Dive Into the new age of Data Driven jobs
What is difference between Data Science, Data Engineering and Data Analytics?
Data science is the study of data. It involves understanding data, mining data, and using data to make predictions.
Data engineering is the process of designing, creating, and maintaining data systems. This includes data warehouses, data lakes, and data pipelines.
Data analytics is the process of analyzing data to extract insights. This can be done using statistical methods, machine learning, or both.
- Strong analytical and mathematical skills
- Familiarity with statistical analysis and modeling techniques
- Strong programming skills (typically in Python, R or Java)
- Experience working with large data sets
- Familiarity with databases and SQL
- Experience with data visualization tools and techniques
- Strong communication and presentation skills
What are common skillset required for all these profiles ?
We are Focused on..
Cloud Analytics Modernization
Cloud computing is the delivery of different services through the Internet. These resources include tools and applications like data storage, servers, databases, networking, and software. Now a day most of the Data Analytics industry is using Cloud Platforms like AWS, GCP, and AZURE.
Data Science Acceleration
Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models.
Big Data Analytics
Big data analytics is the use of advanced analytic techniques against large data sets, including structured/unstructured data and streaming/batch data.
Machine Learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks.
We Cover These Industry Leading Tools in Our Courses
