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A Business Intelligence (BI) analyst is someone who is responsible for analyzing organizational data supporting the decision making processes. There is a large amount of data that is generated throughout the lifecycle of the organization. Making the most of this data and maximizing its utility through intelligent decision making is the work of a BI Analyst.
A true data scientist is someone who can define a problem statement that can be solved using data and then successfully solve it using the knowledge and expert skills in data mining, cleaning, analyzing, and interpreting.
A Deep Learning engineer builds models using deep learning neural networks to draw business insights, which can be used to make business decisions.
An AI engineer builds AI models using machine learning algorithms and deep learning neural networks to draw business insights, which can be used to make business decisions.
A machine learning (ML) engineer is an expert on using data to training models. The models are then used to automate processes like image classification, speech recognition, and market forecasting.
A TensorFlow specialist utilizes the vast knowledge of the TensorFlow library to create everything from neural networks to chat bots as a means to solve a myriad of problems in data science.
66% of data scientists reported using Python daily, making it the most used tool among analytics professionals. Join them and learn Python basics for Data Science, become versatile with Pandas, and challenge R Programmers in Statistic tasks.
R engineers create graphical representations or simulations of data as well as conduct analysis of that data using the R language. Other job duties include designing statistical models, formulating procedures, and providing technical assistance for clients.
Excel and Power BI tools are core in Business Intelligence Analyst arsenal. For performing ETL, data modeling, data mining, data analysis visualization, and presenting insights to stakeholders.
AWS Data & ML Engineer focus on AWS Analytics and ML service offerings such as Amazon Kinesis, AWS Glue, Amazon Redshift, Amazon EMR, Amazon Athena, Amazon SageMaker, and more. AWS Data & ML Engineer design flexible and scalable solutions, and work on some of the most complex challenges in large-scale computing by utilizing skills in data structures, algorithms, and object-oriented programming.
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