- Live web
sessions by experts
exercises in R/Python
tests and quizzes
Data Analysis and Visualization
Hands-on ToolsGain functional knowledge in and be able to use R and Python practically for problem-solving through our hands-on exercises.
Live Web-Based SessionsTalk to our expert faculty through web sessions held during the course and get your doubts clarified.
Comprehensive Course MaterialGet access to 15 hours of concept videos featuring expert tutors, self-assessment quizzes at the end of each module, assignments and reading material.
Vast Career OpportunitiesBecome 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.
Mastering ConceptsMaster concepts like Statistical Techniques, Data Visualization, Data Handling, EDA and Business Statistics.
Businesses are gaining competitive advantage through effective decision making. And, this is facilitated by Data Analytics and Data Science, the two emerging approaches that are enabling business improvement today. Through Data Analysis and Data Visualization, one can extract useful information from raw data and represent it in an intelligible way, which can help in demonstrating storytelling easily.
Manipal ProLearn’s course on Data Analysis and Visualization takes you through the statistical concepts of sampling, frequency distribution and variance. It also teaches the basics of data preparation for exploratory handling and analysis, touching concepts like outliers, missing data, data transformation and dimensionality reduction. Also, you will learn about various data visualization methods for effective storytelling.
The course equips you with sound knowledge of Data Analysis and Visualization and their importance by covering all the essential concepts in the domains. By learning about Exploratory Data Analysis, you will gain an in-depth understanding of which ML algorithm to implement for particular data sets. You will learn how to
- Statistical techniques and concepts required to understand Data Analysis
- The ability to explain the importance of Data Analysis and Visualisation as a first step before modelling
- Implement EDA techniques using either R or Python.
- Perform effective storytelling using structured and unstructured data.
- Describe datasets in terms of variables and their types.
- Identify the central tendency of data using visualizations and handle missing values and outliers in datasets.
- Perform Data Visualization using different techniques.
- Perform dimensionality reduction and approaches to implement the algorithms of association and cluster analysis.
Candidates aspiring to become data scientists
Business Intelligence professionals entering Data Analytics projects
Engineering and non-engineering graduates willing to start a career in analytics
Data and information
Classification of data
Measures of central tendency
Measures of dispersion
Visual representation of data
Events and their types
Types of Probability
Dependence and Independence
Various kinds of sampling
Central Limit theorem
Correlation versus Causation
Diagnostics of Linear Regression
Binary Logistic Regression
Introduction to Exploratory Data Analysis(EDA)
Importance of EDA in Data Science domain
Introduction Key EDA techniques
Basic concepts in business statistics
Sampling techniques & samples
Frequency distribution and central tendency Variability & shape
Data for EDA in enterprises
Data types and formats
Data analysis types and purpose
Handling missing values in data
Data transformation for EDA
Handling categorical & numerical variables
Visualization in EDA
Factor analysis & Principal Component Analysis
Visual Communication Design
Components of visual communication
Datasets and Graphs
Layout and Formatting
Classification of Visualization
Mediums of visualizing structured data –
Metrics and KPIs
Visualizing big data
Data story and infographic design process
Tufte's design principle
Story telling with data
Story development and delivery