Data analysis is a process of inspecting, cleansing, transforming, and modelling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.

Following data collection, the data needs to be critically analysed. For any research, data analysis is very important as it provides an explanation of various concepts, theories, frameworks and methods used.

In statistics, there are four data measurement scales: nominal, ordinal, interval and ratio. These are simply ways to sub-categorize different types of data (here's an overview of statistical data types) .

Mission skill india is a leading institution in Hyderabad for data analysis program.

Big Data Analytics Course Content

  • Introduction to Data Science
  • Analytical Terminology, Analytical Methodology
  • Introduction to SAS, R, R-studio interface
  • Data Collection, Creating Datasets
  • Reading Data From External Files (.Txt, .Xls, .Csv) — Tasks
  • Data Exploration: Proc Print, Proc Contents —Tasks
  • Data Exploration: ProcGchart, ProcGplot —- Tasks
  • Data Exploration: Statistical Terminology
  • Data Exploration: Understanding Probability
  • Data Exploration : Analyzing Categorical Data (ProcFreq) — Tasks
  • Data Exploration : Hypothesis, Types of Errors
  • Data Preparation: Arranging the data: Proc Sort, Proc Format-Tasks
  • Data Preparation: Keeping, Dropping, Renaming, Transposing
  • Data Preparation: Using SAS Functions — Tasks
  • Data Preparation: Conditional Processing, By group Processing -Tasks
  • Data Preparation: Combining Data sets — Tasks
  • Data Preparation: Do-Loops, Arrays
  • Statistics: ProcFreq
  • Statistics: ProcTtest, ProcAnova
  • Proc Npar1way
  • Data Mining: Introduction
  • Introduction to Regression: ProcCorr, ProcReg
  • Dimensionality Reduction Techniques: Proc Factor
  • Dimensionality Reduction Techniques: ProcPrincomp
  • Dimensionality Reduction Techniques: ProcDiscrim
  • Clustering: Introduction
  • Clustering case study — Task
  • Association Rules — Introduction
  • Association Rules — Case study: Task
  • Density Estimation: Proc KDE
  • ProcReg: Case study — Task
  • ProcReg: Model Diagnostics — Task
  • Introduction to Logistic Regression
  • Proc Logistic -Case study, — Task
  • Introduction to Decision Trees
  • ProcDtree, Case study — Task
  • Introduction to SVM, Naive Bayes, Case study
  • Introduction to Neural nets,
  • Neural Nets – the Case study
  • Introduction to KNN, Case study — Task
  • Introduction to Bagging and Boosting
  • Ensemble methods Case study
  • Reinforcement Learning
  • Introduction to Time series
  • Proc Arima — Case study — Task
  • Introduction to Text Analytics
  • Sentiment Analysis in R — Case study
  • Introduction to Optimization
  • Optimization — Case study