interactive computing environments. Its Notebook, an open source web application, allows data scientists to create and share documents containing live code, equations, visualizations, and explanatory text.
Some features of jupyter:

• Configure and arrange the user interface to support a wide range of workflows in data science, scientific computing, and machine learning
• Use interactive widgets to manipulate and visualize data in real time
• extensible and modular: write plugins that add new components and integrate with existing ones
• Supports programming languages including popular data science languages like Python, R, Julia

No doubt there might be lot more tools, but at basic level if you can learn and master these tools, then your pathway towards becoming data scientist may be of some ease.

here i am including some learning resources links so that it can be easily accessible for all data science enthusiasts.

1.https://www.youtube.com/playlist?list=PLlz0muypSBNbd6VbNdU9cCzCQx8_dU7Hy

2.https://www.tableau.com/learn/training/20202

3.For apache spark, You can refer to Srivatsan Srinivasan's AIEngineering channel of youtube
https://youtu.be/pEi-Ak5l00A

Any feedback would be greatly appreciated.