Automated monthly reporting pack
Deliverable: A workbook that takes raw exports, cleans them in Power Query, produces the summary tables and charts, and saves a dated copy from one button, with errors surfaced clearly.
Course
The reporting job taught properly — Excel, SQL, Power BI and automation, organised around producing the recurring reports a business runs on rather than around software features.
6 modules · 4 months
Deliverable: A workbook that takes raw exports, cleans them in Power Query, produces the summary tables and charts, and saves a dated copy from one button, with errors surfaced clearly.
Deliverable: A set of SQL queries joining at least four tables to answer defined business questions, written so another analyst could read and modify them.
Deliverable: A Power BI dashboard built on a proper data model, presenting the month's position in a single view, with a written note on which decision each visual supports.
Deliverable: Two systems reporting different figures for the same period, investigated to root cause, with a written explanation a finance manager would accept.
Every student gets placement assistance — that is what 100% placement assistance means. It is support for all, not a job for all. We do not promise a specific salary, a specific number of interviews, or placement at any named company, and you should be wary of anyone who does.
Most MIS courses are a list — some Excel, some SQL, some Power BI — and leave the student to work out how they connect.
This one is organised around the monthly reporting cycle, because that is the actual job. Raw exports arrive messy. They have to be cleaned the same way every month. Figures have to agree with another system. A pack has to reach a manager by a date, and it has to be right. Each module exists because that cycle needs it.
The single biggest change in an MIS executive’s usefulness is the day they stop asking somebody else for an extract.
That is why SQL is in here and why it is taught to a specific depth — selects, joins across the four or five tables a report touches, grouping and aggregation. Not database administration, not tuning. Enough to get your own data, today, without a ticket.
Do a report manually once and you understand it. Automate it before you understand it and you have built something you cannot debug.
The automation module comes fifth on purpose. By then you have produced the pack by hand and know exactly which steps are identical every month and which need a judgement call. Those are different things, and only the first kind should be automated.
The last thing the course covers is presenting figures nobody wants.
Every MIS executive eventually carries a month that missed target, or a reconciliation that does not close. Doing that well — clearly, early, with the cause identified and without either hiding it or dramatising it — is what gets someone trusted with the reporting in the first place.
Questions
Produces the recurring reports a business runs on — daily sales, monthly performance, stock positions, collections — and answers the questions those reports raise. It is part data work, part accounting sense and part knowing which number the person asking really wants. This course is organised around that job rather than around a list of software.
It helps, because a lot of MIS work sits next to finance, but it is not required. What matters more is comfort with numbers and a willingness to check your own figures. Graduates from any stream take this and do well.
Data Analytics goes further into statistics, Python and drawing conclusions from data. MIS is closer to the business — recurring operational reporting, reconciliation and automation, with SQL and dashboards as the tools. If you want to build models, take Data Analytics. If you want to run the reporting a company depends on, take this.
Enough to pull your own data instead of waiting for somebody else to send it, which is the change that makes an MIS executive genuinely faster. That means selects, joins across several tables, grouping and aggregation. It does not cover database administration or performance tuning, and the SQL course goes further if you want it.
In most Indian companies, yes, and pretending otherwise would not help you in an interview. Power BI is growing quickly and the course covers it properly, but the monthly pack still lands in a spreadsheet more often than not. Being excellent at Excel and competent in the rest is the realistic target.
Three details is all we need. A course advisor will call you back.
The Excel that offices actually run on — lookup logic, PivotTables, Power Query pipelines, dashboards and enough VBA to automate the work you repeat every month.
SQL taught against a database big enough that a bad query is noticeably slow, because toy datasets hide performance entirely and performance is half of what the job tests.
Power BI taught as a data modelling tool rather than a chart menu — star schemas, relationships and DAX first, because that is what separates a report that scales from one that breaks.
A structured path from spreadsheets to SQL, Power BI and Python, built around the four things a data analyst is actually paid to do — pull the data, clean it, analyse it and explain it.
Next step
Tell us what you want to learn and we will help you pick the right course, batch and mode.