Big Data Analytics with Python Training

Big Data Analytics with Python Training

Master Python for Big Data Analytics: NumPy, Pandas, Scikit-learn, and Spark

|
Platform:
Online
In-class
Revised and Updated: 28 September 2026
Date Venue Duration
30 November - 04 December 2026 Sandton, Gauteng 5 Days
22 - 26 February 2027 Sandton, Gauteng 5 Days

Course Introduction

Python stands out as a highly adaptable and robust open-source language, known for its ease of learning and powerful libraries for data manipulation and analysis. It has been extensively used in scientific computing and various mathematical domains such as physics, finance, oil and gas, and signal processing.

Prospen Africa’s Big Data Analytics with Python Training course offers a comprehensive overview of data analysis techniques using Python. As Data Scientist is one of the most sought-after professions today, mastering Python is essential for these roles. This Big Data Analytics with Python Training equips you with the essential tools and knowledge required for Data Analytics with Python, helping you become proficient in Python programming concepts.

Participants will gain expertise in Python programming, focusing on data analytics and Machine Learning techniques. This training course provides practical experience and the skills needed for predictive modelling and other advanced analytics tasks, including working with real-time data and integrating with Big Data platforms such as Hadoop and Spark.

Completing our specialized Big Data Analytics with Python Training program ensures that technical teams transition from basic spreadsheet reporting to scalable, enterprise-grade big data architecture. Mastering Big Data Analytics with Python Training tools enables organizations to unlock competitive market intelligence, making Big Data Analytics with Python Training an essential program for digital transformation.

Course Objectives

Upon successful completion of this Big Data Analytics with Python Training, participants will be able to:

  • Programmatically download and analyse data
  • Manage various types of data: ordinal, categorical, encoding
  • Perform data visualisation
  • Execute step-by-step data analysis
  • Understand the roles of a Machine Learning Engineer
  • Describe Machine Learning
  • Work with real-time data
  • Use tools and techniques for predictive modelling
  • Discuss Machine Learning algorithms and their implementation
  • Validate Machine Learning algorithms
  • Explain Time Series and related concepts
  • Perform Text Mining and Sentiment Analysis

Organisational Benefits

Companies sending employees to this training can benefit in the following ways:

  • Save on operational costs, as Python is free to use due to its OSI-approved open source licence
  • Equip employees with knowledge in data analysis, Machine Learning, data visualisation, web scraping, and Natural Language Processing to improve organisational functions
  • Offer flexible and cost-effective professional development opportunities
  • Analyse case studies and apply successful techniques within the organisation
  • Understand the principles and practices of Big Data Analytics

Who should attend?

Participants focused on Big Data Analytics with Python Training typically include:

  • Analytics Team Managers
  • Business Analysts interested in Machine Learning concepts
  • Information Architects seeking proficiency in Predictive Analytics
  • Programmers, Developers, Technical Leads, and Architects
  • Individuals aspiring to be Machine Learning Engineers
  • Professionals aiming to develop automatic predictive models using data
Microsoft (365) Office Courses

Training Methodology

Our diverse instructional approaches ensure effective learning:

– Lectures & Presentations: Engage with expert-driven, stimulating content.
– Course Material: Access well-crafted supporting resources.
– Group Work: Collaborate on discussions and case studies for practical insights.
– Workshops & Role-Play: Participate in immersive, scenario-based activities.
– Practical Application: Focus on applying theoretical knowledge in real situations.
– Post-Training Support: Receive extensive support after training for skill implementation.

Training Outline

Day 1 — Data Science Foundations, Statistics, and Python Setup
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
  • Exploratory Data Analysis (EDA)
  • EDA — quantitative technique
  • EDA — graphical technique
  • Data analytics conclusions and predictions
  • Data analytics communication
  • Data types for 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 and dispersion
  • Histograms
  • Correlation and inferential statistics
Module 4: Python Environment Setup and Essentials
  • Anaconda and installation of the Anaconda Python distribution
  • Data types with Python
  • Basic operators and functions

Day 2 — Mathematical and Scientific Computing with Python
Module 5: Mathematical Computing with Python (NumPy)
  • Introduction to NumPy
  • Creating and printing an ndarray
  • Class and attributes of ndarray
  • Basic operations, copies, and views
  • Mathematical functions of NumPy
  • Practical Exercise: Lab: Evaluate datasets containing GDPs of different countries and Summer Olympics results.
Module 6: Scientific Computing with Python (SciPy)
  • Introduction to SciPy
  • SciPy sub-packages — integration and optimisation
  • Using SciPy to solve a linear algebra problem
  • Using SciPy to define random variables for random values
  • Practical Exercise: Demo: Calculating eigenvalues and eigenvectors.

Day 3 — Data Manipulation with Pandas
Module 7: Data Manipulation with Pandas
  • Introduction to Pandas
  • Understanding the DataFrame
  • Viewing and selecting data
  • Handling missing values
  • Data operations
  • File read and write support
  • Pandas SQL operations
  • Practical Exercise: Lab: Analyse a Federal Aviation Authority (FAA) dataset and a fire department CSV dataset using Pandas.

Day 4 — Machine Learning and Natural Language Processing
Module 8: Machine Learning with Scikit-learn
  • The Machine Learning approach
  • Understanding datasets and extracting features
  • Identifying problem type and learning model
  • Training, testing, and optimising the model
  • Supervised learning models: linear regression, logistic regression
  • Unsupervised learning models
  • Building a pipeline
  • Model persistence and evaluation
  • Practical Exercise: Lab: Analyse a dataset to identify features and response labels.
Module 9: Natural Language Processing with Scikit-learn
  • NLP overview and applications
  • NLP libraries in Scikit-learn
  • Extraction considerations
  • Model training and grid search
  • Practical Exercise: Lab: Analyse a spam collection dataset and a sentiment dataset using NLP.

Day 5 — Visualisation, Web Scraping, and Big Data Integration
Module 10: Data Visualisation in Python Using Matplotlib
  • Introduction to data visualisation
  • Line properties
  • (x, y) plots and subplots
  • Types of plots
  • Practical Exercise: Lab: Analyse a fuel-economy dataset with a pair plot, and draw a pie chart to visualise a dataset.
Module 11: Web Scraping with Beautiful Soup
  • Web scraping and parsing
  • Understanding and searching the tree
  • Navigating and modifying the tree
  • Parsing and printing the document
  • Practical Exercise: Lab: Scrape a sample public website page to extract structured content.
Module 12: Integration with Hadoop, MapReduce, and Spark
  • Why Big Data solutions are provided for Python
  • 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
  • Practical Exercise: Lab: Determine the word count for a sample large text dataset using PySpark.

Course Categories

Get a Quote Banner Outline

Request a Call Back

Your submission has been successful

Please check your email for confirmation

Success Stories

Discover how our courses enhance professionals’ effectiveness in their workplaces.

Tharisa Minerals

SQL Server Training

The course was very informative and interesting

Revenue Appeals Tribunal Eswatini

Basic Registry, Records and Archives

The facilitator is the best in the field and i personally learnt a lot on the subject matter.

Magalies Water

Document Control and Document Management Systems

The facilitator was knowledgeable, engaging, and presented the material clearly. The institution provided a well-organized learning environment with adequate resources and support throughout the training.

Central Bank of Lesotho

Data & Information Governance

The vast knowledge of Mr Selelepoo is really unmatched. I am totally happy and transformed from this training.

FAQs – Big Data Analytics with Python Training

Explore Big Data Analytics with Python, covering data processing, analysis, visualisation, Python programming, data handling, and practical techniques for extracting business insights.

What is covered in the Big Data Analytics with Python Training?
The Big Data Analytics with Python Training covers Python for data analytics, statistics, NumPy, SciPy, Pandas, machine learning with Scikit-learn, data visualisation, natural language processing, web scraping, Hadoop, and Spark.
Who should attend Big Data Analytics with Python Training?
It is suitable for analytics managers, business analysts, information architects, programmers, developers, technical leads, architects, and professionals aspiring to work in machine learning and predictive analytics.
Does the Big Data Analytics with Python Training cover Python libraries for data analytics?
Yes. Participants learn how to use NumPy for mathematical computing, SciPy for scientific computing, Pandas for data manipulation, Scikit-learn for machine learning, and Matplotlib for data visualisation.
Does the Big Data Analytics with Python Training cover machine learning and predictive modelling?
Yes. The Big Data Analytics with Python Training introduces supervised and unsupervised learning, regression models, feature extraction, model training and testing, pipelines, evaluation, optimisation, and predictive modelling using Scikit-learn.
Does the Big Data Analytics with Python course cover Hadoop and Spark?
Yes. Participants learn about Big Data solutions, Hadoop and HDFS, Hadoop Streaming, MapReduce concepts, and Python integration with Spark using PySpark.
Does the Big Data Analytics with Python Training include practical exercises?
Yes. The five-day course includes practical labs using real datasets for data analysis, NumPy, Pandas, machine learning, NLP, visualisation, web scraping, and PySpark-based Big Data exercises.

Related Courses