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Data Analysis methods and responsibilities of a Data Analyst 14 May, 2019   

Data analysis implies the collection and organization of data that enables a researcher to conclude. Data analysis enables you to find answers to questions, solutions to problems and obtain vital information.

For instance, you have to work on a research assignment for which it is mandatory to collect information from various sources. Now, for the collection of data you need to imply data analysis methods to get the needed information and then work on it to conclude. 

Statistical and mathematical data analysis methods

So, let us begin with some of the most widely practiced data analysis methods based on mathematics and statistics!

1. Descriptive analysis

This kind of data analysis considers past details. Under this method, you look at data and analyze past situations and events to get an idea to approach it in the future. This method allows you to learn from past behaviour and figure out how it can influence performance in the future.

2. Regression analysis

Data analytics under the regression analysis method allows you to model the relationship between one or more independent variables and one dependent variable. In the process of data mining, regression analysis technique is used for predicting values for a particular dataset. This method is used for financial forecasting, market planning and across various businesses and industries. 

3. Factor analysis

This is one of the data analysis methods that are based on regression technique of analysis. This method is used for finding out the essential structure for a series of variables. It involves finding out new independent variables describing the models and patterns of relationships present among new independent variables.

This kind of data analytics is popular for the research of variable relationships with regards to difficult topics like that of socioeconomic status and psychological scales.

4. Discriminant analysis

Another very powerful technique for data classification is discriminant analysis. For data mining, this method makes use of variable measurements over several categories of items to emphasize those points that stand out as a distinguishing factor among the categories.

Now, these measurements are used for classifying new items. This method is generally used for the classification of customers of recently launched products and services into different categories, for the classification of credit card applications into high risk and low-risk groups, etc.

5. Dispersion analysis

This method is not so commonly used for data mining. However, it does have a role in it. By dispersion, we mean the extent to which a series of data is expanded. This method aids scientists in studying the variability of items. Usually, dispersion consists of two matters; firstly it shows a variation of items among themselves; and on the other hand, it shows the variation existing around the average value. Or else, it remains low.

6. Time series analysis

This method of data analysis involves the procedure that models and explains the time-dependent set of data points. This is done with the objective of drawing all important information including rules, patterns, and statistics from data shape. It is used for predicting future evolutions. The daily stock market value is an excellent example of this kind of analysis.

Data analysis methods based on heuristic algorithms, artificial intelligence, and machine learning

These are modern methods used by data scientists as they have extended capabilities and are even capable of solving contemporary tasks. Plus, these methods can be efficiently implemented. Let us see some commonly used such methods!

1. Artificial neural networks

Neural networks are biologically inspired and beautiful programming model that allows a computer system to learn from a set of observational data. Often referred to as ANN, Artificial Neural Networks, process information by presenting a brain metaphor. The paradigms involved are computational models consisting of an interconnected set of artificial neurons and uses computation approach for processing information. It is really a promising process for business applications owing to its high accuracy.

2. Evolutionary programming

This method combines several kinds of data analysis methods with the help of evolutionary algorithms. Some of these are genetic programming, genetic algorithms, co-evolutionary algorithms, etc. Some big data challenges are tackled with evolutionary programming, and this is a common method opted by data management companies. 

3. Decision trees

This is another popular data analysis method in which a tree shaped diagram is used for representing regression or classification models. This fast and easily comprehended method divides a set of data into smaller sub-parts, and at the same time, related decision trees can be continuously developed. These trees depict why and how a particular choice can lead to another with the aid of branches. 

4. Fuzzy logic

The fuzzy logic approach is used to tackle uncertainty that erupts during data mining. This method is largely based on probability. It is known for its immense potential to extract vital information from several data sets. It is an innovative kind of various valued logic under which the true values of variables are actually a real number lying between 0 and 1. 

Key responsibilities of data analyst

A data analyst translates numbers into simple English. Now, every business or organization collects data. This can be anything including market research, sales figures, logistics or just any other set of data.

Now the role of a data analyst is to collect data and utilize it to help businesses in the decision making process. This can mean figuring out the new price of materials for the market, reducing the cost of transportation, cutting down production costs, etc. 

Now let us study the general responsibilities of a data analyst:

Data interpretation, analyzing results with the help of data analysis methods and then generate ongoing reports

Development and implementation of databases, data analytics, data collection systems and different strategies for optimizing statistical quality and efficiency

Identifying, analyzing and interpreting patterns and trends in complex sets of data

Acquiring data from primary and secondary sources while maintaining data systems or databases

Working with management for prioritizing business and need for information

Filtering and cleaning data that is collected by reviewing reports and printouts along with performance indicators to locate and rectify problems related to coding

Locating and defining opportunities for improvement of the new process

Job description of Senior Data Analyst

The job of a Senior Data Analyst is more complex and his services are inevitable for a large business house. In short, he helps clients to enhance their marketing performance with the help of data. Some of his key duties are as follows:

Strategy and management

A Data Analyst performs a managerial role where he supervises and oversees the performance of junior data analyst personnel ensuring efficient implementation of their duties. He is supposed to develop well structured analytical strategies. He analyzes large sets of data and he plays a major role in shaping the data infrastructure of the business.


He develops conceptual and logical data flows in support of various departmental projects. He is entrusted with the responsibility of guiding different departments of the organization for appropriate and effective data use and implementation. He is involved in data mining to search significant business insights and to communicate his verdict to the respective departments and the business at large. 


A Senior Data Analyst’s role is highly collaborative. He understands the data requirements of the business. He works closely with the departmental heads of various departments, key stakeholders and the management personnel to chalk out detailed requirements of the business create functional designs, conduct gap analyses, etc. 

The different data analysis methods are evident of the fact that data analysis is inevitable for any business in current times. Data analysis is extremely important to structure findings from various sources of data and to break a macro problem into smaller and easily understandable parts. It helps to reach a conclusion and make smooth and better business decisions. 

Data Analysis Data Science Regression Analysis Factor Analysis Artificial Neural Networks Decision tress Fuzzy Logic

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