Have you ever thought of a career in Big Data? If you are an aspiring data scientist or even a data analyst, work well on your skills as this sector is currently in-demand and is lucrative.
According to Glassdoor, the annual salary range of a Data Scientist in India is around ₹ 10 Lakhs. With growing digitization, data has become the need of the hour, and most businesses leverage the power of data to develop business strategies, sales etc.
As a result, there are many jobs available in the Data Science sector. However, to become a successful Data Analyst, you need to be skilled with the tools and techniques to comprehend numbers and graphs better. Data experts use tools for visualisation, modelling, extraction, etc. So, if you want to get a job in the data domain, learn these data science tools for a better chance of bagging a high-paying job. The good news is that you don’t have to be a coding ninja to learn the tools listed below:
Top 7 Data Tools for Data Science Jobs
Tableau is the most popular Data Science tool used for Data Visualisation. Tableau offers real-time customisable dashboards. Furthermore, it can handle any data size. The best part of using Tableau is that it is simple for both technical and non-technical users. Furthermore, it can be incorporated into an application or accessed on desktop and mobile.
2. Apache Spark
Apache Spark is used for Data Processing and is quite helpful in Big Data. It can process vast data sets and dispense processing functions across a number of computers, either independently or in conjunction with other distributed computing technologies. In addition, it uses efficient query execution and in-memory caching for quick analytic queries over any volume of data.
3. SQL DBM
SQL DBM, also known as SQL Database Modeler, is one of the best data modelling tools these days. It enables you to alter current schemas visually and transfer them with ease. You may also import a SQL script to generate a database model instantly. Depending on your modifications, it automatically creates and upgrades SQL statements.
4. Microsoft Power BI
Microsoft Power BI is the widely used business intelligence platform. This technology promises to assist companies in extracting actionable insights from all that chaotic, complex data. It helps in gathering, processing, sharing and visualization of data.
SAS stands for statistical analytics software; as its name implies, it is a tool that aids statistical analysis. The common application of SAS includes data management, retrieving statistical data, predictive analysis, and business intelligence. SAS is frequently utilised in business intelligence, weather forecasting, and pharmaceutical industries.
6. Mongo DB
MongoDB is a popular open NoSQL database employed as a Data Science database. MongoDB is a technology that can organize, store, and obtain data usually structured in document format. The advantage of switching to MongoDB is the ease of setting it up and using it. Additionally, it lacks schema because it was created to manage unstructured data.
RapidMiner is not just important for Data Science but also crucial in Machine Learning. It is used for Data Mining and helps in data loading, predictive analytics, statistical modelling, and data visualisation. RapidMiner is built using Java and is pretty simple even if you lack coding skills.
So, what are you waiting for? Don’t spend your time over-thinking, just start with the best Data Science course and learn the domain fundamentals. Enrol in short courses precisely designed to explain these tools. Start with some introductory free courses to determine where you stand in this field. There are countless ways you can learn these Data Science tools, but the question is – when are you going to learn?