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In this course you will learn about the different roles, concepts of data analysis and the tools that are used to perform daily functions. You will gain an understanding of the fundamentals of data analysis such as data gathering.

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Course Access

Unlimited Duration

Last Updated

July 29, 2021

Students Enrolled

20

Total Reviews

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Certification

Data analytics are allowing large and small organizations to make key business decisions that often transform their organization or program practices. It is the science of analyzing raw data in order to make conclusions about information. Increasingly data analytics is used with the aid of specialized systems and software. It offers to look into financial savings opportunities, risks, health ,safety improvements, tracking and more, data analytics provide a comprehensive view of operations to develop future focused business strategies. Many of the techniques and processes of data analytics have been automated into mechanical processes. Data analytics helps for businesses to increase revenues, improve operational efficiency, optimize marketing campaigns and customer service efforts. Throughout this course you will learn the key aspects of data analysis. You will begin to explore the fundamentals of gathering data  and learning how to recognize your data sources.  

Course Curriculum

    • Descriptive Statistics 00:00:00
    • Population Vs Sample 00:00:00
    • Types of Variables 00:00:00
    • Types of Descriptive Statistics 00:00:00
    • Labs 00:00:00
    • What is Probability 00:00:00
    • Mutually Exclusive Events 00:00:00
    • Mutually Non-Exclusive Events 00:00:00
    • Conditional Probability 00:00:00
    • Random Variables 00:00:00
    • Discrete Random Variables 00:00:00
    • Types of Discrete Probability Distributions 00:00:00
    • Labs 00:00:00
    • Introduction 00:00:00
    • Normal Distribution 00:00:00
    • Z-score 00:00:00
    • Central Limit Theorem 00:00:00
    • Two Main Areas of Inferential Statistics 00:00:00
    • Point Estimate 00:00:00
    • Confidence Interval Estimate for Mean 00:00:00
    • Confidence Interval Estimate for Variance 00:00:00
    • Confidence Interval Estimate for Proportion 00:00:00
    • Test of Hypothesis 00:00:00
    • One Sample T test 00:00:00
    • Two Sample T test for Means 00:00:00
    • Two Sample test for Proportions 00:00:00
    • F-Distribution 00:00:00
    • Labs 00:00:00
    • Why ANOVA 00:00:00
    • One Way ANOVA Intuition 00:00:00
    • Case Study 00:00:00
    • Tukey Pairwise Comparison 00:00:00
    • Two way ANOVA(without replication) Intuition 00:00:00
    • Case study 00:00:00
    • Two way ANOVA with replication 00:00:00
    • Case study 00:00:00
    • Test of Independence (Chi-Square Test) 00:00:00
    • Labs 00:00:00
    • Introduction 00:00:00
    • Correlation 00:00:00
    • Simple Linear Regression Intuition 00:00:00
    • Ordinary Least Square Estimation 00:00:00
    • Derivation of OLS by Minimizing Errors 00:00:00
    • Minimizing The Error Term 00:00:00
    • Assumptions of Simple Linear Regression 00:00:00
    • Multiple Linear Regression 00:00:00
    • Estimation of Model Parameters 00:00:00
    • Labs 00:00:00
    • Assumptions of Multiple Linear Regression 00:00:00
    • How to validate the assumptions 00:00:00
    • What if the assumptions are Violated 00:00:00
    • Categorical Predictors 00:00:00
    • Influential Points 00:00:00
    • Model Evaluation Metrics 00:00:00
    • Feature Selection methods 00:00:00
    • Labs 00:00:00
    • Polynomial Regression 00:00:00
    • Stepwise Regression 00:00:00
    • Ridge Regression 00:00:00
    • Lasso Regression 00:00:00
    • ElasticNet Regression 00:00:00
    • Labs 00:00:00
    • Overview of the dataset 00:00:00
    • Importing the data 00:00:00
    • Exploratory Data Analysis 00:00:00
    • Handling Missing Values 00:00:00
    • Handling Outliers 00:00:00
    • Categorical Encoding 00:00:00
    • Correlation 00:00:00
    • Forward Elimination 00:00:00
    • Backward Elimination 00:00:00
    • Model Evaluation 00:00:00
    • Model Testing 00:00:00
    • Finalizing the Model 00:00:00

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