Data Science Courses - Zambia

Data Science Courses

Our Data Science Courses build essential skills in analysing data, applying statistical methods, and using Machine Learning techniques to extract meaningful insights. They support data-driven decision-making across industries by combining practical tools, analytical thinking, and real-world applications.

REASONS TO CHOOSE

Develop advanced analytical skills to extract meaningful insights from large datasets
Gain proficiency in statistical methods for data analysis with our Data Science Courses
Boost your marketability and career opportunities by adding valuable Data Science skills
Acquire proficiency in programming languages such as Python or R
Master data visualisation tools and techniques to communicate results clearly

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Data Science Courses

Duration

Online Instructor-led (1 days)

Online Self-paced (8 hours)

Keras Training for Data Scientists Course Outline

Module 1: Introduction to Keras

What is Keras?
Guiding Principles
Installation and Setup
Configuration

Module 2: Exploring Models

Keras Models
Sequential Model
Model (Functional API)

Module 3: Overview of Keras Layers

Core Layers
Convolutional Layers
Pooling Layers
Locally-Connected Layers
Recurrent Layers
Pre-Processing Layers
Merging Layers
Activations Layers
Normalisation Layers

Module 4: Pre-Processing

Dataset Pre-Processing
Dataset Pre-Processing Utilities
- Image Data Pre-Processing
- Time Series Data Pre-Processing
- Text Data Pre-Processing

Module 5: Optimisers

SGD
RMSprop
Adam and AdamW
Adadelta
Adagrad
Adamax
Adafactor
Nadam
Ftrl

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Who Should Attend this Keras Training for Data Scientists Course?

The Keras Training for Data Scientists is tailored for Data Scientists and professionals who want to gain proficiency in Deep Learning using the Keras framework. This Data Science Training is particularly beneficial for the following professionals:

Data Scientists
Machine Learning Engineers
Software Developers
Deep Learning Engineers
Medical Researchers
Bioinformaticians
Data Journalists

Prerequisites of the Keras Training for Data Scientists Course

There are no formal prerequisites for attending this Keras Training for Data Scientists Course. However, having some prior knowledge of Machine Learning concepts and Python programming can be beneficial.

Keras Training for Data Scientists Course Overview

Keras Training for Data Scientists introduces delegates to a powerful open-source framework for building neural networks. It explains how Keras supports rapid model development and integrates with Python-based deep learning environments, simplifying complex data science workflows.

This training supports professionals seeking to strengthen deep learning capability. It builds the ability to design, test, and refine neural network models efficiently, enabling delegates to meet the growing demand for intelligent, data-driven solutions across industries.

This 1-Day course offered by The Knowledge Academy enables delegates to apply Keras concepts with confidence. Through focused learning, delegates gain the skills to work with core and recurrent layers, preprocess data, and prepare text, images, and sequences for neural network applications.

Keras Training for Data Scientists Course Objectives

To grasp the fundamentals of Keras and its integration with Deep Learning frameworks
To explore various Keras layers for building neural networks
To learn the essentials of preprocessing data for sequence, text, and image applications
To gain proficiency in using regularisers and constraints to enhance model performance
To master the practical aspects of compiling models with loss and optimiser functions
To acquire the skills to architect neural networks efficiently for complex data science problems

Upon completing this course, delegates will possess a robust understanding of Keras, enabling them to efficiently experiment with and architect diverse neural network models. The acquired skills in preprocessing for sequences, text, and images will enhance their ability to tackle complex data science problems.

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What’s Included in this Keras Training for Data Scientists Course?

World-Class Training Sessions from Experienced Instructors
Keras Training for Data Scientists Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (2 days)

Online Self-paced (16 hours)

Pandas for Data Analysis Training Course Outline

Module 1: Introduction and Installation

Define Pandas
Installing Pandas
Running Test Suite
Dependencies

Module 2: Getting Started with Pandas

Package Overview
Exploring Pandas
Essential Basic Functionality
Comparison with Other Tools

Module 3: User Guide

IO Tools
Indexing
Merge, Join, and Concatenate
Reshaping and Pivot Tables
Working with Text Data, Missing Data, and Categorical Data
Nullable Integer Data Type
Computational Tools
Group By: Split-Apply-Combine
Time Series and Time Deltas
Stylings
Options and Settings
Enhancing Performance
Sparse Data Structures

Module 4: Pandas Ecosystem

Statistics and Machine Learning
Visualisation
IDE
Extension Data Types

Module 5: Development Phase

Extending Pandas
Storing Pandas DataFrame Objects in Apache Parquet Format

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Who Should Attend this Pandas for Data Analysis Training Course?

The Pandas for Data Analysis Course aims at equipping delegates with the knowledge and skills to manipulate, analyse, and visualise data using the Pandas library in Python. The following professionals can benefit greatly with this course:

Data Analysts
Data Scientists
Business Analysts
Researchers
Statisticians
Data Engineers
Machine Learning Engineers

Prerequisites of the Pandas for Data Analysis Training Course

There are no formal prerequisites to attend Pandas for Data Analysis Course. However, a basic knowledge of programming can be beneficial in this Data Analysis Training.

Pandas for Data Analysis Training Course Overview

Pandas for Data Analysis Training introduces delegates to a powerful Python library for data analysis and manipulation. It explains how structured tools enable efficient handling, cleaning, and transformation of datasets across analytical and business environments.

This training supports professionals working with data in Python. It builds the ability to manage diverse datasets, improve data quality, and extract meaningful insights, strengthening decision-making and analytical performance across industries.

This 2-Day course offered by The Knowledge Academy enables delegates to apply Pandas with confidence. Through focused learning, delegates gain the skills to work with varied data types and perform essential operations such as merging, reshaping, and transforming data effectively.

Pandas for Data Analysis Training Course Objectives

To master Pandas data structures and operations
To efficiently handle text, missing, and categorical data
To explore essential Pandas functions for merging, reshaping, and pivot tables
To acquire data visualisation skills and utilise integrated development environments
To understand data validation practices and work with extension data types
To gain proficiency in storing Pandas Data Frame objects in Apache Parquet format

Upon completing this Data Science Course, delegates will benefit from enhanced proficiency in data analysis using Pandas. They will be equipped with the skills to efficiently manipulate diverse datasets, perform essential Pandas operations, and utilise data visualisation techniques.

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What’s Included in this Pandas for Data Analysis Training Course?

World-Class Training Sessions from Experienced Instructors
Pandas for Data Analysis Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (3 days)

Online Self-paced (24 hours)

Python Data Science Course Outline

Module 1: Introduction of Python

What is Python?
What can be Done by Using Python Programming Language?
Why Python?

Module 2: Working with IPython

Launching IPython Shell and Jupyter Notebook
Keyboard Shortcuts in the IPython Shell
Special Commands of Python
- Pasting Code Blocks: %paste and %cpaste
- Running External Code: %run
- Timing Code Execution: %timeit
- %magic and %Ismagic
IPython’s In and Out Objects
IPython and Shell Commands
Errors and Debugging
Profiling and Timing Code

Module 3: Introduction to NumPy

Understand Data Types in Python
NumPy Arrays
Universal Functions
Aggregations: Min, Max and more
Computation on Arrays: Broadcasting
Comparison, Boolean Logic, and Masks
Fancy Indexing
Sorting Arrays
NumPy’s Structured Array

Module 4: Working with Pandas

Installing and Using Pandas
Pandas Objects
Data Indexing and Selection
Operating on Data in Pandas
Handling Missing Data
Hierarchical Indexing
Concat and Append
Merge and Join
Aggregations and Grouping
Pivot Tables
Vectorised String Operations
Working with Time Series
eval() and query()

Module 5: Visualisation with Matplotlib

Overview of Matplotlibs
Object Oriented Interface
Two Interfaces
Simple Line Plots and Scatter Plots
Visualising Errors
Density and Contour Plots
Histograms, Binnings, and Density
Customising Plot Legends
Customising Colorbars
Multiple Subplots
Text Annotation
Three-Dimensional Plotting in Matplotlib
Visualisation with Seaborn

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Who Should Attend this Python Data Science Course?

This Python Data Science Course is ideal for professionals who want to apply Python to data analysis, modelling, and data driven decision making. It is particularly beneficial for:

Software Developers
Data Analysts
Machine Learning Engineers
Product Managers
Business Analysts
Natural Language Processing Professionals
Data Scientists

Prerequisites of the Python Data Science Course

There are no formal prerequisites for attending this Python Data Science Training Course. However, a basic understanding of programming would be beneficial.

Python Data Science Course Overview

Python Data Science Training focuses on using Python for data analysis, visualisation, and manipulation tasks. It covers core programming concepts and data-focused techniques required to work confidently with structured datasets.

This Python Data Science Online Training helps delegates build practical data handling and analytical skills using Python libraries. Delegates improve readiness to analyse data, create visual insights, and support informed, data-driven decisions.

This 3-Day course offered by The Knowledge Academy enables delegates to apply Python data science techniques in real scenarios. Delegates learn to work with arrays, perform data manipulation, and visualise results to solve practical data challenges.

Python Data Science Course Objectives

To equip delegates with proficiency in Python
To establish a solid foundation in data manipulation using Python at the advanced level
To cultivate expertise in data visualisation techniques, including the creation of line plots, scatter plots, and the visualisation of three-dimensional functions
To instruct delegates in advanced data visualisation skills, such as crafting histograms, performing binning, estimating density, and customising plot legends and colorbars
To broaden programming versatility by imparting knowledge of multiple programming paradigms supported by Python

Upon completing this course, delegates will have the knowledge and skills required to personalise their Matplotlib visualisations as well.

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What’s Included in this Python Data Science Course?

World-Class Training Sessions from Experienced Instructors
Python Data Science Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (2 days)

Online Self-paced (16 hours)

Probability and Statistics for Data Science Training Course Outline

Day 1: Probability and Random Variables

Module 1: Basic Probability Theory

Probability Spaces
Conditional Probability
Independence

Module 2: Random Variables

Random Variables Intro
Discrete Random Variables
Continuous Random Variables
Conditioning on Events
Functions of Random Variables
Generating Random Variables

Module 3: Multivariate Random Variables

Introduction
Discrete & Continuous Multivariate Variables
Joint Distributions
Independence
Functions of Several Variables
Generating Multivariate Variables & Rejection Sampling

Module 4: Expectation

Expectation Operator, Mean and Variance
Covariance, Conditional Expectation

Module 5: Random Processes

Intro, Mean, and Autocovariance Functions
IID
Gaussian Process
Poisson Process
Random Walk

Day 2: Statistics and Applications (8 hours total)

Module 6: Convergence of Random Processes

Types of Convergence
Law of Large Numbers
Central Limit Theorem
Monte Carlo Simulation

Module 7: Markov Chains

Markov Property and Basic Concepts
Recurrence
Periodicity
Convergence
Introduction to Markov-Chain Monte Carlo (MCMC)

Module 8: Descriptive Statistics

What are Descriptive Statistics?
Examples and Types of Descriptive Statistics

Module 9: Frequentist Statistics

Mean Square Error
Consistency
Confidence Intervals
Parametric vs Nonparametric Model Estimation
Maximum Likelihood Estimation (MLE)

Module 10: Bayesian Statistics

Bayesian Parametric Models
Conjugate Priors
Bayesian Estimators

Module 11: Hypothesis Testing

Hypothesis-Testing Framework
Parametric Testing
Nonparametric Testing: The Permutation Test
Multiple Testing Correction

Module 12: Linear Regression

Introduction to Linear Regression
Linear Models & Applications
Least-Squares Estimation

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Who Should Attend this Probability and Statistics for Data Science Training?

This Probability and Statistics for Data Science Course is designed to provide foundational and practical knowledge in Probability and Statistics, which are crucial for Data Science, Machine Learning, and Data Analysis. The following are some professionals who will benefit from attending this course:

Data Scientists
Machine Learning Engineers
Data Analysts
Business Analysts
Product Managers
Quantitative Analysts
Statisticians

Prerequisites of the Probability and Statistics for Data Science Training

There are no formal prerequisites for this course.

Probability and Statistics for Data Science Training Course Overview

Probability and Statistics Training introduces delegates to the core concepts that underpin data-driven decision-making. It explains how uncertainty, variability, and data patterns are measured and interpreted, forming the foundation for meaningful analysis in Data Science.

This training supports professionals working with data in building analytical confidence. It develops the ability to quantify uncertainty, recognise trends, and draw accurate conclusions, enabling informed decisions across analytical and business-focused roles.

This 2-Day course offered by The Knowledge Academy enables delegates to apply probability and statistics with confidence. Through focused learning, delegates gain the skills to work with distributions, hypothesis testing, and regression, supporting practical, evidence-based decisions.

Probability and Statistics for Data Science Training Course Objectives

To represent and analyse uncertain phenomena using a framework
To quantify the outcome of the experiment as belonging to a specific event
To assign probabilities to each occurrence of interest and an experiment
To become accustomed to Markov chains and different statistical types
To generate samples from the appropriate conditional distribution
To evaluate the occurrence of a particular event that influences another event

Upon completion of this Data Science Training, delegates will possess a strong foundation in Probability and Statistics for Data Science. They will be equipped with the tools and techniques needed to analyse data effectively, make informed decisions, and contribute meaningfully to data-driven projects within their organisations.

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What’s Included in this Probability and Statistics for Data Science Training?

World-Class Training Sessions from Experienced Instructors
Probability and Statistics for Data Science Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (2 days)

Online Self-paced (16 hours)

Text Mining Training Course Outline

Module 1: Introduction to Text Mining

What is Text Mining?
Text Mining Systems Architecture

Module 2: Core Text Mining Operations

What are Core Text Mining Operations?
Text Mining Query Languages

Module 3: Text Mining Pre-Processing Techniques

Task-Oriented Approaches

Module 4: Categorisation

Text Categorisation Applications
Document Representation
Knowledge Engineering to TC
Using Unlabelled Data
Evaluating Text Classifiers

Module 5: Introduction to Clustering

Partitioning of Networks
Clustering Tasks in Text Analysis
Clustering Algorithms
Clustering of Textual Data

Module 6: Information Extraction (IE)

Define Information Extraction
IE Systems Architecture
Anaphora Resolution
IE Inductive Algorithms
Structural Information Extraction (IE)

Module 7: Probabilistic Models for IE

Hidden Markov Models
Stochastic Context-Free Grammar
Maximal Entropy Modelling
Conditional Random Fields

Module 8: Pre-Processing Applications

HMM to Textual Analysis Applications
Using MEMM for IE
Applications of CRFs to Textual Analysis MEMM for IE
Using SCFG Rules
Bootstrapping

Module 9: Presentation-Layer Considerations

Browsing
Accessing Constraints and Simple Specification Filters at the Presentation Layer
Accessing the Underlying Query Language

Module 10: Visualisation Approaches

Architectural Considerations
Text Mining Visualisation Approaches
Visualisation Techniques in Link Analysis

Module 11: Introduction to Link Analysis

Automatic Layout of Networks
Paths and Cycles in Graphs
Centrality
Partitioning of Networks
Networks Pattern Matching

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Who Should Attend this Text Mining Training Course?

This Text Mining Training Course is suitable for a wide range of professionals and individuals who want to gain expertise extracting information, sentiment, or patterns from unstructured text-based data. The following professionals can benefit from this training:

Data Scientists
Software Engineers
Data Analysts
Digital Marketers
Product Managers
Business Intelligence Analysts
NLP Engineers

Prerequisites of the Text Mining Training Course

There are no formal prerequisites for attending this Text Mining Course.

Text Mining Training Course Overview

Text Mining Training introduces delegates to the principles of analysing large volumes of text to uncover patterns, trends, and insights. It explains how structured techniques transform unorganised content from documents, reports, and digital platforms into meaningful information.

This training supports professionals working with data and content in building analytical capability. It develops the ability to extract value from textual sources, strengthening decision-making and providing a competitive advantage in data-driven environments.

This 2-Day course offered by The Knowledge Academy enables delegates to apply text mining concepts with confidence. Through focused learning and practical activities, delegates gain the skills to preprocess data, categorise text, evaluate classifiers, and refine analytical outcomes effectively.

Text Mining Training Course Objectives

To grasp the fundamentals of text mining and preprocessing techniques
To utilise key algorithms for efficient text categorisation
To evaluate and enhance text classifiers using unlabelled data
To gain expertise in clustering and Information Extraction (IE)
To explore techniques for constraint handling and specification filtering
To master the application of hidden Markov models and maximal entropy Markov models for Information Extraction

Upon completing the Data Science Online Training, delegates will possess a comprehensive skill set in text mining, enabling them to efficiently extract valuable insights from textual data, automate processes, and enhance decision-making in their respective professional domains.

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What’s Included in this Text Mining Training?

World-Class Training Sessions from Experienced Instructors
Text Mining Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (2 days)

Online Self-paced (16 hours)

Predictive Analytics Course Outline

Module 1: Introduction to Predictive Analytics

What is Predictive Analytics?
How Does Predictive Analytics Works?
Business Intelligence
Business Intelligence Lifecycle
Predictive Analytics Vs Business Intelligence
Predictive Analytics Challenges

Module 2: Setting Up the Problem

Predictive Analytics Processing Steps
Target Variable
Success Measures for Predictive Models
Data for Predictive Modelling

Module 3: Understanding the Data

Single and Multiple Variables
Data Visualisation
Histograms

Module 4: Data Preparation

Data Cleaning
Feature Creation

Module 5: Itemsets and Association Rules

Association Rules
Parameter Settings
Data Organisation Techniques
Deploying Association Rules
Making Classification Rules from Association Rules

Module 6: Descriptive Modelling

Introduction to Descriptive Modelling
Principal Component Analysis
Clustering Algorithms

Module 7: Interpreting Descriptive Models

Introduction to Descriptive Analytics
How Does Descriptive Analytics Work?
Advantages of Descriptive Analytics

Module 8: Predictive Modelling

Introduction to Predictive Modelling
Decision Tree
Types of Decision Trees
Logistic Regression
Neural Networks
K-Nearest Neighbour
Naïve Bayes
Linear Regression

Module 9: Predictive Models Assessment

Introduction to Predictive Models Assessment
Metrics to Evaluate Predictive Models

Module 10: Model Ensembles

Ensemble Modelling
Types of Model Ensembles
Bias Variance Trade-Off
Bagging and Boosting
Interpreting Model Ensembles

Module 11: Text Mining

Introduction to Text Mining
Structured Vs Unstructured Data
Text Mining Applications
Steps of Data Preparation
Regular Expressions

Module 12: Predictive Model Deployment

Introduction to Predictive Model Deployment
Building Predictive Models

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Who Should Attend this Predictive Analytics Course?

This Predictive Analytics Training Course aims to provide professionals with the core concepts, techniques, and tools used to predict future events based on historical data and delves into the various stages of the analytics life cycle. This training can help various professionals, including:

Data Scientists
Business Analysts
Marketing Analysts
Financial Analysts
Business Intelligence Professionals
Risk Managers
CRM Professionals

Prerequisites of the Predictive Analytics Course

There are no formal prerequisites required for attending this Predictive Analytics Training Course.

Predictive Analytics Course Overview

Predictive Analytics Training introduces delegates to techniques used to analyse historical and transactional data to forecast future outcomes. It covers core methods such as data modelling, statistical analysis, and pattern identification to support informed decision-making.

This certification enhances delegates’ analytical capabilities by building practical skills in identifying trends, risks, and opportunities. It improves readiness to support data-driven strategies, optimise operations, and strengthen organisational planning and resilience.

This 2-Day Predictive Analytics Course offered by The Knowledge Academy enables delegates to apply predictive analytics techniques confidently in real-world scenarios. Delegates learn data cleaning, feature creation, and predictive modelling methods to uncover actionable insights across industries.

Predictive Analytics Course Objectives

To master the predictive analytics process, including data cleaning and feature creation
To gain proficiency in uncovering valuable patterns and associations using itemsets and association rules
To explore various predictive modelling techniques, such as logistic regression and Naïve Bayes
To develop the skills to apply predictive models effectively in real-world scenarios
To enhance decision-making by leveraging data-driven predictions and insights
To empower organisations to become forward-looking and proactive using predictive analytics

Upon completing this Predictive Analytics Course, delegates will gain the skills to develop and apply predictive models effectively. They will be able to make data driven predictions, optimise decision making processes, and support new opportunities for organisational growth.

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What’s Included in this Predictive Analytics Course?

World-Class Training Sessions from Experienced Instructors
Predictive Analytics Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (1 days)

Online Self-paced (8 hours)

Introduction to Knime Analytics Platform Training Course Outline

Module 1: Installing KNIME Analytics Platform

Extensions and Integrations Installation
Update Sites

Module 2: Introduction to KNIME Analytics Platform

Building Workflows

Module 3: KNIME Workbench

Overview of KNIME Workbench
Customising KNIME Workbench
Configure KNIME Analytics Platform
KNIME Tables

Module 4: KNIME Flow Control

Introduction
Flow Variables

Module 5: KNIME Extensions and Integrations

Introduction to KNIME Extensions and Integrations
Community Extensions

Module 6: CSS Styling for JavaScript Views and QuickForm Nodes

Setup
Description of CSS Classes

Module 7: Data Preprocessing in KNIME

Introduction to Data Preprocessing
Types of Data Cleaning Methods
Data Transformation Techniques
Feature Selection and Reduction Methods
Handling Missing and Duplicate Data

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Who Should Attend this Introduction to KNIME Analytics Platform Training Course?

The Introduction to KNIME Analytics Platform Course is designed to provide an in-depth understanding of the KNIME platform, a leading open-source data analytics, reporting, and integration tool. This course can be beneficial for various professionals, including:

Data Scientists
Data Analysts
Data Engineers
Data Visualisation Experts
Machine Learning Engineers
Database Administrators
Marketing Analysts

Prerequisites of the Introduction to KNIME Analytics Platform Training Course

There are no formal prerequisites required for attending this Introduction to KNIME Analytics Platform Training Course.

Introduction to KNIME Analytics Platform Course Overview

KNIME Analytics Platform Training introduces delegates to an open-source environment for building data science workflows and applications. It explains how visual tools transform complex data into structured, reusable processes for effective analysis and insight generation.

This training supports professionals working with data across industries. It builds the ability to create visual workflows, analyse datasets without coding, and extract value from growing data volumes, strengthening capability in modern data-driven roles.

This 1-Day course offered by The Knowledge Academy enables delegates to apply KNIME with confidence. Through focused learning, delegates gain the skills to install and navigate the platform, manage tables, and build practical data workflows effectively.

KNIME Analytics Platform Course Objectives

To empower individuals with the skills to create data science applications using the KNIME Analytics Platform
To enable understanding of data intricacies and the design of code-free, drag-and-drop visual workflows
To cater to a broad spectrum of professionals, including data scientists, analysts, and business intelligence professionals
To provide practical knowledge on installing, updating, and navigating the KNIME Analytics Platform
To equip delegates with the ability to handle KNIME tables for data manipulation and analysis

Upon completing course, delegates will possess the proficiency to create data science applications effortlessly using the KNIME Analytics Platform. They will be equipped with the skills to navigate, manipulate, and analyse data efficiently, fostering the ability to automate data processes.

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What’s Included in this Introduction to Knime Analytics Platform Training?

World-Class Training Sessions from Experienced Instructors
Introduction to Knime Analytics Platform Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (2 days)

Online Self-paced (16 hours)

Data Mining Training Course Outline

Day 1: Foundations and Data Preparation

Module 1: Introduction to Data Mining

What is Data Mining?
Importance and Applications of Data Mining
Types of Data (Structured, Semi-structured, Unstructured)
Data Objects and Attribute Types (Nominal, Ordinal, Numeric)
Measuring Data Similarity and Dissimilarity
Introduction to Data Visualisation Techniques

Module 2: Data Preprocessing Essentials

Overview of Data Preprocessing
Data Integration and Aggregation Techniques
Data Cleaning Strategies (Missing Values, Noisy Data)
Data Reduction Techniques (Sampling, PCA, Feature Selection)
Data Transformation (Normalisation, Scaling, Encoding Categorical Data)
Data Discretisation Methods (Binning, Histogram Analysis)

Module 3: Data Warehousing and Online Analytical Processing (OLAP)

Basic Concepts of Data Warehousing
Introduction to Data Cubes and OLAP Operations
Multidimensional Data Models (Star Schema, Snowflake Schema)
Data Warehouse Design and Architecture
Implementing and Managing Data Warehouses
Practical Uses and Applications of Data Warehousing and OLAP

Module 4: Mining Frequent Patterns and Associations

Concept of Frequent Pattern Mining
Frequent Itemset Mining Algorithms (Apriori Algorithm, FP-Growth)
Association Rule Generation and Interpretation
Evaluation of Patterns (Support, Confidence, Lift, Conviction)
Real-world Application Scenarios and Examples (Market Basket Analysis)

Day 2: Advanced Analytics & Real-world Applications

Module 5: Classification Techniques and Algorithms

Introduction and Purpose of Classification
Decision Tree Induction (CART, ID3, C4.5)
Bayesian Classification (Naïve Bayes Classifier)
Rule-Based Classification Methods
Evaluation Metrics: Confusion Matrix, Accuracy, Precision, Recall, F1-Score
Practical examples using real-world datasets

Module 6: Advanced Classification Methods

Introduction to Neural Networks and Backpropagation (brief overview)
Frequent Pattern-based Classification Concepts
Lazy Learners (k-Nearest Neighbours - kNN)
Overview: Genetic Algorithms, Rough Set Approach, Fuzzy Logic (brief, conceptual coverage)
Strengths and Weaknesses of Advanced Classification Techniques

Module 7: Cluster Analysis Methods

Understanding Cluster Analysis and Applications
Partitioning Methods (k-Means and k-Medoids Clustering)
Hierarchical Clustering (Agglomerative, Divisive)
Density-based Clustering Methods (DBSCAN)
Grid-based Clustering Methods (Conceptual overview)
Evaluating and Visualising Cluster Results

Module 8: Outlier Detection & Advanced Clustering Techniques

Understanding Outliers and Importance of Detection
Statistical Approaches to Outlier Detection
Proximity-based Outlier Detection (Distance-based, Density-based)
Clustering-based and Classification-based Outlier Detection
Overview of Advanced Clustering: Probabilistic Model-Based Clustering (Gaussian Mixtures)
High-dimensional Data Clustering and Challenges (brief overview)

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Who Should Attend this Data Mining Training?

The Data Mining Course is tailored to impart knowledge on the process of extracting patterns, correlations, and information from large amounts of data stored in databases, data warehouses, or other information repositories. This course can be beneficial for various professionals aiming to derive insights from data, including:

Data Scientists
Business Analysts
Database Administrators
Marketing Analysts
Researchers
Statisticians
Machine Learning Engineers

Prerequisites of the Data Mining Training

There are no formal prerequisites required for attending this Data Mining Course. Although, a prior understanding of IT concepts can be helpful.

Data Mining Training Course Overview

Data Mining Training introduces delegates to identifying patterns and extracting valuable insights from large datasets using statistical and analytical techniques. It covers core concepts such as data preprocessing, visualisation, and data warehousing fundamentals.

This training strengthens delegates’ analytical capabilities by building practical skills in data transformation, pattern discovery, and interpretation. It enhances readiness to support data-driven decision making, fraud detection, and business and research analysis.

This 2-Day Data Mining Course offered by The Knowledge Academy enables delegates to apply data mining techniques confidently in real-world scenarios. Delegates gain a hands-on understanding of pattern mining, classification, clustering, and outlier detection to support informed analytical outcomes.

Data Mining Course Objectives

To comprehend Data Mining concepts and its practical applications
To acquire data preprocessing skills for effective analysis
To grasp data transformation and discretisation techniques
To understand data warehousing and Online Analytical Processing (OLAP)
To become proficient in frequent pattern mining and associations
To develop expertise in advanced pattern mining, classification, clustering, and outlier detection

Upon completing this Data Mining Training Course, delegates will gain advanced skills in extracting valuable insights from large datasets, enhancing their ability to make informed business decisions. Additionally, the course equips them with practical knowledge of cutting-edge data mining techniques.

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What’s Included in this Data Mining Training?

World-Class Training Sessions from Experienced Instructors
Data Mining Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (1 days)

Online Self-paced (8 hours)

GIS Development Training Course Outline

Module 1: Introduction to Geographic Information Systems (GIS)

What is GIS?
GIS Terminologies
Overview of ArcGIS Pro
Data Representations in ArcGIS Pro
Desktop GIS Software Packages
GIS Analyst Skills
Installing ArcGIS Desktop

Module 2: Basics of ArcGIS

Explore Data Using ArcGIS Pro
View and Change Layer Properties
Feature Classes and Attribute Tables
Select by Attribute and Calculate Geometry
Select by Location
Define Projections
Analyse Data with Geoprocessing Tools
Geoprocessing Environment Setting
Assess Spatial Relationships with Spatial Join Tool

Module 3: Making Maps with Common Datasets

Core Map Elements
Symbology: Changing How Data Looks
Setting Symbology in ArcGIS
Labelling Map Features
Making Map Books

Module 4: Retrieving and Sharing Data

Using Metadata to Document Data Products
Sharing Data and Maps
Joins and Relates

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Who Should Attend this GIS Development Training?

The GIS Development Course offers professionals the knowledge of the concepts, tools, and techniques necessary for developing Geographic Information Systems (GISs). This course can be beneficial for various professionals, including:

GIS Developers
GIS Analysts
Geospatial Engineers
Remote Sensing Specialists
Infrastructure Planners
Natural Resource Managers
Landscape Architects

Prerequisites of the GIS Development Training

There are no formal prerequisites required for attending this GIS Development Course.

GIS Development Training Course Overview

GIS Development Training introduces delegates to the principles of collecting, managing, analysing, and visualising spatial data. It explains how Geographic Information Systems transform location-based information into meaningful maps and insights for informed decision-making.

This training supports professionals working with location data across diverse fields. It builds the ability to analyse spatial information, optimise resources, and visualise complex data, enabling clearer planning and more accurate decisions.

This 1-Day course offered by The Knowledge Academy enables delegates to apply GIS concepts with confidence. Through focused learning, delegates gain the skills to understand spatial analysis, use ArcGIS, and work effectively with modern Geographic Information Systems.

GIS Development Training Course Objectives

To provide a comprehensive understanding of Geographic Information Systems (GIS) fundamentals
To explore the realm of spatial analysis and cartography within the GIS domain
To familiarise delegates with ArcGIS, the leading software tool for GIS
To demonstrate the evolution of GIS from traditional paper maps to electronic software packages
To equip delegates with the knowledge to analyse data using geoprocessing tools
To guide delegates in creating and using map packages, uploading them to ArcGIS online, and creating layer files and packages

Upon completion of this Data Science Training, delegates will possess a solid understanding of GIS fundamentals and proficiency in using ArcGIS, the industry-leading software. They will be able to analyse spatial data, create insightful maps, and leverage GIS capabilities for informed decision-making.

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What’s Included in this GIS Development Training Course?

World-Class Training Sessions from Experienced Instructors
GIS Development Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (1 days)

Online Self-paced (8 hours)

Decision Tree Modeling Using R Training Course Outline

Module 1: Introduction to Decision Tree

Decision Tree Modelling Objective
Anatomy of a Decision Tree
Important Terminology Related to Decision Trees

Module 2: Overview of R Programming

R Programming Language
Data Types
Control Structures in R

Module 3: Data Treatment Before Modelling

Data Sanity Check-Contents
View
Frequency Distribution
Uni-Variate
Categorical Variable Treatment

Module 4: Classification of Tree Development and Algorithm Details

Installing R Package and R studio
Developing First Decision Tree in R Studio
Find Strength of the Model

Module 5: Decision Tree Analysis in Project Management

Use Decision Tree in Project Management
Tools for Decision Tree Analysis
Decision Tree Analysis Strategy

Module 6: Regression Tree and Auto Pruning

Introduction to Pruning
Understand K Fold Validation for Model 
Develop Regression Tree 
How is it Different from Linear Regression? 
Advantages and Disadvantages over Linear Regression

Module 7: CHAID Algorithm

What is CHAID/CART Algorithm?
Chi-Square Statistics 
Implement Chi-Square for Decision Tree Development 
CHAID Vs CART

Module 8: Other Algorithms

ID3
Random Forest Method
Using R for Random Forest Method

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Who Should Attend this Decision Tree Modelling Using R Training?

The Decision Tree Modelling Using R Training Course offers a detailed exploration of Decision Tree models, one of the most widely used algorithms in Machine Learning and Data Science. This course can be beneficial for various professionals, including:

Data Scientists
Machine Learning Engineers
Data Analysts
Research Scientists
Quantitative Researchers
Risk Assessment Managers
Predictive Modelers

Prerequisites of the Decision Tree Modelling Using R Training Course

There are no formal prerequisites for attending this Decision Tree Modelling Using R Training Course. However, having some knowledge of the R programming language will be helpful.

Decision Tree Modelling Using R Training Course Overview

Decision Tree Modelling Using R Training introduces delegates to a powerful analytical method for data-driven decision-making. It explains how decision trees support pattern recognition, prediction, and optimisation across industries such as finance, automotive, and telecommunications.

This training supports professionals seeking to strengthen data science capability. It builds the ability to analyse complex datasets, identify meaningful patterns, and derive actionable insights, enabling confident, evidence-based decisions in data-driven environments.

This 1-Day course offered by The Knowledge Academy enables delegates to apply Decision Tree Modelling using R with confidence. Through focused learning, delegates gain the skills to design models, understand algorithms, validate outcomes, and apply best practices in real-world scenarios.

Decision Tree Modelling Using R Training Course Objectives

To understand the fundamentals of Decision Tree Modelling
To learn data treatment and frequency distribution techniques
To explore Decision Tree algorithm development and pruning
To gain expertise in advanced topics like Random Forest and CHAID Algorithm
To acquire practical skills in using R for Decision Tree Modelling
To become proficient in applying Decision Tree Modelling to real-world scenarios

By the end of this course, delegates will emerge with expertise in Decision Tree Modelling using R, empowering them to leverage this powerful tool for data analysis and informed decision-making.

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What’s included in this Decision Tree Modelling Using R Training?

World-Class Training Sessions from Experienced Instructors
Decision Tree Modelling Using R Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (1 days)

Online Self-paced (8 hours)

PySpark Training Course Outline

Module 1: Introduction to PySpark

What is PySpark?
Environment
Spark Dataframes
Reading Data
Writing Data
MLlib

Module 2: Installation

Using PyPI
Using PySpark Native Features
Using Virtualenv
Using PEX
Dependencies

Module 3: DataFrame

DataFrame Creation
Viewing Data
Applying a Function
Grouping Data
Selecting and Accessing Data
Working with SQL
Get () Method

Module 4: Setting Up a Spark Virtual Environment

Understanding the Architecture of Data-Intensive Applications
Installing Anaconda
Setting a Spark Powered Environment
Building App with PySpark

Module 5: Building Batch and Streaming Apps with Spark

Architecting Data-Intensive Apps
Build a Reliable and Scalable Streaming App
Process Live Data with TCP Sockets
Analysing the CSV Data
Exploring the GitHub World
Previewing App

Module 6: Learning from Data Using Spark

Classifying Spark MLlib Algorithms
Spark MLlib Data Types
Clustering the Twitter Dataset
Build Machine Learning Pipelines

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Who Should Attend this PySpark Course?

This PySpark Training Course covers the fundamentals of Spark, its architecture, and how to use the PySpark API for Data Processing, Analytics, and Machine Learning tasks. This course can be beneficial for various professionals, including:

Data Engineers
Big Data Analysts
Data Scientists
Machine Learning Engineers
Software Developers
Python Developers
Solution Architects
System Administrators
Database Administrators

Prerequisites of the PySpark Course

There are no formal prerequisites required for attending this PySpark Training Course.

PySpark Training Course Overview

PySpark Training introduces delegates to a powerful framework for large-scale data processing and analytics. It explains how PySpark, as a Python API for Apache Spark, enables efficient handling of big data and supports modern data science workflows.

This training supports professionals working with large datasets in building advanced analytical capability. It develops the ability to process data at scale, apply machine learning techniques, and derive insights, strengthening effectiveness in data-driven environments.

This 1-Day course offered by The Knowledge Academy enables delegates to apply PySpark concepts with confidence. Through focused learning and practical activities, delegates gain the skills to work with big data, perform analytics, and process large datasets efficiently.

PySpark Training Course Objectives

To provide a comprehensive understanding of PySpark fundamentals
To cover advanced topics such as Big Data analytics using PySpark
To offer hands-on experience in applying PySpark for data processing and analytics
To equip professionals with the skills to efficiently handle large-scale data processing tasks
To empower delegates to leverage PySpark for Machine Learning applications

Upon completion of this course, the delegates will possess the skills to effectively utilise PySpark for Big Data processing and analytics. They will have hands-on experience in applying PySpark for Machine Learning applications, enhancing their proficiency in handling large-scale data tasks.

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What’s Included in this PySpark Training Course?

World-Class Training Sessions from Experienced Instructors
PySpark Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (4 days)

Online Self-paced (32 hours)

Advanced Data Science Certification Course Outline

Module 1: Python for Data Analysis - NumPy

Introduction to NumPy
NumPy Arrays
Aggregations
Computation on Arrays: Broadcasting
Comparison, Boolean Logic and Masks
Fancy Indexing
Sorting Arrays
NumPy’s Structured Arrays

Module 2: Python for Data Analysis – Pandas

Installing Pandas
Pandas Objects
Data Indexing and Selection
Operating on Data in Pandas
Handling Missing Data
Hierarchical Indexing
Concat and Append
Merge and Join
Aggregations and Grouping
Pivot Tables
Vectorised String Operations
Working with Time Series
Eval() and Query()

Module 3: Python for Data Visualisation – Matplotlib

Overview
Object-Oriented Interface
Two interfaces
Simple Line Plots and Scatter Plots
Visualising Errors
Contour Plots
Histograms, Binnings and Density
Customising Plot Legends
Customising Colour Bars
Multiple Subplots
Text Annotation
Three-Dimensional Plotting

Module 4: Python for Data Visualisation – Seaborn

Installing Seaborn and Load Dataset
Plot the Distribution
Regression Analysis
Basic Aesthetic Themes and Styles
Distinguish between Scatter Plots, Hexbin Plots and KDE Plots
Use Boxplots and Violin Plots
Compare the Use Cases of Swarn Plots, Bar Plots Strip Plots, and Categorical Plots
Recall Some of the Use Cases and Features of Seaborn

Module 5: Machine Learning

Introduction
Importance
Types
How Does Machine Learning Works?
Machine Learning Mathematics

Module 6: Natural Language Processing

Introduction to NLP
NLP and Writing Systems
Advantages
NLP Applications

Module 7: Deep Learning

Introduction
Importance
Working

Module 8: Big Data

Big Data Analytics
State of Practice in Analytics
Main Roles for New Big Data Ecosystem
Phases of Data Analytics Lifecycle

Module 9: Working with Data in R

Data Manipulation in R
Data Clean Up
Reading and Exporting Data
Importing Data
Charts and Graphs

Module 10: Regression in R

Regression Analysis
Linear Regression
Logistic Regression
Multiple Regression
Normal Distribution
Binomial Distribution

Module 11: Modelling Data

What are the Relationships?
Viewing Relationships
Creating Relationships
Cardinality
Cross Filter Direction
What is DAX?
Syntax
Functions
Row Context
Calculated Columns
Calculated Tables
Measures

Module 12: Shaping and Combining Data using Power BI

Query Editor
Shaping Data and Applied Steps
Advanced Editor
Formatting Data
Transforming Data
Combining Data

Module 13: Interactive Data Visualisations

Page Layout and Formatting
Multiple Visualisation
Creating Charts
Using Geographic Data
Histograms
Power BI Admin Portal
Service Settings
Desktop Settings
Dashboard and Report Settings

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Who Should Attend this Advanced Data Science Certification Course?

This Advanced Data Science Certification Course is tailored for individuals seeking to enhance their expertise in the field of data science. This training is particularly beneficial for professionals such as:

Experienced Data Analysts
Data Scientists
Machine Learning Engineers
Statisticians
Business Analysts
AI Developers
Entrepreneurs
Researchers

Prerequisites of the Advanced Data Science Certification Course

There are no formal prerequisites for the Advanced Data Science Certification Course. However, having prior knowledge of multiple programming languages will be beneficial for the delegates.

Advanced Data Science Certification Course Overview

Advanced Data Science Training introduces delegates to key concepts that underpin modern data-driven practice. It explains how techniques such as numerical computing, regression, and machine learning mathematics enable deeper analysis and informed decision-making across complex datasets.

This Advanced Data Science Training supports professionals seeking to strengthen advanced analytical capability. It builds the ability to identify patterns, interpret data accurately, and drive innovation, enabling confident contribution in data-focused and strategic roles across industries.

This 4-Day course offered by The Knowledge Academy enables delegates to apply advanced data science concepts with confidence. Delegates gain the skills to work with time series, visualise data, manage analytics lifecycles, and operate at an expert level.

Advanced Data Science Certification Course Objectives

To provide a comprehensive understanding of basic and advanced data science concepts
To equip delegates with practical skills in tools such as Pandas and Power BI
To cover critical topics like NumPy arrays, regression analysis, and Machine Learning mathematics
To empower Business Analysts, Data Engineers, Software Developers, and executives with data science proficiency
To enable delegates to unravel complex patterns and make informed decisions through data-driven insights
To ensure delegates emerge as adept Data Scientists capable of tackling real-world data challenges

Upon completion of this Data Science Certification training, delegates will not only possess a solid foundation in data science but also practical skills in tools like Pandas and Power BI. This empowers them to analyse data effectively, make informed decisions, and contribute meaningfully to their organisations' success.

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What’s Included in this Advanced Data Science Certification Course?

World-Class Training Sessions from Experienced Instructors
Advanced Data Science Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (2 days)

Online Self-paced (16 hours)

Data Science with R Training Course Outline

Module 1: Introduction to R

What is R?
Variables
Data Types
Operators
Conditional Statements
Looping Statements
Functions

Module 2: Data Structures in R

What are Data Structures?
Vectors
Lists
Matrix

Module 3: Working with Data in R

Types of Files in R
Working with CSV

Module 4: Data Manipulation in R

What is Data Manipulation?
Installation of Dplyr Package
Data Manipulation Operations in R

Module 5: Data Visualisations in R

What is Data Visualisation?
Working with Graphs and Plots in R

Module 6: Statistics in R

Introduction to Statistics in R
Introduction to Descriptive Statistics
Distributions in R

Module 7: Machine Learning

Introduction to Machine Learning in R
Types of Machine Learning in R
Introduction to Supervised Learning in R
Introduction to Unsupervised Learning in R

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Who Should Attend this Data Science with R Training?

The Data Science with R Training Course is designed for professionals, analysts, and individuals who are interested in acquiring skills and knowledge in data analysis, visualisation, and modelling using the R programming language. This course is beneficial for various professionals, including:

Data Analysts
Data Scientists
Statisticians
Business Analysts
Quantitative Finance Professionals
Epidemiologists and Public Health Experts
Data Engineers
Geographic Information System (GIS) Analysts

Prerequisites of the Data Science with R Training

There are no formal prerequisites for attending this Data Science with R Training.

Data Science with R Training Overview

Data Science with R Training introduces delegates to using the R programming language for analysing, modelling, and visualising data. It explains how R transforms raw datasets into meaningful insights, supporting informed decision-making across data-driven environments.

This training supports professionals seeking to strengthen analytical capability with R. It builds the ability to preprocess data, perform statistical analysis, and develop predictive models, enabling delegates to extract value from complex datasets across industries.

This 2-Day course offered by The Knowledge Academy enables delegates to apply Data Science with R confidently. Through focused learning, delegates gain the skills to work with data structures, manipulate datasets, and enhance analytical outcomes using R.

Data Science with R Training Course Objectives

To provide a comprehensive understanding of the R programming language in the context of Data Science
To equip delegates with the skills to preprocess data, construct predictive models, and perform statistical analyses using R
To empower individuals to effortlessly conduct complex computations and statistical analyses using R tools
To deliver expertise in working with essential tools like data frames, matrices, vectors, and lists in the R language
To enhance proficiency in data manipulation for refining and adapting data for analytical capabilities
To ensure mastery of R programming through hands-on training and practical applications in Data Science

Upon completion of this Data Science with R Training, delegates will possess comprehensive knowledge and mastery of the R programming language. They will be equipped with the skills to preprocess data, construct predictive models, and perform statistical analyses, enhancing their Data Science capabilities.

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What’s Included in this Data Science with R Training Course?

World-Class Training Sessions from Experienced Instructors
Data Science with R Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (1 days)

Online Self-paced (8 hours)

Data Science and Blockchain Training Course Outline

Module 1: Introduction to Data Science

What is Data Science?
How Does Data Science Work?
Data Science Life Cycle
Roles and Responsibilities of a Data Scientist
Importance of Data Science
Data Science Applications
Business Intelligence Vs Data Science

Module 2: Blockchain Overview

Blockchain Technology
Why is Blockchain Important?
How Does Blockchain Work?
Decentralisation in Blockchain
Blockchain Uses
Blockchain Applications

Module 3: Implications of Blockchain in Data Science

Relationship Between Blockchain and Data Science
How Blockchain can Help Big Data?
How Blockchain will Enhance Data Science?

Module 4: Blockchain in Big Data Transformation

Introduction
What is a Blockchain, and How Does it Work?
Bringing Blockchain and Big Data Together
Things That Blockchain Transforms Big Data

Module 5: Blockchain Storage

What is Blockchain Storage?
What Will Blockchain Mean for Data Storage?
Data Flow Through a Blockchain
Blockchain Data Storage Solutions
Why is Data Storage is Shifting to the Blockchain?
Issues with Centralised Data Centres

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Who Should Attend this Data Science and Blockchain Training?

The Data Science and Blockchain Course is designed for those who want to get better at understanding the intricacies surrounding Data Science and Blockchain for complex problem-solving and application development. The Data Science and Blockchain Training Course can benefit professionals such as:

Data Scientists
Blockchain Developers
Business Analysts
Software Engineers
Project Managers
Supply Chain Professionals
Ethical Hackers

Prerequisites of the Data Science and Blockchain Training

There are no formal prerequisites for this Data Science and Blockchain Training Course.

Data Science and Blockchain Training Course Overview

Data Science and Blockchain Training introduces delegates to the principles of extracting insight from data and securing it through decentralised ledger technology. It explains how analytical methods and Blockchain combine to support trusted, data-driven decision-making.

This training supports professionals seeking to build capability across data science and Blockchain. It develops the ability to contribute innovatively in data-driven environments, strengthening career growth and readiness for leadership in emerging technology roles.

This 1-Day course offered by The Knowledge Academy enables delegates to apply Data Science and Blockchain concepts with confidence. Through focused learning, delegates gain the skills to understand data roles, apply modelling techniques, and explore practical Blockchain solutions.

Data Science and Blockchain Training Course Objectives

To learn how to alter information about the records retrospectively
To analyse data and track transactions to make better decisions
To understand the entire process of gathering actionable insights from raw data
To speed up the work process and reduces the time taken to obtain and analyse data
To identify dangerous or fraudulent transactions and prevent fraud entirely
To identify trends, models, and threats through data production and exchange

Upon completion of this 1-day course, delegates will gain a deep understanding of the synergies between data science and blockchain technology. They will be equipped with practical skills to navigate complex data segments, predict outcomes, and unlock blockchain's solutions.

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What’s Included in this Data Science and Blockchain Training?

World-Class Training Sessions from Experienced Instructors
Data Science and Blockchain Certificate
Digital Delegate Pack

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Not sure which course to choose?

Speak to a training expert for advice if you are unsure of what course is right for you. Give us a call on +44 1344 203 999 or Enquire.

Skills You Will Gain from Data Science Training

The Knowledge Academy’s Data Science Courses equip learners with analytical and technical expertise to interpret data and drive strategic business decisions. Learners gain the ability to extract insights, predict outcomes, and create data-driven solutions. Key skills gained through this training include:

Advanced Analytical Skills: Delegates learn to collect, process, and analyse complex data using modern tools and techniques.
Programming and Technical Proficiency: Gain hands-on experience in Python, R, SQL, and data visualisation tools to handle large datasets effectively.
Informed Decision-Making: Professionals can use predictive analytics and data models to support evidence-based business strategies.
Career Flexibility and Demand: Learners can stand out in an expanding field with opportunities across finance, healthcare, technology, and more.
Problem-Solving and Innovation: Develop the ability to translate data into actionable insights that improve efficiency and performance.

Career Scope After Data Science Training

The Knowledge Academy’s Data Science Training opens a range of high-demand roles across global industries. Learners gain the skills to lead analytical projects and contribute to data-driven transformation. The following are some of the prominent career opportunities available after completing this training:

Data Scientist: Build predictive models, apply algorithms, and generate insights that shape business strategies.
Data Analyst: Interpret data trends, create dashboards, and provide actionable reports for decision-makers.
Machine Learning Engineer: Design and deploy intelligent models that enable automation and predictive capability.
Business Intelligence Specialist: Combine analytics and visualisation to help organisations identify trends and improve outcomes.
Data Engineer: Develop data pipelines and architecture that support analysis, storage, and scalable data access.
AI Specialist: Advance into roles that apply deep learning and artificial intelligence to complex problems.

Data Science Courses FAQs

What is Data Science?

Data Science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract insights from structured and unstructured data, helping organisations make data-driven decisions and predictions.

What are the Data Science Courses about?

Data Science Courses focus on turning raw data into meaningful insight. They teach you how to collect, analyse, and interpret information using tools like Python, statistics, and visualisation, helping you make informed, data-driven decisions across real business scenarios.

What are the roles and responsibilities of a Data Scientist?

A Data Scientist analyses complex data, builds predictive models, and develops Machine Learning algorithms to uncover insights. They interpret results, optimise data strategies, and communicate findings to support informed business decisions.

What are the benefits of obtaining a Data Science Certification?

Obtaining a Data Science Certification through Data Science Courses strengthens your ability to analyse data, apply machine learning, and support data-driven decisions. It enhances career prospects, credibility, and earning potential across industries seeking skilled data professionals.

Can I pursue Data Science Courses without a background in mathematics or statistics?

Yes, you can learn Data Science without a strong background in mathematics or statistics. Our Science Courses starts with foundational concepts and gradually introduces essential analytical techniques in a structured and accessible way.

Are Data Analytics Courses and Data Science Courses the same?

No, Data Analytics and Data Science Courses are different. Data Analytics focuses on interpreting existing data for decision-making, while Data Science involves advanced techniques like machine learning to predict future trends and patterns.

How can I access the content for Data Science Courses?

You will be able to access the course material using MyTKA Training Portal for the Data Science Courses. It will be available to you even after the training period, so that you can constantly refer to and learn.

Are there any prerequisites for Data Science Courses?

The prerequisites for the Data Science Courses are based on the course specifications and the target group of professionals they serve. Check the respective course page of the course that you are planning to take to know about its prerequisites.

What are the best advanced Data Science Courses?

Some of the best advanced Data Science Courses include Advanced Data Science Certification, Predictive Analytics, PySpark Training, and Data Science with R. Specialised options such as Keras Training, Text Mining, Data Mining, and Decision Tree Modelling also support deeper expertise in analytics and machine learning.

Does TKA offer support to candidates taking up Data Science Training Courses?

Yes, The Knowledge Academy offers support via phone & email before attending, during, and after the Data Science Courses. Our customer support team is available to assist and promptly resolve any issues you may encounter.

What is the next step after completing Data Science Courses?

After completing a Data Science Online Courses, you can apply for roles like Data Scientist or Analyst, specialise in areas like machine learning or big data, or pursue advanced certifications to enhance your expertise.

What topics are covered in these Data Science Courses?

Our Data Science Courses cover topics such as Python and R programming, data analysis with Pandas, statistics and probability, data mining, predictive analytics, and machine learning techniques. They also include text mining, big data processing, visualisation, and emerging areas like blockchain and analytics platforms.

What is the average salary hike that one can expect on completing Data Science Courses?

Completing Data Science Courses can lead to an average salary increase of 15–30%, depending on your prior experience, role, and industry. The boost reflects high demand for data skills and the ability to contribute to data-driven decision-making and analytics initiatives.

How beneficial is it to learn programming languages for Data Science?

Learning programming languages is essential in data science because they let you prepare, analyse, model and visualise data efficiently. Languages like Python and R are widely used to build statistical models and machine learning solutions that drive real business decisions.

Do Data Science Training Courses cover programming languages other than Python and R?

Yes, our Data Science Training Courses cover additional tools and languages beyond Python and R. Depending on the course, this includes SQL, PySpark, and platforms such as KNIME, supporting broader data processing and analytics skills.

Why choose The Knowledge Academy in Zambia over others?

The Knowledge Academy stands out as a prestigious training provider known for its extensive course offerings, expert instructors, adaptable learning formats, and industry recognition. It's a dependable option for those seeking Data Science Courses.

What are the best Data Science Courses in Zambia?

Please see our Data Science Courses available in Zambia

Which is the best training institute/provider of Data Science Courses in Zambia?

The Knowledge Academy is one of the Leading global training provider for Data Science Courses.

What is the cost/training fees for Data Science Courses in Zambia

The training fees for Data Science Courses in Zambia starts from $3795

Why we're the go to training provider for you

Best price in the industry

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Many delivery methods

Flexible delivery methods are available depending on your learning style.

High quality resources

Resources are included for a comprehensive learning experience.

"Really good course and well organised. Trainer was great with a sense of humour - his experience allowed a free flowing course, structured to help you gain as much information & relevant experience whilst helping prepare you for the exam"

Joshua Davies, Thames Water