In the recent years, the job of a Data analyst is supposed to be among the coolest leagues. By 2025, 94% of the Fortune 500 companies would have invested 15% or more in their Data Science projects. An average data science team consists of 3 or more data analysts and a senior Data Scientist with industry-oriented experience in various key technologies, management and operations.
If you are eyeing a data analyst role in any of the big companies, you have to have a basic competence in at least two programming languages, software training and Big Data management platforms. If you are worried that it’s too much to handle for a Fresher, we have a step guide for you.
This article explains quick steps on how to pursue a career in Data Science.
1.What is Data Analytics?
Data analytics is a combination of several technologies and techniques. This set includes many processing methods to obtain relevant results for strategic management and implementation.
As an industry term, data analytics refer to an assortment of basic Business Intelligence (BI), Online Analytic Processing (OLAP) and advanced analytics such as Predictive, Prescriptive and Customer Intelligence.
2.Your starting Point
Any graduate can pursue a data science career. However, IT and Computer Science engineers are best suited to further their career in the Data Science industry. As long as you love to work with networking, software development and computer applications, you can start a data analyst career any day.
3.Basic Competence Levels
A degree in engineering can help to start your career. Basic competencies in technical skills include programming experience in Python coding, Hadoop platform, Apache Spark and Data Visualization.
4.Pick the Best Technology for your roles
Here are the top 5 technologies that will help to become a certified data analyst—
- Data Virtualization: With Data Virtualization techniques, you can approach an alternate career for data management and as a packaged technology for Data Warehousing and ETL.
- Predictive Analytics
- Search and Self-Extraction tools such as file systems, databases and APIs
- In-memory data fabric such as DRAM, Flash and SSD
- Data Discovery and Data Integration between Structured, Semi-structured and Unstructured data sets
5.Learn about the data analytics organization
Most modernized data-driven companies apply machine learning and AI techniques in their businesses. A successful data analyst should be focused on targets to build data sets, data models and drive data-centric in the organization.
Since Big Data analytics has climbed to the top of every corporate setup, you can bring your own creativity and expertise to move the needle on competitive differentiation.
While it may take years to learn these steps, you have a simpler, and much faster route to successful transition in this career. It’s to enroll with an online data analytic course. Data Analytics courses in Bangalore not only train professionals to work with basic IT support but also build models that predict and optimize business outcomes for the businesses.