Custom Courses › Information Technology (IT) and Digital Skills

Big Data and Data Analytics Training

SAQA US AATICD-0090 | NQF 5 | Credits 10 | Duration 5 Days
From $913 per delegate

Description

This course equips learners with the skills to collect, process, analyze, and interpret large datasets to drive informed business decisions. Participants will gain practical knowledge of big data technologies, data analytics tools, and statistical methods to uncover patterns and insights. The course bridges the gap between raw data and strategic action, enabling professionals to leverage data as a competitive asset.

Learning Outcomes

  • Apply big data concepts and technologies to identify and capture relevant data from diverse sources.
  • Analyze large datasets using appropriate statistical methods and data mining techniques.
  • Evaluate data quality and integrity to ensure reliable analysis and reporting.
  • Implement data visualization tools to communicate insights effectively to stakeholders.
  • Design data-driven strategies to solve business problems and optimize processes.
  • Demonstrate ethical handling of data in compliance with relevant legislation and organizational policies.

Target Audience

This course is designed for data analysts, IT professionals, business intelligence specialists, and managers who need to harness big data for decision-making. It is also suitable for graduates seeking to enter the data analytics field.

Prerequisites

None — open enrollment. However, basic computer literacy and familiarity with spreadsheets are recommended.

Course Outline

Day 1: Foundations of Big Data and Data Analytics

Objectives:
• Understand the definition and characteristics of Big Data (Volume, Velocity, Variety, Veracity, Value).
• Differentiate between structured, semi-structured, and unstructured data.
• Explain the data analytics lifecycle and its phases.
• Identify key technologies and tools used in Big Data ecosystems.

Topics:
• Introduction to Big Data: 4 Vs and beyond.
• Data types and sources.
• Overview of data analytics: descriptive, diagnostic, predictive, prescriptive.
• The data analytics lifecycle: discovery, data preparation, modeling, deployment.
• Big Data technologies: Hadoop, Spark, NoSQL databases.
• Data storage and processing concepts.
• Role of data governance and ethics.
• Industry use cases and examples.

Day 2: Data Collection, Storage, and Processing

Objectives:
• Describe methods for collecting data from various sources.
• Understand data storage solutions: HDFS, cloud storage, data lakes.
• Explain batch and real-time processing paradigms.
• Perform basic data ingestion using common tools.

Topics:
• Data collection methods: APIs, web scraping, IoT sensors.
• Data storage: HDFS architecture, cloud storage options.
• Data lakes vs data warehouses.
• Batch processing with MapReduce.
• Real-time processing with Apache Kafka and Spark Streaming.
• Data ingestion tools: Sqoop, Flume.
• Data serialization formats: Avro, Parquet.
• Hands-on: Ingesting a sample dataset into HDFS.

Day 3: Data Preparation and Exploration

Objectives:
• Apply data cleaning techniques to handle missing values and outliers.
• Perform data transformation and feature engineering.
• Use exploratory data analysis (EDA) to uncover patterns.
• Visualize data distributions and relationships.

Topics:
• Data quality issues: missing values, duplicates, inconsistencies.
• Data wrangling with Python (Pandas) or R.
• Feature engineering: encoding categorical variables, scaling.
• EDA techniques: summary statistics, correlation analysis.
• Data visualization using Matplotlib, Seaborn, or Tableau.
• Outlier detection methods.
• Hands-on: Cleaning and exploring a real-world dataset.
• Best practices for reproducible data preparation.

Day 4: Analytical Modeling and Machine Learning

Objectives:
• Understand supervised and unsupervised learning algorithms.
• Build predictive models using regression and classification.
• Evaluate model performance with appropriate metrics.
• Apply clustering techniques for segmentation.

Topics:
• Introduction to machine learning: types and workflow.
• Linear regression and logistic regression.
• Decision trees and random forests.
• Model evaluation: accuracy, precision, recall, F1-score, ROC.
• Unsupervised learning: K-means clustering.
• Dimensionality reduction: PCA.
• Hands-on: Building and evaluating a classification model.
• Overfitting and regularization techniques.

Day 5: Advanced Analytics, Deployment, and Big Data Ecosystem Integration

Objectives:
• Explore advanced analytics: deep learning, natural language processing.
• Understand model deployment strategies and MLOps.
• Integrate analytics with Big Data platforms like Spark.
• Discuss ethical considerations and future trends.

Topics:
• Introduction to deep learning and neural networks.
• Natural language processing basics.
• Model deployment: APIs, containerization (Docker).
• MLOps: monitoring, versioning, CI/CD.
• Big Data analytics with Apache Spark MLlib.
• Scalable data pipelines: Airflow, Luigi.
• Ethics in data analytics: bias, privacy, fairness.
• Future trends: edge analytics, AI-driven automation.

Practicals

16 hours of practicals To be conducted online or on-campus or in-house
Overview

Practical sessions are integral to this course, enabling learners to apply Big Data concepts to real-world datasets. Through hands-on exercises, learners gain proficiency in data ingestion, cleaning, modeling, and deployment using industry-standard tools.

Practical Activities
  • Data Ingestion and Storage — Learners ingest a dataset from a CSV file into HDFS using command-line tools and verify storage. (4h)
  • Data Cleaning and Exploration — Learners clean a messy dataset (handle missing values, outliers) and perform EDA using Python. (4h)
  • Building and Evaluating a Predictive Model — Learners build a classification model using scikit-learn, tune hyperparameters, and evaluate performance. (4h)
  • Big Data Analytics with Spark — Learners use PySpark to perform data processing and run a machine learning algorithm on a large dataset. (4h)

Summatives

Each delegate is assessed continuously throughout the course via daily exercises, scored practical assignments, and a final summative test at the end.

Practical Assignments — 30%

Practical assignments are observed and scored against a rubric during the practical sessions. Each delegate's practical mark is averaged into a single 100% score and contributes 30% to the final total.

Daily Exercises — 20%

Every training day ends with a multiple-choice exercise scored out of 100%. The scores from each daily exercise are averaged across the duration of the course to produce a Daily Average mark, which contributes 20% to the final total.

Final Test — 50%

On the last day a final summative test is written. It is a multiple-choice paper with multiple-answer questions: each question may have more than one correct option, and a single wrong selection on a question marks the entire question wrong — no partial credit. The final test is scored out of 100% and contributes 50% to the overall mark.

Final Total
Component Out of Weight
Practical Assignments (rubric-scored) 100% 30%
Daily Average (multiple choice) 100% 20%
Final Test (multi-answer multiple choice) 100% 50%
Final Total — 100%

All marks are recorded on the AATICD LMS and visible to each learner under their account.

Certificate

Certificate of Completion

Awarded to delegates who achieve an overall mark of 50% or higher on the Final Total (Practicals 30% + Daily Average 20% + Final Test 50%).

How it works
  • Certificates are auto-generated on the AATICD LMS as soon as the marks pass the 50% threshold.
  • Each certificate is a branded PDF with the delegate's name, the course title, the unit standard ID, NQF level, credits, and the date of issue.
  • You can download or print your certificate from your LMS dashboard at any time after issue — there's no reissue fee and no expiry date.
  • If you scored under 50% you can sit the final test again at the next scheduled session at no extra cost.
Where to find it

Sign in to the LMS, open your dashboard, and your certificates appear under My Certificates. Each entry has a View / Download button and a print option.

Training Discounts

Group discounts apply automatically — the more delegates you enrol, the greater the saving. Discounts are calculated at 3% per 5 delegates, scaling up to 40% off for 100+ delegates.

Delegates Discount
5 3% off
10 6% off
15 9% off
20 12% off
25 15% off
30 18% off
50 30% off
75 40% off
100 40% off

3% discount per 5 delegates, up to 40% off for 100+ delegates. Contact us for a custom group quote.

Upcoming Training Sessions
Online training — attend live sessions from anywhere via our virtual classroom.
Start End Delivery Season Price Action
Mon 07 Dec 2026 Fri 11 Dec 2026 Virtual Summer 2026 $913 Register
Mon 04 Jan 2027 Fri 08 Jan 2027 Virtual Summer 2026 $913 Register
Mon 01 Feb 2027 Fri 05 Feb 2027 Virtual Summer 2026 $913 Register
Mon 01 Mar 2027 Fri 05 Mar 2027 Virtual Autumn 2027 $913 Register
Mon 07 Jun 2027 Fri 11 Jun 2027 Virtual Winter 2027 $913 Register
Mon 21 Jun 2027 Fri 25 Jun 2027 Virtual Winter 2027 $913 Register
Mon 05 Jul 2027 Fri 09 Jul 2027 Virtual Winter 2027 $913 Register
Mon 06 Sep 2027 Fri 10 Sep 2027 Virtual Spring 2027 $913 Register
Mon 20 Sep 2027 Fri 24 Sep 2027 Virtual Spring 2027 $913 Register
Mon 04 Oct 2027 Fri 08 Oct 2027 Virtual Spring 2027 $913 Register
Mon 06 Dec 2027 Fri 10 Dec 2027 Virtual Summer 2027 $913 Register
Mon 20 Dec 2027 Fri 24 Dec 2027 Virtual Summer 2027 $913 Register
Mon 03 Jan 2028 Fri 07 Jan 2028 Virtual Summer 2027 $913 Register
On-Campus training — face-to-face sessions at our training venues across Africa and beyond.
Showing all 380 sessions across 26 venues
Start End Venue Season Price Action
Mon 19 Oct 2026 Fri 23 Oct 2026 Dar es Salaam, Tanzania Spring 2026 $3,343 Register
Mon 19 Oct 2026 Fri 23 Oct 2026 Kinshasa, DRC Spring 2026 $3,343 Register
Mon 19 Oct 2026 Fri 23 Oct 2026 Kigali, Rwanda Spring 2026 $3,343 Register
Mon 19 Oct 2026 Fri 23 Oct 2026 Mbabane, Eswatini Spring 2026 $2,425 Register
Mon 19 Oct 2026 Fri 23 Oct 2026 Nairobi, Kenya Spring 2026 $3,343 Register
In-House training — we bring the trainer to your organisation, tailored to your team.
Start End Delivery Season Price Action
Mon 14 Dec 2026 Fri 18 Dec 2026 Your Premises Summer 2026 $1,231 Register
Mon 11 Jan 2027 Fri 15 Jan 2027 Your Premises Summer 2026 $1,231 Register
Mon 08 Feb 2027 Fri 12 Feb 2027 Your Premises Summer 2026 $1,231 Register
Mon 08 Mar 2027 Fri 12 Mar 2027 Your Premises Autumn 2027 $1,231 Register
Mon 14 Jun 2027 Fri 18 Jun 2027 Your Premises Winter 2027 $1,231 Register
Mon 28 Jun 2027 Fri 02 Jul 2027 Your Premises Winter 2027 $1,231 Register
Mon 12 Jul 2027 Fri 16 Jul 2027 Your Premises Winter 2027 $1,231 Register
Mon 13 Sep 2027 Fri 17 Sep 2027 Your Premises Spring 2027 $1,231 Register
Mon 27 Sep 2027 Fri 01 Oct 2027 Your Premises Spring 2027 $1,231 Register
Mon 11 Oct 2027 Fri 15 Oct 2027 Your Premises Spring 2027 $1,231 Register
Mon 13 Dec 2027 Fri 17 Dec 2027 Your Premises Summer 2027 $1,231 Register
Mon 27 Dec 2027 Fri 31 Dec 2027 Your Premises Summer 2027 $1,231 Register
Mon 10 Jan 2028 Fri 14 Jan 2028 Your Premises Summer 2027 $1,231 Register
Training Discounts
Delegates Discount
5 3% off
10 6% off
15 9% off
20 12% off
25 15% off
30 18% off
50 30% off
75 40% off
100 40% off

3% off per 5 delegates, up to 40% for 100+

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