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Data Analysis and Visualization

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Overview
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    Hands-on Tools
    Gain functional knowledge in and be able to use R and Python practically for problem-solving through our hands-on exercises.
  • manipal overview icon
    Live Web-Based Sessions
    Talk to our expert faculty through web sessions held during the course and get your doubts clarified.
  • manipal overview icon
    Comprehensive Course Material
    Get access to 15 hours of concept videos featuring expert tutors, self-assessment quizzes at the end of each module, assignments and reading material.
  • manipal overview icon
    Vast Career Opportunities
    Become eligible for job roles such as Data Engineer, Data Analyst and Data Visualization Analyst or take up a career in market/product analysis or quality management.
  • manipal overview icon
    Mastering Concepts
    Master concepts like Statistical Techniques, Data Visualization, Data Handling, EDA and Business Statistics.
manipal overview icon
Hands-on Tools
Gain functional knowledge in and be able to use R and Python practically for problem-solving through our hands-on exercises.
manipal overview icon
Live Web-Based Sessions
Talk to our expert faculty through web sessions held during the course and get your doubts clarified.
manipal overview icon
Comprehensive Course Material
Get access to 15 hours of concept videos featuring expert tutors, self-assessment quizzes at the end of each module, assignments and reading material.
manipal overview icon
Vast Career Opportunities
Become eligible for job roles such as Data Engineer, Data Analyst and Data Visualization Analyst or take up a career in market/product analysis or quality management.
manipal overview icon
Mastering Concepts
Master concepts like Statistical Techniques, Data Visualization, Data Handling, EDA and Business Statistics.
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Course Curriculum
1.1 Introduction to Statistics
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Data and information
Classification of data
Measures of central tendency
Measures of dispersion
Visual representation of data

1.2 Probability
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Probability
Sample space
Events and their types
Types of Probability
Dependence and Independence
Bayes’ Theorem

1.3 Sampling and Sampling Distributions
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Sampling
Various kinds of sampling
Central Limit theorem
Standard error
Confidence intervals

1.4 Testing of Hypothesis
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Mann-Whitney-Wilcoxon test
Kruskal-Wallis test
Non-parametric tests
Chi-square test

1.5 Simple Correlation
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Correlations
Correlation versus Causation

2.1 Regression
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Diagnostics of Linear Regression
Binary Logistic Regression

2.2 Essentials of Exploratory Data Analysis
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Introduction to Exploratory Data Analysis(EDA)
Importance of EDA in Data Science domain
Introduction Key EDA techniques

2.3 Foundational Concepts of Business Statistics
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Basic concepts in business statistics
Sampling techniques & samples
Frequency distribution and central tendency Variability & shape

2.4 Foundational Concepts of Data Handling
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Data for EDA in enterprises
Data types and formats
Data quality
Data analysis types and purpose
Handling missing values in data
Data transformation for EDA

3.1 Data Analysis Techniques
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Handling categorical & numerical variables
Visualization in EDA
Dimension reduction
Association analysis
Clustering
Factor analysis & Principal Component Analysis

3.2 Visual Communication Elements
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Visual Communication Design
Components of visual communication
Datasets and Graphs
Layout and Formatting
Classification of Visualization

3.3 Visualizing Structured Data and Big Data
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Mediums of visualizing structured data –
Dashboards
Scorecards
Infographics
Metrics and KPIs
Visualizing big data

3.4 Storytelling with Data
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Data story and infographic design process
Tufte's design principle
Story telling with data
Story development and delivery

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