Module 1: Artificial Intelligence & Big Data Analytics – An Introduction Bridge Module

Terminal Outcomes:
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• Explain fundamental use cases of AI/Bigdata, types of AI systems and types of roles under this occupation

Theory – Key Learning Outcomes
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• Explain the relevance of AI & Big Data Analytics for the society
• Explain the various use-cases of AI & Big Data in the industry
• Define “general” and “narrow” AI
• Describe the fields of AI such as image processing, computer vision, robotics, NLP, etc.

Practical – Key Learning Outcomes 
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• Outline a career map for roles in AI & Big Data Analytics
• Analyse the differences between key terms such as Supervised Learning, Unsupervised Learning and Deep
Learning

Module 2: Basic Statistical Concepts
Terminal Outcomes:
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• Distinguish between various basic statistical concepts
• Apply different statistical techniques

Theory – Key Learning Outcomes 
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• Distinguish between different probability distributions such as Normal, Poisson, Exponential, Bernoulli, etc.
• Identify correlation between variables using scatterplots and other graphical techniques 

Practical – Key Learning Outcomes
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• Apply basics of descriptive statistics including measures of central tendency such as mean, median and mode
• Apply different correlation techniques such as Pearson’s Correlation Coefficient, Methods of Least Squares
etc.
• Apply different techniques for regression analysis including linear, logistic, ridge, lasso, etc.
• Use hypothesis testing to draw inferences and measure statistical significance

Module 3: Statistical Tools and Usage
Terminal Outcomes:
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• Assess the various types of statistical tools available
• Assess the various types of statistical packages available

Theory – Key Learning Outcomes 
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• Explain the basics of using statistical software packages and IDEs such as RStudio, Jupyter Notebooks

Practical – Key Learning Outcomes
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• Apply basic functions and libraries present in statistical software packages and IDEs
• Use statistical packages, frameworks and libraries such as NumPy and Pandas for developing applications

Module 4: Importing Data
Theory – Key Learning Outcomes
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• Identify the type of data, volume of data, and variables required for the analysis
• Distinguish between different types of data such as numerical, categorical, etc.
• Identify common open and paid data sources
• Discuss the uses and characteristics of different open source and paid data sources
• Describe the purpose of metadata
• Describe various Data validation tools and processes

Practical – Key Learning Outcomes
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• Demonstrate the process of capturing various types of data such as enterprise data, consumer data etc. from various data sources
• Conduct the process of importing data from both public and private databases or data stores and store it in datasets or data frames
• Organize and map metadata as per the needs of the analysis
• Perform data profiling for data quality assessment and validation

Module 5: Pre-processing Data
Terminal Outcomes:
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• Explain the fundamentals of pre-processing data
• Demonstrate the analysis of unprocessed data
• Evaluate different techniques to process data

Theory – Key Learning Outcomes 
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• Differentiate the unprocessed and processed data
• Explain the impact of unprocessed data on subsequent analytical operations
• Describe the various anomalies that may be found in unprocessed data (e.g. missing values, incorrect data types, and redundant data)
• Explain the Data Normalization techniques and concepts
• Describe the properties of different tools that can be used to validate the pre-processed data

Practical – Key Learning Outcomes
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• Analyze unprocessed data to discover anomalies such as missing values, incorrect data types, etc.
• Apply different techniques and functions to clean unprocessed data including removing missing values,
transforming incorrect data types, etc.
• Apply different approaches to normalize datasets such as feature scaling etc.
• Apply appropriate tools and techniques to perform pre-processed data validation