Course Overview
Python has been one of the most adaptable, and robust open-source languages that are easy to learn and uses powerful libraries for data manipulation and analysis. For many years now, Python has been used in scientific computing and mathematical domains such as physics, finance, oil and gas, and signal processing. This Big Data Analytics with Python course provides a complete overview of data analysis techniques using Python. A Data Scientist is one of the strongest professions today and Python is a crucial skill for such roles.
This Zoe training course will empower you with the practical experience required to perform predictive modelling that would require Machine Learning using Python. The Big Data Analytics with Python course teaches you to master the concepts of Python programming. Through this training, you will gain knowledge of the essential tools of Data Analytics with Python. Participants will learn concepts and techniques like web scraping, hypothesis building, data wrangling, data exploration, data visualisation, mathematical computing, Python programming concepts, NumPy and SciPy, and Scikit-Learn for Natural Language Processing.
Why This Course Is Required?
Big Data Analytics with Python has become essential for modern organizations as Python emerges as the dominant programming language for data science and machine learning applications, with the global big data analytics market projected to reach $1.1 trillion by 2032, growing at a CAGR of 14.5% from 2024 to 2032. Python’s popularity stems from its simplicity, extensive library ecosystem including NumPy, Pandas, Scikit-learn, and Matplotlib, making it the preferred choice for data scientists and analysts who need to process, analyze, and visualize massive datasets efficiently while building sophisticated machine learning models.
The exponential growth of data generated by businesses, IoT devices, and digital processes has created an urgent need for professionals skilled in Python-based big data analytics to extract valuable insights that drive strategic decision-making and competitive advantage. Without comprehensive understanding of Python’s data analytics capabilities, organizations struggle to harness their data assets effectively while missing opportunities to implement predictive analytics, automate decision-making processes, and optimize operations through advanced statistical modeling and machine learning techniques.
Research demonstrates that the global big data analytics market was valued at $0.3 trillion in 2023, and is projected to reach $1.1 trillion by 2032, growing at a CAGR of 14.5% from 2024 to 2032. The big data analytics market is defined as the industry that collects, processes, and analyzes huge and complex data sets to extract important insights and make sound business decisions. Python has become indispensable in this market due to its powerful libraries for data manipulation, analysis, and machine learning that enable organizations to extract meaningful patterns, trends, and correlations from enormous volumes of structured and unstructured data.
Course Objectives
Upon completing this big data analytics with python training course successfully, participants will be able to:
- Programmatically download and analyse data and practise techniques to manage various types of data – ordinal, categorical, encoding
- Learn data visualisation and master the art of performing step-by-step data analysis
- Gain information on the roles of a Machine Learning Engineer and describe Machine Learning
- Work with real-time data and learn tools and techniques for predictive modelling
- Discuss Machine Learning algorithms and their implementation and validate Machine Learning algorithms
- Explain Time Series and its related concepts and perform Text Mining and Sentimental analysis
Master Python data analytics excellence and drive data-driven innovation—enroll today to become an expert in Big Data Analytics with Python!
Training Methodology
This is an interactive big data analytics with python training program and will consist of the following training approaches:
- Lectures delivered by experienced Python and data analytics professionals
- Seminars & Presentations featuring real-world case studies and industry examples
- Group Discussions fostering collaborative learning and knowledge sharing
- Assignments that reinforce key concepts and practical applications
- Case Studies & Functional Exercises based on actual Python data analytics scenarios and implementations
This immersive approach fosters collaborative learning through peer interaction, group problem-solving, and knowledge sharing among participants from diverse data analytics backgrounds. The methodology emphasizes practical skill development over theoretical memorization, ensuring participants leave with immediately applicable tools and strategies.
Similar to all our courses, this program also follows the ‘Do-Review-Learn-Apply’ model, creating a structured learning journey that transforms Python data analytics knowledge into operational excellence through systematic practice and implementation.
Who Should Attend?
This big data analytics with python training course would be suitable for:
- Analytics Team Managers overseeing data analytics operations and strategies
- Business Analysts who want to comprehend Machine Learning concepts for strategic decision-making
- Information Architects who want to gain proficiency in Predictive Analytics
- Programmers, Developers, Technical Leads, Architects working with data-intensive applications
- Individuals aspiring to be Machine Learning Engineers
- Professionals who want to develop automatic predictive models using data
Organizational Benefits
Companies who send in their employees to participate in this big data analytics with python training course can benefit in the following ways:
- Save on operational costs as Python is free to use because of its OSI-approved open source license
- Give your employees knowledge in data analysis, Machine Learning, data visualisation, web scraping, and Natural Language Processing, which will, in turn, allow them to apply these concepts to improve the functions in your organisation
- Provide your workforce with flexible and cost-effective professional development opportunities
- Analyse case studies in this domain and be able to apply successful techniques in your organisation
- Comprehend the principles and practice of Big Data Analytics and the context in which this operates
Studies show that organizations implementing comprehensive Python-based big data analytics capabilities experience significant operational improvements through enhanced data processing efficiency, predictive modeling accuracy, and automated decision-making systems. The integration of advanced technologies such as machine learning and artificial intelligence with big data analytics opens new possibilities for automation, personalization, and scalability, paving the way for transformative changes in how businesses operate and compete in the digital age. Training enables organizations to leverage Python’s extensive ecosystem of data science libraries to build sophisticated analytics pipelines, implement real-time data processing systems, and develop predictive models that drive innovation and competitive advantage while reducing costs through open-source solutions.
Empower your organization with Python data analytics expertise—enroll your team today and see the transformation in data science capabilities and analytical insights!
Personal Benefits
Professionals who participate in this big data analytics with python training course can benefit in the following ways:
- Build and use Machine learning applications and other scientific computations
- Cut development time in half with Python’s simple to read syntax and easy compilation feature
- Learn to debug programs with Python’s built-in debugger
- Learn to run Python on Windows, Linux/Unix, and Mac OS, and Java and .NET virtual machines
- Understand why Python is considered one of the most preferred languages for Data Analytics
Course Outline
MODULE 1: DATA SCIENCE OVERVIEW
- Introduction to Data Science
- Different Sectors Using Data Science
- Purpose and Components of Python
MODULE 2: DATA ANALYTICS OVERVIEW
- Data Analytics Process
- Knowledge Check
- Exploratory Data Analysis (EDA)
- EDA-Quantitative Technique
- EDA – Graphical Technique
- Data Analytics Conclusion or Predictions
- Data Analytics Communication
- Data Types for Plotting
- Data Types and Plotting
MODULE 3: STATISTICAL ANALYSIS AND BUSINESS APPLICATIONS
- Introduction to Statistics
- Statistical and Non-statistical Analysis
- Major Categories of Statistics
- Statistical Analysis Considerations
- Population and Sample
- Statistical Analysis Process
- Data Distribution
- Dispersion
- Histogram
- Correlation and Inferential Statistics
MODULE 4 PYTHON ENVIRONMENT SETUP AND ESSENTIALS
- Anaconda
- Installation of Anaconda Python Distribution
- Data Types with Python
- Basic Operators and Functions
MODULE 5: MATHEMATICAL COMPUTING WITH PYTHON (NUMPY)
- Introduction to NumPy
- Activity-Sequence it Right
- Creating and Printing an nd array
- Class and Attributes of nd array
- Basic Operations
- Copy and Views
- Mathematical Functions of NumPy
- Evaluate the datasets containing GDPs of different countries
- Evaluate the datasets of Summer Olympics 2012
MODULE 6: SCIENTIFIC COMPUTING WITH PYTHON (SCIPY)
- Introduction to SciPy
- SciPy Sub Package – Integration and Optimisation
- SciPy Sub package
- Demo – Calculate Eigenvalues and Eigenvector
- Use SciPy to solve a linear algebra problem
- Use SciPy to define 20 random variables for random values
MODULE 7: DATA MANIPULATION WITH PANDAS
- Introduction to Pandas
- Understanding DataFrame
- View and Select Data Demo
- Missing Values
- Data Operations
- File Read and Write Support
- Pandas SQL Operation
- Analyse the Federal Aviation Authority (FAA) dataset using Pandas
- Analyse the dataset in CSV format given for fire department
MODULE 8: MACHINE LEARNING WITH SCIKIT–LEARN
- Machine Learning Approach
- Understand data sets and extract its features
- Identifying problem type and learning model
- How it Works
- Train, test and optimising the model
- Supervised Learning Model Considerations
- Scikit-Learn
- Supervised Learning Models – Linear Regression
- Supervised Learning Models – Logistic Regression
- Unsupervised Learning Models
- Pipeline
- Model Persistence and Evaluation
- Analyse a dataset to find the features and response label of it
MODULE 9: NATURAL LANGUAGE PROCESSING WITH SCIKIT LEARN
- NLP Overview
- NLP Applications
- NLP Libraries-Scikit
- Extraction Considerations
- Scikit Learn-Model Training and Grid Search
- Analyse a given spam collection dataset
- Analyse the sentiment dataset using NLP
MODULE 10: DATA VISUALISATION IN PYTHON USING MATPLOT-LIB
- Introduction to Data Visualisation
- Line Properties
- (x, y) Plot and Subplots
- Types of Plots
- Analyse the “auto mpg data” and draw a pair plot
- Draw a pie chart to visualise a dataset
MODULE 11: WEB SCRAPING WITH BEAUTIFUL SOUP
- Web Scraping and Parsing
- Knowledge Check
- Understanding and Searching the Tree
- Navigating options
- Demo3 Navigating a Tree
- Knowledge Check
- Modifying the Tree
- Parsing and Printing the Document
- Scrape the Simplilearn website page to perform some tasks
- Scrape the Simplilearn website page to perform some tasks
MODULE 12: INTEGRATION WITH HADOOP MAP-REDUCE AND SPARK
- Why Big Data Solutions are Provided for Python0
- Big Data and Hadoop
- Hadoop Core Components
- Python Integration with HDFS using Hadoop Streaming
- Using Hadoop Streaming for Calculating Word Count
- Python Integration with Spark using PySpark
- Using PySpark to Determine Word Count
- Determine the word count for Amazon dataset
Real World Examples
The impact of Big Data Analytics with Python training is evident in leading implementations:
- Walmart’s Python-Powered Analytics Infrastructure (Global)
Implementation: Walmart leverages Python extensively in their big data analytics infrastructure to process massive volumes of transactional data, customer behavior patterns, and supply chain information, utilizing Python libraries like Pandas, NumPy, and Scikit-learn for advanced analytics and machine learning applications.
Results: Walmart’s Python-based analytics systems enable real-time inventory optimization, demand forecasting, and personalized customer recommendations across their global retail network. The implementation has resulted in significant cost savings through improved inventory management, reduced waste, and enhanced customer satisfaction through data-driven decision making that processes billions of transactions and customer interactions daily. - Enterprise Python Data Science Implementations (Global)
Implementation: Major technology companies and financial institutions have adopted Python as their primary language for big data analytics, implementing comprehensive data science pipelines using Python frameworks for data ingestion, processing, machine learning, and visualization across diverse industries including healthcare, finance, and e-commerce.
Results: Organizations report significant improvements in analytical capabilities, with Python-based solutions enabling faster time-to-insight, improved model accuracy, and enhanced scalability for processing large datasets. The adoption of Python for big data analytics has resulted in accelerated innovation cycles, improved customer experiences through personalized services, and enhanced operational efficiency through automated decision-making systems that leverage machine learning algorithms and predictive analytics.
Be inspired by industry-leading Python data analytics achievements—register now to build the skills your organization needs for data science excellence!
