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Components of a Financial Accounting System

06/03/2016 By debkr

Nominal Ledger (NL)

Also known as the General Ledger (GL). Structured by the Chart of Accounts, which is in turn defined by the Management Accounts and other Management Information, Reporting and Analysis requirements specific to the business. Accounts consist of Trial Balance – made up of various ledger entries (Purchase Ledger, Sales Ledger, etc.) together with various Period-End Journals (Accruals, Prepayments, Depreciation, etc.). Reported in month-end accounts (Financial Statements: Profit and Loss, Balance Sheet, Cash Flow Statement) and other general ledger reports. Actual results are compared monthly against Budget or Forecast, and are often also forecast out to year-end. Month-end and ad hoc analysis and reporting can also be included (e.g. Revenue analysis, Cost analysis, Product or Service analysis, Customer analysis, Contribution or Profitability analysis). [Read more…] about Components of a Financial Accounting System

Critical Metrics: Designing Better Management Information Systems

04/03/2016 By debkr

Three kinds of Business Metrics: Sales (Revenue), Profitability (Efficiency) and Risk metrics. (Ref. # 1)

Four types of Functional Analytics: Production (e.g. Publishing), Marketing/Sales, Operational and Financial analytics.

Create a matrix 3 across by 4 down.

Fit all the different possible metrics (appropriate to your specific business) in the relevant sector, being one of the 3 kinds of Business Metrics and one of the 4 types of Functional Analytics. [Read more…] about Critical Metrics: Designing Better Management Information Systems

Business Analytics: PTP and Syllabus

04/03/2016 By debkr

Business Analytics: personalised training plan

Key Learning Objectives
Understand data-rich business environments and their commercial drivers (incl. how they differ from trad. businesses)
Understand and apply business metrics best practice (setting, measuring, reporting, decision-making)
Select and use relevant data tools to solve business problems (competitiveness, profitability, cash flow, marketing)
Understand and apply critical business metrics for internet marketing / direct-response marketing businesses
Apply data analysis best practice (framing the problem, developing robust predictive models, generating insights and value from data, visualising/communicating data and insights) [Read more…] about Business Analytics: PTP and Syllabus

Programming in Python: First Steps

02/03/2016 By debkr

PTP 1. Programming Stream / B. Python (beginner)

PTP Outcomes:
Familiarise myself with Python language and ecosystem
Be able to do simple coding exercises
Gain an insight into Python’s use in data science, data manipulation and visualisation
Familiarise myself with relevant data analysis packages/libraries

Initial thoughts
As mentioned in my Introduction to Data Science post, I’ve researched and set myself a Personalised Training Plan based on the knowledge/skills I’ll need to learn and develop if I’m to pivot my career away from finance/accountancy and towards data/analytics. As first steps in the Programming and Data Science streams, I’ve signed up for two specialisations at Coursera.org (links here and here). [Read more…] about Programming in Python: First Steps

Introduction to Data Science

02/03/2016 By debkr

PTP 5. Data Science Stream / A. Introduction to data science

PTP Outcomes:
Learn about the history of data science, where it’s come from, where it’s going to
Get an overview of the main principles, methods and applications of data science
Understand how data scientists develop insights from raw data, and how they present those insights to others
Become familiar with the most important tools, languages, platforms being used by data scientists
Learn about the fundamentals of data engineering

Some thoughts before I being
My Personalised Training Plan has been set after reasonably extensive research online, taking account of topics covered by various online and offline schools/courses, the advice of Analytics Vidhya and Ajit Jaokar on how to move into a data science career, as well as a review of key skills required by data scientists based on current jobs advertised online (e.g. on LinkedIn). [Read more…] about Introduction to Data Science