As the term suggests, Data Analytics refers to data processing methods intended to increase productivity and business profits. Data is collected from several sources and is cleaned and classified to examine various behavioral trends. The techniques and methods used differ depending on the organization or the person.
So, in short, if you understand your Business Administration and have the capacity to do an Exploratory Data Analysis, to collect the knowledge you need, then you’re good at going on a Data Analytics career.
Top Tools in Data Analytics
With the growing demand for Data Analytics on the market, a range of tools has emerged with different functionalities for this reason. The top methods in the data processing field are either open-source or user-friendly as follows.
- R programming – This method is the leading computational framework used for statistics and data processing. R compiles and runs on different platforms such as UNIX, Windows, and Mac OS. It also offers tools for the automated installation of all packages as per user-requirement.
- Python – Python is an open-source, object-oriented programming language that can be easily interpreted, written, and maintained. It offers a range of machine learning and visualization libraries, such as Scikit-Learn, Matplotlib, TensorFlow, Keras, Pandas, etc.
- Tableau Public – This is a free app that links to any database, such as Excel, Company Data Warehouse, etc. It then produces visualizations, maps, dashboards, etc with real-time web updates.
- QlikView – This tool provides in-memory data processing with quick results delivered to end-users. It also offers user aggregation and user analysis of data being reduced to about 10 present of its original scale.
- SAS – A programming language and framework for data processing and analysis, this method is easily accessible and can analyze data from various sources.
- Microsoft Excel – This method is one of the most commonly used methods for data analysis. Mostly used for internal client data, this tool analyzes tasks that summarize data with a sample of pivot tables.
- RapidMiner – A efficient, integrated platform that can be integrated with any type of data source, such as Oracle, Microsoft SQL, Excel, Sybase, Access, Tera data, etc. This method is mainly used for predictive analysis, such as data mining, text analysis, machine learning.
Become a Data Analyst
A Data Analyst brings value to their organizations by taking information on particular topics and then interpreting, evaluating, and presenting findings in detailed reports. So, if you can collect data from multiple sources, analyze data, gather secret information, and produce reports, you will become a Data Analyst.
In addition to the above-mentioned capabilities, the Data Analyst should also have skills such as Statistics, Data Cleaning, Data Visualization, and Exploratory Data Analysis. Also, if you know about Machine Learning, that will help you stand out from the crowd. As specialists, data analysts are also asked to use their expertise and resources to provide competitive analysis and to identify patterns within industries.
With demand for data Analytics growing, students are looking for full-time Masters programs to gain a deeper understanding of this booming area and to pursue their careers. Data Analytics Malaysia has emerged as one of the best possible choices. It offers cost-effective learning opportunities with a vibrant data science ecosystem, which is why many foreign students are drawn to it.