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Data analysis is the process of collecting, inspecting, and evaluating data to discover meaningful information and reaching a conclusion for effective decision making. Data analysis is vital to gain the insight that drives the best possible business decisions. In short, data analysis is interpreting data into meaningful information. There are different types of data such as quantitative and qualitative data which a data analyst uses for analyzing.
Quantitative Data: It can be measured or written down in numerical form. E.g. height, size, length, price, area, volume, humidity, temperature etc.
Qualitative Data: It deals with the information, description characteristics etc. that you can observe but not measure — E.G. colour, taste, smell, texture, etc.
Before analyzing, the data is collected from various sources using different methods. Some of the means of data collection are as follows:
Primary Data: It is the data that is received by the researcher or analyst for the first time. The researcher himself collects this data. The sources of collecting primary data are questionnaires, survey, interview, and observation.
Secondary Data: It is the data which already exists. The researcher or analyst uses this data for data analysis. The sources of collecting secondary data are reports, journals, news, books, magazines etc.
A data analyst retrieves, gathers and organizes data and uses it to settle at meaningful conclusions. Data analysts’ work varies depending on the type of data that they use to complete their work. It could be sales, social media, inventory, etc. It could vary according to the requirements of client-specific projects as well.
Companies in almost every industry benefit from the work of data analysts, from healthcare providers to retail stores to fast-food chains. Data analysts bring insights to an organization that is extremely valuable to employers who want to know more about consumer needs. Data analysts spend most of their time developing systems for collecting data and compiling them into reports that are instrumental in the development of the company.
You can include a data analyst in everything from setting up an analytics system to providing insights based on the collected. An analyst may even have to train others in his data-collection order.
Bachelor’s Degree from an accredited university or college in computer science.
Analyst must have relevant work experience as a data analyst or experience in related field.
Ability to work with stakeholders.
Ability to assess potential risks.
He must be able to analyze existing tools and databases.
Ability provide software solution recommendations.
He must be able to translate business requirements into non-technical terms for better understanding.
He needs to have high-level experience in methodologies and processes for managing large-scale databases.
Understanding of metadata standards.
Competent written and verbal skills.
Manage, create, update and delete master data.
Manage user activities.
Ensure the quality of imported data.
Commission and decommission data sets.
Analyst must process confidential data in compliance with guidelines.
Generate and develop analytic reports from single or multiple systems.
Manage and design reporting environment, including data sources, security, and metadata.
He must support the data warehouse to identify and revise reporting requirements.
He needs to support data-integrity and normalization initiatives.
Assess and implement upgraded software.
Assist with strategic decisions on new systems.
Troubleshoot reporting database environment and analytic reports.
Evaluate changes and updates to source production systems.
He must be able to train end-users on new reports and dashboards.
Provide technical expertise in data storage structures, data mining, and data cleansing.
Data analysis is a complicated job as the data analyst have to collect data from various sources, gather information, facts and figures via several sources, and then analyze the review and conclude the data with possible solutions. For this intricate work, organizations need a team of professionals who will do it for them. When you outsource a data analyst, you get a dedicated team of data professionals who are well skilled and have in-depth knowledge of data analysis.
Even if you already have IT people or analysts in your staff, they may not always be able to concentrate on data analytics. To avoid this risk, you can outsource an analytical data service, so that it lightens the burden of your internal source. The outsourcing company will give you the desired results and proper data analysis of your company.
When you hire data analysts, the data analytical agencies have a variety of data analysis tools used to analyze, organize and visualize data. Another advantage of data analytics outsourcing is that they have experience in their respective field of work. You can be sure that they have done similar projects for other organizations also. They can use their expertise and come up with some creative and innovative ways to interpret your data.
Outsourcing is the most cost-effective way for any services. If you outsource the services of data analytics, you will save a lot of your money. It will be costly if you hire software engineers, project managers, and database specialists separately for data analysis. On the contrary, if you outsource data analytics services, it will minimize your cost.
The outsourcing agency has a team of professional experts who know their job role very well. They know what to do when to do, how to do, and how to manage time as well. So, hiring an outsourcing company for data analytics means saving time.
The outsourcing data analytics agencies ensure proper and systematic management, organizing and storage of your crucial data across multiple platforms.
Outsourcing data analytical agencies provide you with high-quality service related to your data management, data storage, interpretation etc. It will help you make effective business decisions and eases your decision-making process.
As business data contain very crucial and confidential information regarding your business, the outsourcing agencies make sure that they secure your data and keep your data safe.
A data analyst’s job is to collect relevant data and use it to help companies make better business decisions. I am good with numbers, collecting data, and market research. I want to work in this job role because it encompasses the skills at which I am proficient. Also, I find data and marketing research interesting.
One way to approach this question is; “I would prefer to work as a marketing analyst because it’s in line with my skills and interests. Besides, I have seen that the companies who hire for this role work in industries that are booming and can, therefore, provide good career growth.”
A sample answer for this question could be; “I have a breadth of software experience. For example, at my current employer, I do a lot of ELKI data management and data mining algorithms. I can also create databases in Access and make tables in Excel.”
The various steps in an analytics project include
Validation of data
Implementation and tracking
Data cleansing deals with identifying and removing errors and inconsistencies from data to enhance the quality of data.
A few of the best data-cleansing practices include the following:
Sorting data by different attributes.
For large datasets cleansing it stepwise.
Improving the data with each step until you achieve a good data quality.
For large datasets, break them into small data. Working with less data increases your iteration speed.
Managing common cleansing task to create a set of utility functions/tools/scripts. It may include, remapping values based on a CSV file or SQL database or, regex search-and-replace, blanking out all values that don’t match a regex.
In case of any issue with data cleanliness, arranging them by estimated frequency and attack the most common problems is necessary.
Analysis of the summary statistics for each column.
Keeping track of every date cleaning operation, so you can alter changes or remove services if required.
Google Search Operators
Google Fusion tables
|DATA PROFILING||DATA MINING|
|It targets the instance analysis of individual attributes. It gives information on various characteristics like value range, discrete value and their frequency, occurrence of null values, data type, length, etc.||It focuses on cluster analysis, detection of unusual records, dependencies, sequence discovery, relation holding between several attributes, etc.|
Some of the common problems faced by data analyst are
Duplicate entries and values.
Different value representations.
Identifying overlapping data.
Hadoop and MapReduce is the programming framework developed by Apache for processing extensive data set for an application in a distributed computing environment.
The missing patterns are
Missing completely at random
Missing at random
Missing that depends on the missing value itself
Missing that depends on unobserved input variable
In KNN imputation, it imputes the missing attribute values by using the attribute’s value that is most similar to the attribute whose values are missing. It determines the similarity of two attributes by using a distance function.
Methods used by data analyst for data validation include-
Prepare a validation report that gives information on all suspected data. It should provide information like validation criteria that it failed and the date and time of occurrence
Experience personnel should examine the suspicious data to determine their acceptability
Invalid data should be assigned and replaced with a validation code
To work on missing data, use the best analysis strategy like deletion method, single imputation methods, model-based methods, etc.
To deal with the multi-source problems, I would restructure schemas to accomplish a schema integration. I would try to Identify similar records and merge them into a single document containing all relevant attributes without redundancy
Although single imputation is widely in use, it does not reflect the uncertainty created by missing data at random. So, multiple imputation is more favorable than single imputation in case of data missing at random.
Criteria for a good data model includes;
It is easily consumable
Massive data changes in a good model should be scalable
It should provide predictable performance
A good model can adapt to changes in requirements
Time series analysis can process in two domains, frequency domain and the time domain. In Time series analysis, the output of a particular process can be forecast by analyzing the previous data by the help of various methods like exponential smoothening, log-linear regression method, etc.
A correlogram analysis is the standard form of spatial analysis in geography. It consists of a series of estimated autocorrelation coefficients calculated for a different spatial relationship. It can be used to construct a correlogram for distance-based data when it expresses the raw data as a distance rather than values at individual points.
Statistical methods that are useful for data scientist are’;
Spatial and cluster processes
Rank statistics, percentile, outliers detection
Imputation techniques, etc.
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