Data analytics, also known as data analysis, is a crucial component of modern business operations. It involves examining datasets to uncover useful information that can be used to make informed decisions. This process is used across industries to optimize performance, improve decision-making, and gain a competitive edge.
Data Analytics is a systematic approach that transforms raw data into valuable insights. This process encompasses a suite of technologies and tools that facilitate data collection, cleaning, transformation, and modelling, ultimately yielding actionable information. This information serves as a robust support system for decision-making. Data analysis plays a pivotal role in business growth and performance optimization. It aids in enhancing decision-making processes, bolstering risk management strategies, and enriching customer experiences. By presenting statistical summaries, data analytics provides a concise overview of quantitative data.
While data analytics finds extensive application in the finance industry, its utility is not confined to this sector alone. It is also leveraged in diverse fields such as agriculture, banking, retail, and government, among others, underscoring its universal relevance and impact. Thus, data analytics serves as a powerful tool for driving informed decisions and fostering growth across various industries.
Process of Data Analytics
Data analysts, data scientists, and data engineers together create data pipelines which helps to set up the model and do further analysis. Data Analytics can be done in the following steps which are mentioned below:
- Data Collection : It is the first step where raw data needs to be collected for analysis purposes. It consists of two steps in which data collection can be done. If the data are from different source systems then using data integration routines the data analysts have to combine the different data whereas sometimes the data are the subset of the data set. In this case, the data analyst would perform some steps to extract the useful subset and transfer it to the other compartment in the system.
- Data Cleansing : After collecting the data the next step is to clean the quality of the data as the collected data consists of a lot of quality problems such as errors, duplicate entries and white spaces which need to be corrected before moving to the next step. By running data profiling and data cleansing tasks these errors can be corrected. These data are organised according to the needs of the analytical model by the analysts.
- Data Analysis and Data Interpretation: Analytical models are created using software and other tools which interpret the data and understand it. The tools include Python, Excel, R, Scala and SQL. Lastly this model is tested again and again until the model works as it needs to be then in production mode the data set is run against the model.
- Data Visualisation: Data visualisation is the process of creating visual representation of data using the plots, charts and graphs which helps to analyse the patterns, trends and get the valuable insights of the data. By comparing the datasets and analysing it data analysts find the useful data from the raw data.
Having a technical and discipline-specific expertise can help you get to the interview room but it’s the soft skills that will make the hiring manager hand you the appointment letter. In this module, students will learn various Soft Skills like how to communicate professionally in English, Speaking in public without hesitation, using effective gestures and postures to appear impressive, managing stress and emotions and taking successful interviews. Achievers Technologies Pvt. Limited Company provides the best classes in Soft-skill training.
During / After this course, students may also appear for the following certifications :
Adobe Certified Associate
Certified Data Analytics Professional