+ Reply to Thread
Page 4 of 4 FirstFirst ... 2 3 4
Results 31 to 39 of 39

Thread: A Review On Data Analyst

  1. #9
    Banned kantu has a reputation beyond repute kantu has a reputation beyond repute kantu has a reputation beyond repute kantu has a reputation beyond repute kantu has a reputation beyond repute kantu has a reputation beyond repute kantu has a reputation beyond repute kantu has a reputation beyond repute kantu has a reputation beyond repute kantu has a reputation beyond repute kantu has a reputation beyond repute kantu's Avatar
    Join Date
    Aug 2020
    Posts
    5,147
    Thanks
    2,826
    Thanked 5,564 Times in 2,298 Posts
    If you have done any kind of scientific research, you must be familiar with the term quantitative research. It is a type of research, besides a type of qualitative research. It is called quantitative because the results of the research will be in the form of statistical numbers. This type is mainly used by researchers who are trying to research something by taking certain measurements. Let's see how to analyze quantitative research data.

    Data analysis is a process for simplifying data in a form that is easier to interpret or understandable for those who read it. In data analysis, it means that you are trying to process data into information. Later, this information becomes a characteristic of data that is easy to understand and answers problems related to research

    Data analysis is understanding the meaning of all the data that has been collected, then grouping them and summarizing them into something that is easy to understand. Until finally a general pattern of all of them was found, represented by statistical symbols, such as the mean µ (mean), the number Σ (sigma), the significance level α (alpha), the correlation coefficient ρ (rho) and others. The data analysis technique depends on the research objectives and the type of data that has been collected.

    Data Analysis Stages


    In analyzing the data, there were several simple steps taken, namely editing, scoring, coding, cleaning, tabulating data, descriptive analysis, and inferential analysis. Later, the results of sample analysis in statistical units are continued to predict population parameters. Meanwhile, the results of population analysis in parameter units have been completed or there is no follow-up

    In the data analysis process, researchers need accurate and reliable data. So that it can be used in the research conducted. The key to quantitative data analysis (statistics) is simplification of the data. If you are going to analyze the data, then here are the steps:

    - Preparation
    Prepare all the data that has been collected, check the completeness or fill in the instruments in data collection.

    - Tabulation
    If your research uses a questionnaire / questionnaire / test, give a score (rating) according to what you have determined at the beginning of the research method. Give the code to the item that was scored earlier. Changing data, adjusting and modifying it according to the analysis technique to be applied. Usually, interval data will be converted to ordinal data (graded). Then the ordinal (interval) data is transformed into discrete data.

    - Application of Data
    (adjusted to the research approach)

    Though trading on financial markets involves high risk, it can still generate extra income in case you apply the right approach. By choosing a reliable broker such as InstaForex you get access to the international financial markets and open your way towards financial independence. You can sign up here.


  2. The Following 7 Users Say Thank You to kantu For This Useful Post:

    alkatiri (2020-09-14), bahiyaa (2020-09-14), dandin (2020-09-14), irmafuad (2020-10-19), ismar (2020-10-17), piton (2020-09-20), yuyul (2020-09-18)

  3. #8
    Banned yuyul has a reputation beyond repute yuyul has a reputation beyond repute yuyul has a reputation beyond repute yuyul has a reputation beyond repute yuyul has a reputation beyond repute yuyul has a reputation beyond repute yuyul has a reputation beyond repute yuyul has a reputation beyond repute yuyul has a reputation beyond repute yuyul has a reputation beyond repute yuyul has a reputation beyond repute yuyul's Avatar
    Join Date
    Nov 2018
    Posts
    18,377
    Thanks
    33,105
    Thanked 53,424 Times in 13,258 Posts
    This post is sponsored by a content payout program available to anyone to participate.
    Data digitization has opened up opportunities for the use of big data in universities. The value of big data lies in the results of analysis and predictions or the actions taken from the results of these analyzes and predictions. Data digitization enables effective practice to utilize big data analytics in the form of learning analytic, academic analytic and process analytic. By using big data analytics at the University which is supported by the cooperation of all educational enterprise contributors, be it students, staff, lecturers, administrators, and the community, it is possible to make accountable decisions based on information and data mined carefully, so that in the end it supports improvement. decision making based on data in universities in an effort to improve the success performance of students and institutions.

    With the use of big data analytics at universities, it can be obtained more insight into students, academics, and university processes so that it supports predictive analysis and increases decision making based on data which in turn can help improve the success performance of students and institutions.
    The use of big data analytic in universities which includes learning analytic, academic analytic, and process analytic.

    Benefits and Uses of Big Data Analytics for Universities

    1. Learning Analytic
    Big Data Analytic can be used to analyze in real time student experiences that can be generated from student activities, such as: lecture registration, payments, class participation, online learning, and assessment.
    Learning Analytic is analyzing learning data in real time so that it can be used to predict successful students and students who are at academic risk. With big data analytics in higher education, it can provide insight into students who are at risk of dropping out so that preventive action can be taken or provide additional support to increase their success, and confidence, in the learning process.
    Learning Analytic has the potential to help students and lecturers together recognize signs of danger before threats to learning success materialize. Learning Analytic provides tools, technology, and a platform to empower educators and open doors to meaningful learning experiences that can engage, inspire, and prepare current and future students for success.

    2. Academic Analytic
    The object that can be analyzed in Academic Analytic is the performance of academic staff. With academic analytic, real time analysis can be carried out on data which is a variable measurement of academic performance so that it can be seen that outstanding academic staff and academic staff are underperforming compared to other academic staff.
    For the academic analytic process, data sources can be used from the Outcomes Assessment, Lecture Assessment, Staff Assessment, Faculty Assessment, and Finance Assessment data.

    3. Analytic Process
    Big Data Analytic can also be used to analyze real time business processes in Higher Education. The data used can be obtained from log data or activity data from students, lecturers, and units related to the processes and activities that occur in tertiary institutions to then carry out process analytics using process mining to find new business process models. But process analytics is not limited to the process discovery of business processes but also allows for compliance checking, detecting deviations, predicting delays, supporting decision making, and recommending process redesigns.

    For analytics process, data sources can be used from log activity data from the Student Information System process, Cource Management, Online Education, Student Assessment, and log activity data from the finance process. Student Assessment, and data finance. Data from several sources is then collected in a data warehouse for analysis using real time analysis and prediction technology, such as: OLAP, Analytical Reporting Tools (Business Intelligence), and Data Mining / Predictive Modeling.
    The results of the analysis and predictions will be displayed in the form of a Dashboard Analytic Presentation which is expected to support the improvement of decision making in higher education in an effort to improve educational, academic performance as well as the success of students and institutions.

    Though trading on financial markets involves high risk, it can still generate extra income in case you apply the right approach. By choosing a reliable broker such as InstaForex you get access to the international financial markets and open your way towards financial independence. You can sign up here.


  4. The Following 5 Users Say Thank You to yuyul For This Useful Post:

    dandin (2020-10-04), irmafuad (2020-10-19), ismar (2020-10-17), kantu (2020-09-23), piton (2020-09-20)

  5. #7
    Banned m148 has a reputation beyond repute m148 has a reputation beyond repute m148 has a reputation beyond repute m148 has a reputation beyond repute m148 has a reputation beyond repute m148 has a reputation beyond repute m148 has a reputation beyond repute m148 has a reputation beyond repute m148 has a reputation beyond repute m148 has a reputation beyond repute m148 has a reputation beyond repute m148's Avatar
    Join Date
    Oct 2018
    Posts
    19,438
    Thanks
    37,600
    Thanked 53,441 Times in 13,937 Posts
    This post is sponsored by a content payout program available to anyone to participate.
    Definition of Data Analysis

    Data analysis is a process or effort to process data into new information so that the characteristics of the data become easier to understand and useful for problem solutions, especially those related to research.
    Data analysis can also be defined as activities carried out to change the results of data from research into new information that can be used in making conclusions. In general, the purpose of data analysis is to explain the data to make it easier to understand, then make conclusions.

    “Data analysis is the process that determines the business formally to find themes and formulate hypotheses (ideas) as suggested and as an attempt to provide assistance and themes to hypotheses “ ( Taylor 1975 )

    “ Data analysis is the process of arranging the order of data, organizing it into patterns, categories, and basic units of description “ ( Lexy J. Moleong, 2002)

    Data Analysis Steps and Procedures :

    1. Data collection, the initial stage of data analysis activity is data collection for analysis.
    2. The Editing Stage, which is the process of checking the clarity and completeness of filling out the data collection instruments.
    3. The coding stage, which is the process of identifying and classifying all statements on the instrument to collect data based on the variables being studied.
    4. Testing stage, namely the process of testing data quality, both in terms of validity and reliability of the instrument from data collection.
    5. Stage of Describing the Data, namely the process of describing the data by presenting it in the form of a frequency table or diagram with various measures of central tendency and dispersion measures. The aim is to understand the characteristics of the sample data from a study.
    6. Hypothesis Testing Stage, namely the process of testing a proposition whether it can be accepted or rejected, whether it has meaning or not. Based on this stage, conclusions or decisions will be made.


    Types of Data Analysis

    1. Descriptive Data Analysis
    The definition of descriptive data analysis is an analytical technique used in analyzing data by making pictures of the data collected without making generalizations from the research results. Some of them are included in descriptive data analysis techniques, for example the presentation of data in the form of:

    - Chart
    - Table
    - Presentation
    - Frequency
    - Diagram
    - and others

    2. Inferential Data Analysis
    The definition of inferential data analysis is a technique of analyzing data using statistics by making generally accepted conclusions. Inferential analysis using certain statistical formulas. The results of the calculation of the formula will be the basis for generalizing the sample to the population. Inferential analysis serves to generalize the results of a sample study for the population.

    Benefits of Data Analysis

    There are several advantages to analyzing data for study. Following are some of the advantages of analyzing data:

    - Get clearer measurement results.
    - More reliable identification process.
    - Allows to identify things that are important.
    - Can be seen visually so that it helps in making decisions quickly and accurately.
    - In business activities, it helps the process of identifying problems that require action or decisions.
    - Have a better awareness of potential customers.

    Though trading on financial markets involves high risk, it can still generate extra income in case you apply the right approach. By choosing a reliable broker such as InstaForex you get access to the international financial markets and open your way towards financial independence. You can sign up here.


  6. The Following 10 Users Say Thank You to m148 For This Useful Post:

    alkatiri (2020-09-14), bahiyaa (2020-09-14), dandin (2020-09-14), fadhiya (2020-09-14), irmafuad (2020-09-14), ismar (2020-10-17), jindon (2020-09-14), kantu (2020-09-14), piton (2020-09-14), yuyul (2020-09-14)

  7. #6
    Banned irmafuad has a reputation beyond repute irmafuad has a reputation beyond repute irmafuad has a reputation beyond repute irmafuad has a reputation beyond repute irmafuad has a reputation beyond repute irmafuad has a reputation beyond repute irmafuad has a reputation beyond repute irmafuad has a reputation beyond repute irmafuad has a reputation beyond repute irmafuad has a reputation beyond repute irmafuad has a reputation beyond repute irmafuad's Avatar
    Join Date
    Mar 2019
    Posts
    17,299
    Thanks
    22,821
    Thanked 38,731 Times in 12,018 Posts
    This post is sponsored by a content payout program available to anyone to participate.
    There are some who claim they are the same. There are also those who say that Data Scientists are data analysts who live and work in America. Because according to them these two professions are the same. But because this profession only appeared recently when Big Data came out, I personally state that these two professions are different even though one person may have both abilities as a Data Analyst and a Data Scientist.

    There are several opinions that support my opinion. The following is a summary that I can about the differences between these two professions.

    Data analyst or also sometimes called Business Data Analyst is a profession that observes data, looking for patterns from the data that indicate the condition of a company. In general, data analysts use data generated from the Business Intelligence (BI) application. Data analysts have deep skills in the business field that the company is engaged in. With this capability, the pattern of data received by applications such as BI can be visualized. These data patterns are generally of a general nature that many similar companies experience.

    We have known data analysts long before Big Data technology. All applications of business intelligence, data mining and data warehouse require a data analyst to translate data. This data needs to be translated into sentences that are understood by the top management level. So it is known, for example, whether the sales increase / decrease by what percentage and so on, depending on the type of data generated.

    Now what about the Data Scientist? Data scientist according to the name is a scientist or scientist. Like scientists, data scientists conduct experiments to find new things that will be useful for companies. these new things could be patterns that are not common or are not commonly known to similar companies. By discovering these new patterns, companies can gain an advantage over their competitors. Often times, victory in the competition in business is determined by the ability of data scientists to analyze the data. Examples of patterns that data scientists are looking for include patterns or item recommendation models that are commonly used on e-commerce web sites like Amazon or for example like on Netflix. This recommendation pattern or model, even though the name is the same, certainly differs from one company to another due to the characteristics of the consumers, the types of goods offered, etc. other than that

    Data scientists are needed to analyze large amounts of data contained in Big Data systems. A Data Scientist must have skills in three areas. First is the ability about the business logic in the field that the company he is working with, such as a data analyst. The second is statistical and mathematical ability which is sufficient to find out the following data patterns with algorithms. The third is the ability to use tools from the Big Data system that help it process and analyze data such as the Apache Mahout and Apache Spark Machine Learning applications.

    Though trading on financial markets involves high risk, it can still generate extra income in case you apply the right approach. By choosing a reliable broker such as InstaForex you get access to the international financial markets and open your way towards financial independence. You can sign up here.


  8. The Following 10 Users Say Thank You to irmafuad For This Useful Post:

    alkatiri (2020-09-13), bahiyaa (2020-09-13), dandin (2020-09-13), fadhiya (2020-09-13), ismar (2020-10-17), kantu (2020-09-13), m148 (2020-09-13), piton (2020-09-13), Unregistered (1), yuyul (2020-09-13)

  9. #5
    Banned alkatiri has a reputation beyond repute alkatiri has a reputation beyond repute alkatiri has a reputation beyond repute alkatiri has a reputation beyond repute alkatiri has a reputation beyond repute alkatiri has a reputation beyond repute alkatiri has a reputation beyond repute alkatiri has a reputation beyond repute alkatiri has a reputation beyond repute alkatiri has a reputation beyond repute alkatiri has a reputation beyond repute alkatiri's Avatar
    Join Date
    Jul 2020
    Posts
    7,107
    Thanks
    4,780
    Thanked 9,363 Times in 3,686 Posts
    This post is sponsored by a content payout program available to anyone to participate.
    Entry-Level Data Analyst

    Entry-level data analysts are those who are just getting into the world of data analysis business. They are usually just graduating from university and are just starting out in this field. There are also many beginner data analysts who start their careers without having a formal education from any institution They learn data analysis techniques using alternative methods. There are so many private tutors or YouTube videos that can be used to learn data analysis methods, formal education through certified online courses or e-learning programs still takes precedence over these alternatives.

    Entry-level data analysis jobs have the easiest requirements when compared to other types. This relatively low demand is due to the fact that most companies prefer these young data analysts to work on increasing their knowledge first and learning to experience the intricacies of data analysis techniques. When a company hires an entry-level data analyst, the company already knows that they have to teach the young analyst all the basic skills needed to do well in the company. For this reason, many companies have special training programs that are explicitly designed for those who wish to work as data analytics specialists.

    As a beginner, the roles and responsibilities of a data analyst revolve around simple and easy work, such as helping supervisors and other senior teams with their main work, as well as practicing and learning how to analyze data using the programs needed to do the task, etc. You may think that the job and responsibility of an entry-level data analyst is something easy, but it's not really. Because there are so many people competing for a job position as a data analyst. If you decide to stick with your career as a data analyst, then there are many career path options you can take (assuming that you put the time and energy into achieving it). If you want a bigger salary and progress to become a junior data analyst, then you should devote your spare time to further study this topic, so that your potential can be maximized.

    Though trading on financial markets involves high risk, it can still generate extra income in case you apply the right approach. By choosing a reliable broker such as InstaForex you get access to the international financial markets and open your way towards financial independence. You can sign up here.


  10. The Following 10 Users Say Thank You to alkatiri For This Useful Post:

    bahiyaa (2020-09-13), dandin (2020-09-13), irmafuad (2020-09-12), ismar (2020-10-17), jindon (2020-09-13), kantu (2020-09-13), m148 (2020-09-13), piton (2020-09-13), Unregistered (1), yuyul (2020-09-11)

  11. #4
    Highly Reputed Member jindon has a reputation beyond repute jindon has a reputation beyond repute jindon has a reputation beyond repute jindon has a reputation beyond repute jindon has a reputation beyond repute jindon has a reputation beyond repute jindon has a reputation beyond repute jindon has a reputation beyond repute jindon has a reputation beyond repute jindon has a reputation beyond repute jindon has a reputation beyond repute jindon's Avatar
    Join Date
    Apr 2019
    Posts
    15,438
    Thanks
    19,164
    Thanked 33,038 Times in 10,729 Posts
    SubscribeSubscribe
    subscribed 10
    This post is sponsored by a content payout program available to anyone to participate.
    Data practitioners in companies make this profession more and more in demand. You should know, a data analyst should have some business knowledge in order to be able to fulfill the goals of a company. Data Analyst is a profession in charge of analyzing data and gaining new knowledge through it. Having good analytical skills doesn't seem enough, the competence of good hard skills is one of the things that supports you in a career in the data field.

    SQL is a programming language used in accessing data. The ability to process SQL is a fundamental and mandatory hard skill in a career in the data field. Not only data analysts but also data engineers and data scientists need to master this hard skill because to be able to do the analysis, of course it requires data which is usually stored in a database. "With SQL allows the data analyst to be able to read and transform data for later processing into insight,"

    Though trading on financial markets involves high risk, it can still generate extra income in case you apply the right approach. By choosing a reliable broker such as InstaForex you get access to the international financial markets and open your way towards financial independence. You can sign up here.


  12. The Following 8 Users Say Thank You to jindon For This Useful Post:

    alkatiri (2020-09-11), dandin (2020-10-04), irmafuad (2020-09-12), ismar (2020-10-17), m148 (2020-09-13), piton (2020-09-20), Unregistered (1), yuyul (2020-09-14)

  13. #3
    Banned alkatiri has a reputation beyond repute alkatiri has a reputation beyond repute alkatiri has a reputation beyond repute alkatiri has a reputation beyond repute alkatiri has a reputation beyond repute alkatiri has a reputation beyond repute alkatiri has a reputation beyond repute alkatiri has a reputation beyond repute alkatiri has a reputation beyond repute alkatiri has a reputation beyond repute alkatiri has a reputation beyond repute alkatiri's Avatar
    Join Date
    Jul 2020
    Posts
    7,107
    Thanks
    4,780
    Thanked 9,363 Times in 3,686 Posts
    This post is sponsored by a content payout program available to anyone to participate.
    In today's digital era, the need for Data Analysts is increasing. Especially with the increasing number of technology-based startups or startups. They definitely need a Data Analyst to process company data.

    In general, the data analyst is responsible for translating the numbers into a report that management can understand. Every company definitely needs sales data, market research, logistics, and transportation costs.

    A job in the data field requires a Data Analyst to be able to study a problem with systematic techniques and frameworks. Morning Future reported, data analyst is a profession that deals with data analysis. This profession requires someone to understand the origins of data and distortion by analyzing these data.
    The way to analyze these data is not arbitrary, but by using special technology. Morning Future itself stated that this profession is the profession of the future. This statement is based on data quoted from the World Economic Forum. In this data, it is stated that in 2020 this profession will become the favorite profession throughout the world.
    The data above is also corroborated by data from IBM. According to these data, it is estimated that there will be 700 thousand recruiters by 2020.

    Data analyst duties:
    - Understanding the origin of data and the possibility of data distortion with special technology.
    - Collect and analyze data
    - Identify the correlation and interpretation patterns contained in the data. The results of this identification will later produce useful information, especially for the company

    Though trading on financial markets involves high risk, it can still generate extra income in case you apply the right approach. By choosing a reliable broker such as InstaForex you get access to the international financial markets and open your way towards financial independence. You can sign up here.


  14. The Following 8 Users Say Thank You to alkatiri For This Useful Post:

    dandin (2020-10-04), irmafuad (2020-09-12), ismar (2020-10-17), jindon (2020-09-02), m148 (2020-09-13), piton (2020-09-20), Unregistered (1), yuyul (2020-09-14)

  15. #2
    Banned yuyul has a reputation beyond repute yuyul has a reputation beyond repute yuyul has a reputation beyond repute yuyul has a reputation beyond repute yuyul has a reputation beyond repute yuyul has a reputation beyond repute yuyul has a reputation beyond repute yuyul has a reputation beyond repute yuyul has a reputation beyond repute yuyul has a reputation beyond repute yuyul has a reputation beyond repute yuyul's Avatar
    Join Date
    Nov 2018
    Posts
    18,377
    Thanks
    33,105
    Thanked 53,424 Times in 13,258 Posts
    This post is sponsored by a content payout program available to anyone to participate.
    The Data Analyst is responsible for translating figures into reports that management can easily understand. Every business collects data, whether it's sales data, market research, logistics, or transportation costs. The job of a Data Analyst is to acquire and use that data to help companies make better business decisions. This can mean pricing a new product to launch, finding ways to reduce transportation costs, solving problems that are costly to the company, or determining how many employees have to work on Saturdays.

    There are various kinds of Data Analysts in the work environment such as Operations Analysts, Marketing Analysts, Financial Analysts, etc.

    Duty :
    Interpret data, analyze results using statistical techniques and provide ongoing reports
    Develop and implement databases, data collection systems, data analysis and other strategies that optimize the efficiency and quality of statistics
    Get data from primary or secondary data sources and maintain databases / data systems
    Identify, analyze, and interpret trends or patterns in complex data sets
    Filter and "clean" data by reviewing computer reports, printouts, and performance indicators to find and fix coded problems
    Work closely with management to prioritize business and information needs
    Look for and define new process improvement opportunities

    Though trading on financial markets involves high risk, it can still generate extra income in case you apply the right approach. By choosing a reliable broker such as InstaForex you get access to the international financial markets and open your way towards financial independence. You can sign up here.


  16. The Following 8 Users Say Thank You to yuyul For This Useful Post:

    alkatiri (2020-08-30), dandin (2020-10-04), irmafuad (2020-09-12), ismar (2020-10-17), jindon (2020-09-02), m148 (2020-09-13), piton (2020-09-20), Unregistered (1)

  17. #1
    Banned piton has a reputation beyond repute piton has a reputation beyond repute piton has a reputation beyond repute piton has a reputation beyond repute piton has a reputation beyond repute piton has a reputation beyond repute piton has a reputation beyond repute piton has a reputation beyond repute piton has a reputation beyond repute piton has a reputation beyond repute piton has a reputation beyond repute piton's Avatar
    Join Date
    Aug 2020
    Posts
    7,316
    Thanks
    5,434
    Thanked 10,983 Times in 3,910 Posts
    This post is sponsored by a content payout program available to anyone to participate.

    A Review On Data Analyst

    Are you interested in knowing how to become a Data Analyst? The following are some of the abilities you need to have.

    - Data Analyst jobs at entry level are usually filled only by graduates of certain majors. Examples include having a bachelor's degree in marketing, finance, computer science, mathematics, statistics and economics, the reason is that knowledge of analysis and calculation is needed in this type of work.

    - Jobs in the data sector require a Data Analyst to be able to study a problem with a systematic framework and technique.This step makes it easier for the Data Analyst to analyze every company's business problem.

    - Besides being able to analyze data, a Data Analyst is expected to have the knowledge and skills to assess the progress of a company's business. So, the insights generated by the Data analyst will be in line with the company's business.

    - Even though they always dissect data, a Data Analyst must also have good communication skills. Because, he will explain technical matters, assess the progress of the company's business, and convey it to many people in the company.

    - In analyzing data, teaching computers to be able to make decisions or predict something after having data is important. If you don't have this one skill, try to start learning it.

    - The median, mean, mode, and standard deviation are examples of statistical concepts that you need to learn. The reason is, to become a Data Analyst, you need to be able to interpret data. Have a very strong understanding of inferential and descriptive statistics

    - As a Data Analyst, it would be better if you also understand a programming language. Even so, the more programming languages you understand, the better it will be and help your work. SQL programming is the most common among Data Analysts. In order to become a reliable Data Analyst, you need to keep learning and gaining experience.

    - As a prospective Data Analyst, you should have a lot of knowledge in the field of Microsoft Excel. Because later your work will have a lot to do with Excel, such as calculating numbers, organizing data for analysis. Microsoft Excel methods are still widely used such as writing Macros by using VBA search to perform analysis

    - Besides getting knowledge related to Data Analysts from universities, you can also hone your skills by taking online courses provided for the Data Analyst profession. You can try learning Basic Data Analyst with Python which is taught by IBM at Coursera

    Though trading on financial markets involves high risk, it can still generate extra income in case you apply the right approach. By choosing a reliable broker such as InstaForex you get access to the international financial markets and open your way towards financial independence. You can sign up here.


  18. The Following 8 Users Say Thank You to piton For This Useful Post:

    alkatiri (2020-08-30), dandin (2020-10-04), irmafuad (2020-09-12), ismar (2020-10-17), jindon (2020-09-02), m148 (2020-09-13), Unregistered (1), yuyul (2020-09-13)

+ Reply to Thread
Page 4 of 4 FirstFirst ... 2 3 4

Posting Permissions

  • You may not post new threads
  • You may not post replies
  • You may not post attachments
  • You may not edit your posts

Currently Active UsersCurrently Active Users

There are currently users online. members and guests

Forex Forum India | Forex Community Place Statistics Forex Forum India Statistics

Most users ever online was .

Welcome to our newest member,

Threads:

Posts:

Member: