What is the major responsibilities of data analysis....?

1.    Participate in the design of data collection tools (questionnaires, check lists,...etc)

2.    Develop the data analysis plan and select the appropriate statistical analysis tests and procedures according to the type of the data according to the desired report or list of indicators

3.    Design the data entry sheet in the electronic data analysis programme, or the master sheet if the data is to be analyzed manually

4.    Supervise the data entry process

5.    Run a quality check of the data to assess accuracy and completeness of the entered data and clean accordingly

6.    Analyze the data and produce it in the form of tables, charts or figures

7.    Participate in the interpretation of the results

8.    Write the data analysis report

 

It is highly dependant on the position. In general the following are the processes involved in data science and analytics:

 

1. Data acquisition
2. Data wrangling/munging (clean & tidy data)
3. Exploratory analysis & visualization
4. Statistical inferencing & regression modelling
5. Machine learning (the above process can be extended into supervised and unsupervised machine learning) and employing/developing algorithms.
6. Reporting & Communicating the results through static or interactive reports. Again based on the position & the work the results might end up in the form of an interactive web app.

These processes and responsibilities could be those of one man or a whole team. It all depends on the company and project you're working on.

Prepares daily and weekly reports, monthly and yearly and submitted to senior management, including production, quality, costs and financial data analysis. ... Diagrams of product quality work throughout the year to improve quality. ... Work report to the company's incentive each month. .... Work yields a periodic report to the company each month .... the work of a monthly report to calculate the cost of each product to manage costs. ... Work Report of the Department of Planning data good and bad raw materials

Its all about reports (collecting and cleansing the data then convert those data into numbers using your statistical and analytical tools

Post data compilation, it's authenticity needs to be verifiable. Must not have duplication,

Must be clear and specific