CertificationA certificate Exploratory Data Analysis using R will make you eligible for a career as a Data Analyst.
Job OpportunitiesPost the exploratory data analytics certification, you’ll be able to find better job opportunities.
Who Should AttendManagers, analysts, business intelligence professionals, product managers.
Salary PackagesThe average pay for an entry-level Data Analyst is ₹4.8 lakhs per year.
Expert FacultyLearn from experts with a vast experience in the use of R programming for data analysis.
Want to build a career in the field of data analytics and visualization? Exploratory data analysis is a very helpful approach for summarizing and visualizing the important characteristics of a data set, helping organizations make better business decisions. The most popular tool for exploring and analysing data is R – an open source programming language with powerful data visualization capabilities.
Manipal ProLearn’s course in Exploratory Data Analysis using R will provide you with an introduction to the R programming language and demonstrate how R can be used for exploratory data analysis and use that data in strategic predictive modelling initiatives.
After completing the exploratory data analytics certification, you’ll be considered as a strong and competent data analyst. The course will help you to:
- Explain the importance of EDA as the first step before modelling.
- Describe datasets in terms of variables and their types.
- Identify the central tendency of data using visualizations.
- Describe the need for dimensionality reduction and approaches to implement the algorithms of association and cluster analysis.
- Identify and handle the missing values and outliers in datasets.
- Learn the need for different data transformations.
Engineers and non-engineers looking to perform statistical data analysis
Managers and analysts interested in understanding statistical data analysis
Business Intelligence professionals entering data analytics projects
Data Science aspirants
Basics of EDA
Importance of EDA in Data Science domain
Key EDA techniques
Explain why EDA is needed in the field of Data Science
Describe the important techniques in EDA
Variables & data types
Sampling techniques & samples
Frequency distribution and central tendency
Variability & shape
Relationship among variables
Describe central tendency of data and key statistics
Describe variability and shape of data in dataset
Identify association among variables
Develop insight into missing values and outliers
Data for EDA
Data types and formats
Data analysis types and purpose
Handling missing values in data
Data transformation for EDA
Describe different types of data in enterprises
Describe the importance of data quality & cleansing
Explain the purpose of data analysis & types
Describe method to treat missing values
Handling categorical & numerical variables
Plotting EDA graphs
Factor analysis & PCA
Visualize central tendency, shape, distribution of dataset
Identify outliers and missing values
Explain the need for data transformation and types
Implement the algorithms of association & clustering
Describe factor analysis and Principal Component
Analysis (PCA) for dimensionality reduction
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