Africred Remittance

Master In Data Science

We know the amount of data produced globally daily is 2.5 quintillion bytes, which is enormous and is expected to keep increasing as the world’s population gets more access to the internet. The data is now considered a commodity that is more valuable than oil, and buried in these data are answers to countless questions. Data Science helps to deal with these vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. When the opportunity in data science comes knocking at your door, will you be ready?

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12 month


US $5000

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    Program Overview

    The Master in Data Science program aims to prepare students with a well-rounded education in different areas of Data Science to prepare themselves to become successful data scientists. The curriculum is made up of 4 core modules and 2 specialization modules providing in-depth knowledge in the field of Data science and its industrial application. Students will learn how to work with data to solve complex problems. Topics covered include Python, Data Science Algorithms, Data Analytics in Business & Data Mining. In this online masters degree in data science, students will also learn two specialization modules in (I) Statistical Data Modelling and (II) Applications of Data in Artificial Intelligence & Block Chain. At the end of course modules, all students are required to undertake a capstone project in Data Science to have hands-on working experience by solving real-world problems. At the end of course modules, all students are required to undertake a capstone project in Data Science to have hands-on working experience by solving real-world problems.

    Training Key Features


    • 200 hours of live instructor-led training
    • 3 industry based projects, 6 assignments, 6 Case Studies
    • 24/7 support and LMS Access
    • Hands on experience with latest tools and applied projects
    • Live engagement classes by seasoned academics and professionals
    • Internship/Projects
    • Flexible timing for working professionals
    • EMI option


    Module Description

    This module inculcates practical understanding and a framework that allows the execution of essential analytics actions such as extracting, cleaning, changing, and analysing data. In this module, learners grasp the knowledge of programming languages, tools, frameworks, and libraries utilised throughout the course to acquire and model data sets. Data analysis is accomplished through visualising, summarising, and developing rudimentary data handling abilities by paying attention to variable types, names, and values. In addition, managing data using dates, strings, and other elements, enhances learners’ abilities to perform data research and generate visualisations.

    Learning Outcomes

    L01: Analyse information using data visualisation, summary, and counting tools.

    L02: Acquire rudimentary skills in data handling, focusing on variable types, names, and values.

    L03: To learn how to use the pipe operator to combine numerous tidying operations in a chain.

    L04: The ability to work with data that includes dates, strings, and other variable

    Content Covered

    •  Data Cleaning Techniques
    •  Data Preprocessing
    •  Data Manipulation
    •  Core Python Programming
    •  Data Visualisation using Matplotlib
    •  Linear Algebra
    •  Statistics and Probability
    •  Exploratory data analysis
    •  Variance, Standard Deviation, Median
    •  Bar charts and Line charts
    •  Python libraries and framework in data analysis
    •  2D Scatter Plot
    •  3D Scatter plot
    •  Pair plots
    •  Univariate, Bivariate, and Multivariate
    •  Histograms
    •  Boxplot
    •  IQR (InterQuartile Range)
    •  Data analysis with Pandas

    Program Details

    Course Description

    Course Description

    This module gives learners the insight to apply many prediction models and grasps linear regression. Create predictions based on a group of input variables using regression analysis methods. Learners investigate how to model an extensive range of real-world interactions using complicated statistical methodologies, such as generalised linear and additive models. This module inculcates intermediate and advanced statistical modelling methodologies. It is specifically created for learners to develop proficiency in linear regression analysis, experimental design, and extended linear and additive models. Based on these skills, interpreting data, discovering links between variables, and generating predictions are made simpler via intuitive representations.

    Learning Outcomes

    LO1: Differentiate between various types of predictive models and Master linear regression

    LO2: Understand the inner workings through algorithms of different models

    LO3: Analyse and explore the results of logistic regression and understand when to discriminant analysis

    LO4: Maximise analytical productivity by analysing different models and interpreting their accuracy in a well-organised manner

    Content Covered

    •  Selecting a Sample
    •  Point Estimation
    •  Sampling Distributions
    •  Interval Estimation
    •  Hypothesis Tests
    •  Statistical Inference and practical significance
    •  A Simple Linear Regression Model
    •  Least Square method
    •  Inference and Regression
    •  Multiple Regression Model
    •  Logistics Regression
    •  Predictions with Regression
    •  Model Fitting
    •  Tableau data model
    •  Shape and data transformation using Tableau Query Editor
    •  Tableau Report View

    Course Description

    In this module, learners will better grasp artificial intelligence (AI) applications in business and comprehend AI decision-making. Through breakthroughs in IoT and the emergence of Blockchain, this curriculum prepares students with a broad foundation of AI-enabled software solutions. As learners continue through this module, they become acquainted with the technology that powers the automated world—knowing the sorts of algorithms and how they may be utilised to enhance or replicate human behaviour across diverse applications. This module teaches about AI, IoT, Blockchain, and machine learning components while building on a solid conceptual framework that will present rigorous, hands-on, and step-by-step ways to tackle realistic, complex real-world challenges.

    Learning Outcomes

    LO1. Introducing Artificial Intelligence (AI), exploring its features and variants in the business domain. Furthermore, to understand the business context of AI and interpret AI decision-making.

    LO2. Understand & create an AI implementation plan for a business setup through recognition of suitable model parameters

    LO3: To further explore the components of Blockchain and understand Distributed Ledger Technology (DLT) concept, features, benefits, and relevance in application

    LO4: Understanding Hyperledger, Smart Contracts, and IoT (Internet of Things) in applied business models to assess the impact in the long term

    Content Covered

    •  Introduction to Artificial intelligence
    •  AI enables applications
    •  What is Deep Learning
    •  Artificial Neural Networks
    •  Image Processing and OpenCV
    •  Introduction to NLP
    •  Artificial Neural Networks
    •  Text Processing
    •  Text Classification
    •  Topic Modelling
    •  Recurrent Neural Networks
    •  Major components of IoT
    •  Variety of Sensors
    •  Actuators
    •  IoT protocols at various layers
    •  Applications and user interface in IoT
    •  Smart factories of tomorrow and the Industrial Internet of Things
    •  Introduction to Blockchains
    •  Introduction and usage of Hyperledger and Smart Contract
    •  Blockchains Structure
    •  Centralised, Decentralised, and Distributed systems
    •  Introduction to DLT
    •  DLT features, benefits, and usage in Blockchain
    •  Types of Blockchains
    •  Why Blockchain?
    •  Building AI and ML applications using Blockchain technology

    Module Description

    The purpose of this module is to discuss and explain the role of Data science and its practices in an organisation and their influence on the overall performance and competence of the organisation. This module is designed to develop an understanding of the contemporary practices and competence to develop a research or design question, illustrate how it links to current knowledge and carry out the study in a systematic manner. Learners will be encouraged to pick a research/development project that displays their past learning in the data science domain. It is meant to acquire an understanding of Data Science and the paradigm shift in the approaches and methods related to various functions of DS like data visualisation, probability, inference and modelling, data mining, data organisation, regression, and machine learning to name a few. It also endeavours to highlight the role and significance of data analytics and data modelling during the planning, decision-making, and implementation of change in the organisation. Upon completing the module, the participants will have comprehensive knowledge about the broader data analysis context and a data product to demonstrate their data science expertise to potential employers or educational programs.

    Learning Outcomes

    LO1: Conduct independent Research and Development within the context of a Data Science Project.

    LO2: Developing the ability to solve problems using analytics and data science independently.

    LO3: Communicate technical information clearly and succinctly to a broad, non-specialist audience.

    LO4: Create detailed written documentation to a standard expected of a professional in the field of Data Science & evaluate Project outcomes concerning key research publications in the relevant field.

    If you’d like to gain the complete skill set to succeed in today’s business world, this is the area for you.

    Entry Requirements

    Bachelor’s Degree from a recognized University
    Proficiency in English language


    Due to its involvement in modern Machine Learning algorithms with maths and programming, candidates having knowledge of linear algebra, probability, and calculus could be a plus.

    Skills Covered

    1. Python for data science
    2. Data analysis
    3. Data Mining
    4. Data Analytics in Business
    5. Algorithms in Data Science
    6. Data in AI & Blockchain


    1. Bachelor’s Degree from a recognized University
    2. Proficiency in English language

    Tools/ Frameworks/ Libraries

    Scripting Tools
    Pandas, numPy, seaborn, matplotlib, cufflinks, scikit, NLTK, CoreNLP, spaCy, PyNLP, Tensorflow, Keras, Open CV, Power BI, Excel
    IDE Shell
    Jupyter Notebook, google colab, pycharm, visualstudio code

    Application and Use Cases

    Traffic Management: Data Science can identify the cause of congestion & manage traffic effectively
    Road Safety: Data Science can help us identify accident hotspots & recommend safety measures
    eCommerce: Data Science can gain behaviour patterns & provide recommendations to customers
    Banking: Data Science can handle customer data, detect fraud, manage credit risk in allotting loans
    Marketing & Sales: Data Science helps businesses to market & sell product to the right audience.
    Health Care: Data Science is used for drug discovery, predicting anomalies, and monitoring patient health
    Forecasting: Data Science can be used to predict future happenings by analysing historical data.
    Manufacturing: Data Science can automate large-scale processes & speed up implementation time
    Retail: Data Science can help with demand forecasting, pricing decisions & optimise product placement

    Learning Outcomes

    Live & interactive lectures by expert faculties

    Recorded session for offline viewing

    World-class curriculum by eminent faculty

    Regular webinars by industry leaders

    Assignments for module assessments

    Easy-to-use LMS accessible anywhere

    Online library to further enhance your knowledge

    Dissertation on your area of research work

    Learn Fast

    Online courses with compact learning chapters enable you to learn business skills faster than ever.

    Study Online

    Get access to online study materials. All courses are 100% online and self-paced.

    Global Community

    No conventional requirements needed, our courses are open to all ages, professions and citizenship.

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