Big Data With Python

Big Data With Python

As Python has emerged as a popular language for handling and analyzing big data due to its extensive ecosystem of libraries and tools. With the increasing volume, velocity, and variety of data, Python provides efficient solutions for working with large datasets. Libraries such as Pandas, NumPy, and Dask offer powerful data structures and functions that enable data manipulation, cleaning, and aggregation at scale. Additionally, PySpark, a Python API for Apache Spark, allows distributed processing of big data across clusters, making it suitable for handling massive datasets.

Python’s integration with Hadoop and other distributed file systems further enhances its capabilities in the big data realm. Moreover, Python’s support for machine learning libraries like scikit-learn and TensorFlow enables the application of advanced analytics and predictive modeling to large datasets. With its simplicity, versatility, and robust libraries, Python empowers data scientists and analysts to tackle the challenges posed by big data, extract valuable insights, and make data-driven decisions.

What you'll learn

  • Python Environment Setup
  • Decision Making
  • Loops and Number
  • Strings
  • Lists
  • Tuples
  • Dictionary
  • Date and Time
  • OOPS
  • Lambda,Map and filter
  • Spark and Hadoop
  • Partitioning and Bucketing
  • Hadoop distributed File System (HDFS)
  • joins in Hive, sqoop
  • Azure Cloud with DataBricks
  • Introduction Hive
  • Exceptions
  • Connecting Spark with S3
  • Various data sources
  • Various levels of persistence
  • Spark DataFrame
  • User Define Functions

This course includes:

  • Timeline: 72 Hours
  • Mode: Online
  • Theory : 20%
  • Hands On Assignments: 70%
  • Discussion : 10%

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 Python is popular due to its simplicity, readability, and versatility. It has a clean syntax that makes it easy to learn and write code, while its extensive libraries and frameworks enable developers to tackle a wide range of tasks, including web development, data analysis, machine learning, and automation, making it a preferred choice for beginners and experienced developers alike.

Data science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It involves collecting, cleaning, analyzing, and interpreting data to uncover patterns, trends, and correlations, enabling organizations to make informed decisions, solve complex problems, and drive innovation.

Data science involves the entire process of extracting insights and knowledge from data, including data collection, cleaning, analysis, and interpretation, while data analysis specifically focuses on examining data sets to discover patterns, trends, and relationships, often with a narrower scope and emphasis on statistical techniques and visualization. Data science encompasses a broader range of techniques and methodologies, incorporating data analysis as one of its components.

Machine learning is a subfield of artificial intelligence (AI) that focuses on the development of algorithms and models that allow computers to learn and make predictions or decisions without being explicitly programmed. It involves training and optimizing models using data to enable machines to recognize patterns, make accurate predictions, and improve performance over time.

Data mining is the process of discovering patterns, relationships, and insights from large datasets through automated or semi-automated analysis. It involves using statistical techniques, machine learning algorithms, and pattern recognition methods to extract valuable information and knowledge from data, often with the goal of making predictions or uncovering hidden patterns that can drive decision-making and improve business outcomes.

In machine learning, a model refers to a mathematical representation or algorithm that is built using training data to make predictions or decisions. It captures patterns and relationships in the data and can be used to classify, regress, cluster, or generate new data based on the learned patterns, enabling the model to generalize its predictions to unseen data.

An algorithm in AI refers to a set of well-defined instructions or rules that guide the behavior of an AI system. It is a step-by-step procedure designed to solve a specific problem or perform a task, allowing AI systems to process data, make decisions, learn from examples, and perform various cognitive tasks.

Data science is a multidisciplinary field that involves extracting insights and knowledge from data through scientific methods and techniques. Big data, on the other hand, refers to large and complex datasets that cannot be easily managed or processed by traditional data processing tools, often requiring specialized technologies and techniques for storage, retrieval, and analysis. Data science and big data often intersect, as data scientists utilize big data technologies to extract valuable insights from vast amounts of data.

AI, big data, and data science are closely related fields that often intersect and complement each other. Data science provides the methodologies and techniques to extract insights from data, big data offers the infrastructure and tools to manage and process large datasets, and AI utilizes algorithms and models to enable machines to learn from data and make intelligent decisions, forming a symbiotic relationship where each field enhances and supports the others in leveraging the power of data for various applications.

AI-based process intelligence refers to the use of artificial intelligence techniques and algorithms to gain insights and understand the intricacies of business processes. It involves analyzing large volumes of data generated during process execution to identify inefficiencies, bottlenecks, and improvement opportunities, allowing organizations to optimize and automate their workflows, enhance productivity, and make data-driven decisions for process optimization and innovation.
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