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Aritificial Intelligence / Machine Learning with Data Science

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Course Duration

10 week

About AnnexIT

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Python Statistics for Data Science Course

Python Scripting allows programmers to build applications easily and rapidly. This course is an introduction to Python scripting, which focuses on the concepts of Python, it will help you to perform operations on variable types using Pycharm. You will learn the importance of Python in a real-time environment and will be able to develop applications based on the Object-Oriented Programming concept. End of this course, you will be able to develop networking applications with suitable GUI.

    AI/ML Data Science Course Curriculum

    Goal: In this module, you will be introduced to data and its types and accordingly sample data and derive meaningful information from the data in terms of different statistical parameters.

     

    Objectives: At the end of this module, you should be able to:

    • Understand various data types
    • Learn Various variable types
    • List the uses of variable types
    • Explain Population and Sample
    • Discuss sampling techniques
    • Understand Data representation

     

    Topics:

    • Introduction to Data TypesNumerical parameters to represent data
    • Mean
    • Mode
    • Median
    • Sensitivity
    • Information Gain
    • Entropy
    • Statistical parameters to represent data

     

    Hands-On/Demo

    • Estimating mean, median and mode using python
    • Calculating Information Gain and Entropy

    Goal: In this module, you should learn about probability, interpret & solve real-life problems using probability. You will get to know the power of probability with Bayesian Inference.

     

    Objectives: At the end of this Module, you should be able to:

    • Understand rules of probability
    • Learn about dependent and independent events
    • Implement conditional, marginal and joint probability using Bayes Theorem
    • Discuss probability distribution
    • Explain Central Limit Theorem

     

    Topics:

    • Uses of probability
    • Need of probability
    • Bayesian Inference
    • Density Concepts
    • Normal Distribution Curve

     

    Hands-On/Demo:

    • Calculating probability using python
    • Conditional, Joint and Marginal Probability using Python
    • Plotting a Normal distribution curve

    Goal: Draw inferences from present data and construct predictive models using different inferential parameters (as a constraint).

    Objectives: At the end of this Module, you should be able to:

    • Understand the concept of point estimation using confidence margin
    • Draw meaningful inferences using margin of error
    • Explore hypothesis testing and its different levels

     

    Topics:

    • Point Estimation
    • Confidence Margin
    • Hypothesis Testing
    • Levels of Hypothesis Testing

     

    Hands-On/Demo:

    • Calculating and generalizing point estimates using python
    • Estimation of Confidence Intervals and Margin of Error

    Goal: In this module, you should learn the different methods of testing the alternative hypothesis.

     

     

    Objectives: At the end of this module, you should be able to:

    • Understand Parametric and Non-parametric Testing
    • Learn various types of parametric testing
    • Discuss experimental designing
    • Explain a/b testing

     

    Topics:

    • Parametric Test
    • Parametric Test Types
    • Non- Parametric Test
    • Experimental Designing
    • A/B testing

     

    Hands-On/Demo:

    • Perform p test and t tests in python
    • A/B testing in python

    Goal: Get an introduction to Clustering as part of this Module which forms the basis for machine learning.

     

    Objectives: At the end of this module, you should be able to:

    • Understand the concept of association and dependence
    • Explain causation and correlation
    • Learn the concept of covariance
    • Discuss Simpson’s paradox
    • Illustrate Clustering Techniques

     

    Topics:

    • Association and Dependence
    • Causation and Correlation
    • Covariance
    • Simpson’s Paradox
    • Clustering Techniques

     

    Hands-On/Demo:

    • Correlation and Covariance in python
    • Hierarchical clustering in python
    • K means clustering in python

    Goal: Learn the roots of Regression Modelling using statistics.

     

    Objectives: At the end of this module, you should be able to:

    • Understand the concept of Linear Regression
    • Explain Logistic Regression
    • Implement WOE
    • Differentiate between heteroscedasticity and homoscedasticity
    • Learn the concept of residual analysis

     

    Topics:

    • Logistic and Regression Techniques
    • Problem of Collinearity
    • WOE and IV
    • Residual Analysis
    • Heteroscedasticity
    • Homoscedasticity

     

    Hands-On/Demo:

    • Perform Linear and Logistic Regression in python
    • Analyze the residuals using python

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Course Features

  • Duration 60h
  • Max Students Unlimited
  • Certificate Yes
  • Skill Advance
  • Category Cloud

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