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Summary: A comprehensive BigData syllabus encompasses foundational concepts, essential technologies, data collection and storage methods, processing and analysis techniques, and visualisation strategies. Fundamentals of BigData Understanding the fundamentals of BigData is crucial for anyone entering this field.
Data Visualization : Techniques and tools to create visual representations of data to communicate insights effectively. Tools like Tableau, PowerBI, and Python libraries such as Matplotlib and Seaborn are commonly taught. Tools and frameworks like Scikit-Learn, TensorFlow, and Keras are often covered.
Programs like Pickl.AI’s Data Science Job Guarantee Course promise data expertise including statistics, PowerBI , Machine Learning and guarantee job placement upon completion. It emphasises probabilistic modeling and Statistical inference for analysing bigdata and extracting information.
They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. With expertise in programming languages like Python , Java , SQL, and knowledge of bigdata technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently.
Here is the tabular representation of the same: Technical Skills Non-technical Skills Programming Languages: Python, SQL, R Good written and oral communication Data Analysis: Pandas, Matplotlib, Numpy, Seaborn Ability to work in a team ML Algorithms: Regression Classification, Decision Trees, Regression Analysis Problem-solving capability BigData: (..)
I would first perform exploratory data analysis to understand the data distribution and identify potential patterns or insights. Then, I would use sampling techniques or employ bigdata processing tools like Apache Spark to analyse the large dataset efficiently. Access to IBM Cloud Lite account.
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