Data Analysis Services :

In almost any type of industry, data is an immensely valuable tool that can provide you with an upper hand in developing, presenting and marketing your new products or services to your target audience. However, the process of data analysis cannot be carried out by anyone and requires the expertise of trained research analysts who can gather, analyse and extrapolate valuable information from your data.

This is where SIDSOFT comes into the picture, as not every company can afford to carry out data analysis in-house. Today, SIDSOFT offers comprehensive data analysis services for small, medium and large corporations across a wide range of industries. Our thorough research data analysis services will enable you to better understand the key points within your industry and make informed decisions based on the hard facts gathered by our research professionals.

Data Analysis Services Offered

At SIDSOFT , we offer a wide range of data analysis services especially designed to gve you the full spectrum of information for any given component of your business.

Sample Size Defination

The sample size of a statistical sample is the number of observations that constitute it. The sample size is typically denoted by n and it is always a positive integer. No exact sample size can be mentioned here and it can vary in different research settings. However, all else being equal, large sized sample leads to increased precision in estimates of various properties of the population.

Different Types of Sample :

There are 5 different types of sample

  • Simple Random Sample : Obtaining a genuine random sample is difficult. We usually use Random Number Tables, and use the following procedure;
    1. Number the population from 0 to n
    2. Pick a random place I the number table
    3. Work in a random direction
    4. Organise numbers into the required number of digits (e.g. if the size of the population is 80, use 2 digits)
    5. Reject any numbers not applicable (in our example, numbers between 80 and 99)
    6. Continue until the required number of samples has been collected
    [ If the sample is "without replacement", discard any repetitions of any number]
    1. The sample will be free from Bias (i.e. it's random!)
    Disadvantages: 1.Difficult to obtain 2. Due to its very randomness, "freak" results can sometimes be obtained that are not representative of the population. In addition, these freak results may be difficult to spot. Increasing the sample size is the best way to eradicate this problem.
  • Systematic Sample :
    With this method, items are chosen from the population according to a fixed rule, e.g. every 10th house along a street. This method should yield a more representative sample than the random sample (especially if the sample size is small). It seeks to eliminate sources of bias, e.g. an inspector checking sweets on a conveyor belt might unconsciously favour red sweets. However, a systematic method can also introduce bias, e.g. the period chosen might coincide with the period of faulty machine, thus yielding an unrepresentative number of faulty sweets.
    1. Can eliminate other sources of bias.
    1. Can introduce bias where the pattern used for the samples coincides with a pattern in the population.
  • Stratified Sampling :
    The population is broken down into categories, and a random sample is taken of each category. The proportions of the sample sizes are the same as the proportion of each category to the whole.
    1. Yields more accurate results than simple random sampling.
    2. Can show different tendencies within each category (e.g. men and women).
    1. Nothing major, hence it's used a lot
  • Quota Sampling :
    As with stratified samples, the population is broken down into different categories. However, the size of the sample of each category does not reflect the population as a whole. This can be used where an unrepresentative sample is desirable (e.g. you might want to interview more children than adults for a survey on computer games), or where it would be too difficult to undertake a stratified sample.
    1. Simpler to undertake than a stratified sample.
    2. Sometimes a deliberately biased sample is desirable.
    1. Not a genuine random sample.
    2. Likely to yield a biased result.
  • Cluster Sampling :
    Used when populations can be broken down into many different categories, or clusters (e.g. church parishes). Rather than taking a sample from each cluster, a random selection of clusters is chosen to represent the whole. Within each cluster, a random sample is taken.
    1. Less expensive and time consuming than a fully random sample.
    2. Can show "regional" variations.
    1. Not a genuine random sample.
    2. Likely to yield a biased result (especially if only a few clusters are sampled).

Data collection

Data is collected from a variety of sources. The requirements may be communicated by analysts to custodians of the data, such as information technology personnel within an organization. The data may also be collected from sensors in the environment, such as traffic cameras, satellites, recording devices, etc. It may also be obtained through interviews, downloads from online sources, or reading documentation.

Data validation


data validation is the process of ensuring that a program operates on clean, correct and useful data. It uses routines, often called "validation rules" "validation constraints" or "check routines", that check for correctness, meaningfulness, and security of data that are input to the system. The rules may be implemented through the automated facilities of a data dictionary, or by the inclusion of explicit application program validation logic.

Data validation is intended to provide certain well-defined guarantees for fitness, accuracy, and consistency for any of various kinds of user input into an application or automated system. Data validation rules can be defined and designed using any of various methodologies, and be deployed in any of various contexts.

Data validation guarantees to your application that every data value is correct and accurate. You can design data validation into your application with several differing approaches: user interface code, application code, or database constraints.

There are several types of data validation.

  • Data type validation.
  • Range checking.
  • Code checking.
  • Complex validation.

Data Cleaning

Once processed and organized, the data may be incomplete, contain duplicates, or contain errors. The need for data cleaning will arise from problems in the way that data is entered and stored. Data cleaning is the process of preventing and correcting these errors. Common tasks include record matching, deduplication, and column segmentation. Such data problems can also be identified through a variety of analytical techniques. For example, with financial information, the totals for particular variables may be compared against separately published numbers believed to be reliable. Unusual amounts above or below pre-determined thresholds may also be reviewed. There are several types of data cleaning that depend on the type of data. Quantitative data methods for outlier detection can be used to get rid of likely incorrectly entered data. Textual data spellcheckers can be used to lessen the amount of mistyped words, but it is harder to tell if the words themselves are correct.

Data Formulation

A statistical analysis of time series regression models for longitudinal data with and without lagged dependent variables under a variety of assumptions about the initial conditions of the processes being analyzed. The analysis demonstrates how the asymptotic properties of estimators of longitudinal models are critically dependent on the manner in which samples become large: by expanding the number of observations per person, holding the number of people fixed, or by expanding the number of persons, holding the number of observations per person fixed. The paper demonstrates which parameters can and cannot be identified from data produced by different sampling plans.

Data Audit

A Data Audit is a services engagement backed by our applications that — in a very short timeframe — delivers report findings identifying specific data challenges that may be hindering your operational efficiency and ability to achieve successful business outcomes based on the health of your data.

Data Audit is the first step to delivering Business-Ready Data for organizations focused on data quality, data migrations or data governance. Data Audits span nearly any industry and line of business and include 17 years of prebuilt knowledge, content and reporting to quickly discern discrepancies, anomalies and errors in business data. The Data Audit includes more than 150 data quality checks from all relevant sub-modules. The resulting analysis reporting includes information such as data relevancy.

Setting data to distribution

The Distribution Fitting Process Once you have selected the candidate distributions which can supposedly provide a good fit (see the article above), you are ready to actually fit these distributions to your data. The process of fitting distributions involves the use of certain statistical methods allowing to estimate the distribution parameters based on the sample data. This is where distribution fitting software can be very useful: it implements the parameter estimation methods for most commonly used distributions, so you can save your time and focus on the data analysis itself. If you are fitting several different distributions, which is usually the case, you need to estimate the parameters of each distribution separately.

The input of distribution fitting software usually includes:

  • Your data in one of the accepted formats
  • Distributions you want to fit
  • Distribution fitting options

The distribution fitting results include the following elements:

  • Graphs of your input data
  • Parameters of the fitted distributions
  • Graphs of the fitted distributions
  • Additional graphs and tables helping you select the best fitting distribution

Specific Applications

Even though probability distributions can be applied in any industry dealing with random data, there are additional applications arising in specific industries (actuarial science, finance, reliability engineering, hydrology etc.), enabling business analysts, engineers and scientists to make informed decisions under uncertainty.

Data Analysis

Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains.
Main data analysis
In the main analysis phase analyses aimed at answering the research question are performed as well as any other relevant analysis needed to write the first draft of the research report.[33]

  1. Exploratory and confirmatory approaches
    In the main analysis phase either an exploratory or confirmatory approach can be adopted. Usually the approach is decided before data is collected. In an exploratory analysis no clear hypothesis is stated before analysing the data, and the data is searched for models that describe the data well. In a confirmatory analysis clear hypotheses about the data are tested. Exploratory data analysis should be interpreted carefully. The confirmatory analysis therefore will not be more informative than the original exploratory analysis.
  2. Stability of results
    It is important to obtain some indication about how generalizable the results. While this is hard to check, one can look at the stability of the results. There are two main ways of doing this:
    (1) Cross-validation: By splitting the data in multiple parts we can check if an analysis (like a fitted model) based on one part of the data generalizes to another part of the data as well.
    (2) Sensitivity analysis: A procedure to study the behavior of a system or model when global parameters are (systematically) varied. One way to do this is with bootstrapping.
  3. Statistical methods
    Many statistical methods have been used for statistical analyses. A very brief list of four of the more popular methods is:
    (1) General linear model: A widely used model on which various methods are based (e.g. t test, ANOVA, ANCOVA, MANOVA). Usable for assessing the effect of several predictors on one or more continuous dependent variables.
    (2) Generalized linear model: An extension of the general linear model for discrete dependent variables.
    (3) Structural equation modelling: Usable for assessing latent structures from measured manifest variables.
    (4) Item response theory: Models for (mostly) assessing one latent variable from several binary measured variables (e.g. an exam).

Data Inference

When making sampling distribution inferences about the parameter of the data, θ, it is appropriate to ignore the process that causes missing data if the missing data are ‘missing at random’ and the observed data are ‘observed at random’, but these inferences are generally conditional on the observed pattern of missing data. When making direct-likelihood or Bayesian inferences about θ, it is appropriate to ignore the process that causes missing data if the missing data are missing at random and the parameter of the missing data process is ‘distinct’ from θ. These conditions are the weakest general conditions under which ignoring the process that causes missing data always leads to correct inferences.

Data Simulation

Simulation is a tool for managing change. Simulation provides more than an answer: it shows you how the answer was derived; it enables you to trace from cause to effect; and it allows you to generate explanations for decisions. Simulation is a component of a business rules engine. You can view simulation as a solution to both off-line design and on-line operational management problems.

1)Forecasting data and methods

The appropriate forecasting methods depend largely on what data are available. If there are no data available, or if the data available are not relevant to the forecasts, then qualitative forecasting methods must be used. These methods are not purely guesswork- there are well-developed structured approaches to obtaining good forecasts without using historical data.

  • Quantitative forecasting: can be applied when two conditions are satisfied:
    • Numerical information about the past is available.
    • It is reasonable to assume that some aspects of the past patterns will continue into the future.
  • Cross-sectional forecasting : With cross-sectional data, we are wanting to predict the value of something we have not observed, using the information on the cases that we have observed. Cross-sectional models are used when the variable to be forecast exhibits a relationship with one or more other predictor variables. The purpose of the cross-sectional model is to describe the form of the relationship and use it to forecast values of the forecast variable that have not been observed. Under this model, any change in predictors will affect the output of the system in a predictable way, assuming that the relationship does not change. Models in this class include regression models, additive models, and some kinds of neural networks
  • Time series forecasting : Time series data are useful when you are forecasting something that is changing over time (e.g., stock prices, sales figures, profits, etc.). Examples of time series data include:
    1. Daily IBM stock prices.
    2. Monthly rainfall.
    3. Quarterly sales results for Amazon.
    4. Annual Google profits.
    Anything that is observed sequentially over time is a time series.

2)Finalization Of Results

Finalization varies significantly between languages and between implementations of a language, depending on memory management method, and can generally be partially controlled per-object or per-class by a user-specified finalizer or destructor, Finalization is primarily used for cleanup, to release memory or other resources: to deallocate memory allocated via manual memory management; to clear references if reference counting is used (decrement reference counts); to release resources, particularly in the Resource Acquisition Is Initialization (RAII) idiom; or to unregister an object.

Benefits of Data Analysis

The data analysis services that we offer can help any type of business across a range of industries leverage a large number of tangible benefits. The following are the top three benefits that your company can leverage by partnering with us:

  1. We will inspect, clean and transform your data to create models that will highlight the important information within your business and provide you with insights that can give you a competitive edge over other companies in your industry.
  2. With our advanced data analysis services, you can make key decisions while receiving important conclusions that might otherwise have been hidden within massive or disorganized data sets.
  3. By using cutting edge statistical tools and working with only the best trained statisticians and data management experts in the research field, we can ensure that your analysis needs are met for every major component of your business, whether you need your financial data to be analysed and presented or are interested in key decision points based on competitor data.

To ensure that you receive only the best data analysis services, partner with a company who is trusted by executives and research firms around the globe - SIDSOFT. Contact us today to start data analysis services and leverage the benefits.