Advance Data Science

Formulate and use appropriate models of data analysis to solve hidden solutions to business-related challenges.

About the Course
Advance Data Science

Data analytics helps businesses increase revenues, improve operational efficiency, optimize marketing campaigns and customer service efforts, respond more quickly to emerging market trends and gain a competitive edge over rivals.

Duration: 90 Days

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Certification in Advanced Data Sciences

Course Details:

Foundation of Data Science:

  • Obtain, clean/process and transform data.

  • Analyze and interpret data using an ethically responsible approach.

  • Use appropriate models of analysis, assess the quality of input, derive insight from results, and

    investigate potential issues.

  • Apply computing theory, languages and algorithms, as well as mathematical and statistical models, and

    the principles of optimization to appropriately formulate and use data analyses.

  • Formulate and use appropriate models of data analysis to solve hidden solutions to business-related

    challenges.

  • Data analytics helps businesses increase revenues, improve operational efficiency, optimize marketing

    campaigns and customer service efforts, respond more quickly to emerging market trends and gain a

    competitive edge over rivals.

Data Analytics: Concept, Tools & Techniques:

  • understand techniques for quantitative data analysis, and be confident in their ability to tackle data
    analysis problems;

  • use Python to apply the techniques learned on the module;

  • validate and evaluate data analysis results,

  • Demonstrate satisfactory knowledge of network models.

Big Data:

  • Ability to identify the characteristics of datasets and compare the trivial data and
    big data for various applications.

  • Ability to solve problems associated with batch learning and online learning, and thebig data
    characteristics such as high dimensionality, dynamically growing data and
    in particular scalability issues.

  • Perform data gathering of large data from a range of data sources.

  • Critically analyse existing Big Data datasets and implementations, taking practicality, and usefulness
    metrics into consideration.

  • Understand and demonstrate the role of statistics in the analysis of large datasets.

  • Select and apply suitable statistical measures and analyses techniques.

Network Science:

  • Recognize the importance of the network approach.

  • Map out networks from data on complex systems in diverse fields of applications and use simple
    visualization software;

  • Carry out statistical analysis of complex networks regarding the basic characteristics;

  • Measure dynamic properties of processes on networks.

  • This course provides an introduction to complex networks, their structure, and function, with examples
    from engineering, biology, and social sciences.

  • This course will provide students with the mathematical tools and computational training to understand
    large-scale networks in the current era of Big Data.

Machine Learning:

  • Machine learning techniques enable us to automatically extract features from data to solve predictive
    tasks, such as speech recognition, object recognition, machine translation, question- answering, etc

  • Have a good understanding of the fundamental issues and challenges of machine learning: data, model
    selection, model complexity, etc.

  •  Have an understanding of the strengths and weaknesses of many popular machine learning approaches.

  •  Appreciate the underlying mathematical relationships within and across Machine Learning algorithms
    and the paradigms of supervised and un-supervised learning.

  •  Be able to design and implement various machine learning algorithms in a range of real-world
    applications.

Computation Intelligence:

  • Course introduces Computational Intelligence computing concepts and demonstrates how they are used
    to solve problems that are normally difficult or intractable by conventional means.

  •  Demonstrate awareness of the major challenges and risks facing computational intelligence and the
    complexity of typical problems within the field.

  • Able to implement solutions to various problems in computational intelligence.

  • Course covers core concepts in AI including problem solving by search, knowledge representation and
    reasoning, symbolic AI languages, decision making, biologically-inspired algorithms, learning and
    computer vision.

  •  Design and implement an expert system that operates in a realistic problem domain.

  • Design solutions for problems involving uncertain inputs or outcomes.

  • Apply heuristic search to optimisation problems.

  • Apply machine learning algorithms to real data sets to build predictive models.

  • Knowledge of current and emerging AI technologies.

  •  The ability to design and implement computing solutions using artificial intelligence techniques.

Data Modeling:

  • Data modeling is an essential part of the data science pipeline. This, combined with the fact that it is a
    very rewarding process, makes it the one that often receives the most attention among data science
    learners.

  • This topic teaches that data science modeling involves evaluating a model, for example, making sure
    that it is robust and therefore reliable.

  • In-depth knowledge of a broad range of methods and techniques for analysing and solving problems
    within applicable fields.

  • Good theoretical insight and the ability to apply theory to the development of methods and techniques
    for solving problems.

Data Science Techniques; Cleaning, Analysis, Interpretation and Visualization:

  • Live lab and practical on the tool for hands on experience using data sets available on Hadoop and
    Infovieve.