What is Data Analysis

https://www.predictiveanalyticstoday.com/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 Aalysis consists of several phases. 
These are Initial phase data cleaning and quality analysis, quality of measurement, analysis and main data analysis.

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.

2.Quality Analysis:
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.

4.Analysis:
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.
Associations
Circumambulations
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.
Distribution.
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
1.Ethnographic Analysis
2.Narrative Analysis
3.Phenomenological Analysis
4.Constant Comparative Analysis
5.Hermeneutic Analysis
6.Discourse Analysis
7.Grounded Theory Analysis
8.Content 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

Orange Data mining, R Software Environment, Weka Data Mining, Tableau Public, Arcadia Data, Microsoft R, ITALASSI, Shogun, Trifacta, ELKI, Scikit-learn, Data Applied, Lavastorm Analytics Engine, Gephi, DataMelt, TANAGRA, Julia, RapidMiner Starter Edition, SciPy, KNIME Analytics Platform Community, Dataiku DSS Community, Google Fusion Tables, Massive Online Analysis, NodeXL, DataPreparator, NetworkX, NumPy, OpenRefine, DataWrangler, PAW, 
ILNumerics, ROOT, DataCracker, Scilab, FreeMat, Ipython, jMatLab, SymPy, Fluentd, EasyReg, Matplotlib
 
a href="https://www.predictiveanalyticstoday.com/top-data-analysis-software/" class="btn  btn-primary " target="_blank">Top Free Data Analysis Software 

ELKI
Nvivo,ATLAS.ti, MAXQDA, Quirkos, webQDA, Dedoose, HyperRESEARCH, Raven’s Eye, Qiqqa, Focuss On, F4analyse, Annotations, Datagrav, QDA Miner, and SaturateApp are some of the top Qualitative Data Analysis Software.
Top Qualitative Data Analysis Software

ATLAS.ti

General Architecture for Text Engineering – GATE, Coding Analysis Toolkit, FreeQDA, TAMS, Transana, ConnectedText, RQDA, Qiqqa, LibreQDA, QCAmap, Visão, Aquad, Weft QDA, Cassandre, ELAN, CATMA, Compendium, fs/QCA, Tosmana, QDA Miner Lite Kirq are some of the Top Free Qualitative Data Analysis Software.

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Compendium
 

More Information on Predictive Analysis Process

a href="https://www.predictiveanalyticstoday.com/wp-content/uploads/2013/12/Predictive-Analytics-Process-Flow.jpg">

Predictive Analytics Process Flow
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.