What is Data Analysis ?
What is Data Analysis ? : The process of inspecting, cleaning, transforming, and modeling data with the objective of discovering useful information, arriving at conclusions, and supporting the decision making process is called Data Analysis. There are multiple facets and approaches with diverse techniques for the data analysis. The data analysis in statistics are generally divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). Data need to be cleaned. Data cleaning is the process of correcting the outliers and other incorrect and unwanted information. There are several types of data cleaning process to employ depends on the type of data to be cleaned. For quantitative data methods the outlier detection can be used to get rid of anomaly in the data. Spellcheckers can used to lessen the amount of mistyped words in case of textual data.
Business intelligence covers the data analysis that run heavily on aggregation, disaggregation, slicing and dicing, focusing on the business information. Predictive analytics is the application of statistical or structural models for predictive forecasting. Text analytics is the application of statistical, linguistic, and structural models to extract and classify information from texts. All these are varieties of data analysis.
What is Data Analysis ? Data Analysis Methods
Data Analysis consists of several phases. These are Initial phase data cleaning and quality analysis, quality of measurement, analysis and main data analysis.
Initial phase data analysis:
1.Data Cleaning :
This is the first process of data analysis where record matching, deduplication, and column segmentation are done to clean the raw data from different sources.
Using frequency counts, descriptive statistics such as mean, standard deviation, median, normality histograms such as skewness, kurtosis, frequency, where the n variables are compared with variables of external to the data set.
3.Quality of Measurement:
Using confirmatory factor analysis and Analysis of homogeneity.
There are many analyses which can be done during the initial data analysis phase.
- Univariate statistics ,single variable.
- Bivariate associations correlations.
- Graphical techniques scatter plots.
- Nominal and ordinal variables.
- Frequency counts in numbers and percentages.
- Hierarchical loglinear analysis
- Loglinear analysis for the identification of important variables and possible confounders.
- Exact tests or bootstrapping in case of subgroups are small
- Computation of new variables.
- Continuous variables.
- Statistics – M, SD, variance, skewness and kurtosis.
- Stem and leaf displays.
- Box plots.
Main Data Analysis
1.Using exploratory and confirmatory approaches:
In an exploratory analysis no clear hypothesis is stated before analysing the data, and in a confirmatory analysis clear hypotheses about the data are tested.
2.Stability of results
Stability of results using cross validation,sensitivity analysis and statistical methods.
3. Analysis using different statistical methods
1.General linear model: The different statistical models which the general linear model are based are t test, ANOVA, ANCOVA, MANOVA, MANCOVA, ordinary linear regression and F-test. This is multiple linear regression model’s generalization to the case of more than one dependent variable.
2.Generalized linear model: This is an extension and generalisation of the general linear model for discrete dependent variables.
3. Structural equation modelling: These are sable for assessing latent structures from measured manifest variables.
4. Item response theory: Models for used for assessing one latent variable from several binary measured variables.
Data Analysis Approaches
4.Constant Comparative Analysis
7.Grounded Theory Analysis
9.Cross cultural Analysis
Process of Analysis
• Look at the data from the perspective of what you want to know
• Estimate the averages of the data
• Evaluate the exceptions from the average
Data Analysis Software
ELKI, ITALASSI, R, Data Applied, DevInfo, Tanagra, Waffles, Weka, Gephi, OpenRefine, Fusion Tables, DataMelt, Orange, Wrangler Encog, RapidMiner, PAW, SCaVi, ILNumerics.Net, ROOT, Julia, MOA, NumPy, SciPy, KNIME, NetworkX, matplotlib, IPython, SymPy, Scilab, FreeMat, jMatLab, NodeXL Basic, Fluentd, and Tableau Public are some of the free or open source top software for data analysis.
QDA Miner, ATLAS.ti, HyperRESEARCH, MAXQDA, NVivo, Qiqqa, XSight, Quirkos, Dedoose, webQDA, f4analyse, Annotations , Saturate, Raven’s Eye, Focuss On are the Qualitative Data Analysis Software in no particular order.
QDA Miner Lite, Coding Analysis Toolkit (CAT), Computer Aided Textual Markup & Analysis (CATMA), Aquad , Compendium, Cassandre, LibreQDA , RQDA, TAMS Analyzer , ELAN , FreeQDA, Weft QDA, Qiqqa, QCAmap, ConnectedText, Transana, and Visão the free Qualitative Data Analysis Software in no particular order.
Top Free Qualitative Data Analysis Software
More Information on Predictive Analysis Process
For more information of predictive analytics process, please review the overview of each components in the predictive analytics process: data collection (data mining), data analysis, statistical analysis, predictive modeling and predictive model deployment.