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MohitKR

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  1. Odds ratio is the measure of association between an exposure and an outcome. It quantifies the relationship between an exposure and a case control. It corresponds to the odds that an outcome will occur for a particular exposure, compared to that in the absence of that exposure. Odds ratios are mostly used for case-control studies. They can also be used in cross-sectional and cohort study designs with some assumptions. Odds ratio example The above table shows two levels of exposure to cold drink: those who drink it, those who didn’t and also shows two outcome levels: (“cases”) people who are ill and (“controls”) people who are not. The odds ratio is calculated as below: 1. People who are ill: people who drink cold drink / people who did not = 14/18 2. People who are not ill: people who drink cold drink / people who did not = 33/23 3. Dividing the two results, we get (14/18) / (33/23) = 0.54 The resulting odds ratio of 0.54 means that ill people were about half as likely to drink cold drink as well people. The odds ratio is used to determine whether a particular exposure is a risk factor for a particular outcome, and to compare the magnitude of various risk factors for that outcome. · Odds Ratio >1: Exposure relates to higher odds of outcome · Odds Ratio=1: Exposure doesn’t affect the odds of outcome · Odds Ratio<1: Exposure relates to lower odds of outcome
  2. Technical debt which is also known as code debt results when the development teams take actions to expedite the delivery of a project which needs refactorization. It is the result of prioritizing the delivery speed over perfection in code. The three main types of technical debts are as follows: 1. Deliberate Debt Engineers often know what the right way is; for doing something and what is the fastest way of doing something. Many a times, the quick way is the right way but sometimes the team intentionally does the wrong way because they need to quickly deliver the product in the market. How to address it: The agile team will not agree, but it is ideally sensible to track this type of technical debt in the backlog specifically when the deferring work needs completion. It it’s not tracked; it will turn into accidental design debt over a period. Products owners are accountable for this kind of debt as it is incurred because of business decisions. 2. Accidental Design Technical Debt: The team tries to balance forward thinking and future-proofing their designs with simplicity and quick delivery while designing the software systems. This is a very difficult balance to get in a single time as systems regularly evolves and requirements change continuously. You will realise the design made is flawed and the new functionality is slow to implement and difficult. A newer design would be much easier to refactor but sometimes you may have to do more significant refactoring. How to address it: Refactoring a system itself is a huge topic, but this needs to happen every now and then, otherwise the system gets over engineered and slows down all along. Product owners and team leaders are accountable to resolve this type of technical debt as it is incurred because of design decisions and frequent change in requirements. 3. Bit Rot Technical Debt: This debt happens over time, the system slowly passes onto unnecessary complexities through lot of incremental changes when worked by several people who may not have full understanding of the original design. How to address it: This is the only type of technical debt which you should avoid consistently. The teams should take time to understand the design of system even if they have not originally designed it and they work to improve the design incrementally and work on always improving the code. The development team is accountable to avoiding this kind of technical dent as it is incurred because of the developers involved in the development team. Below are various common causes of technical debt: Insufficient definition where requirements are still getting defined during development which causes rework. Enhancements of projects over a very long period. Business Pressures - when the business considers getting something released sooner before the necessary changes are complete technical debt is involved. Decisions made due to lack of process understanding. Lack of proper documentation where the code is created In scenarios where the software functions are not modular and is not flexible to adapt the changes as per business needs Lack of knowledge sharing around the organisation and proper mentorship of developers around the organisation. Delay in refactoring, the longer it takes for refactoring the codes are added more and more and the technical debt becomes bigger. Development of codes parallelly which creates debt because of the work required to merge into a single source code. Lack of poor technological leadership and last minute specification changes.
  3. Time-series analysis is a technique for analysing time series data which is a series of data points recorded over a specified period. The analysis extracts meaningful statistical information and helps to forecast future value. It also helps to identify the characteristics of the data. Time series(Y) have following components: 1. Long term trend(T) – It is the movement of any data where in the short-term effects such as seasonal variations or cyclic variations are ignored. For example, the enrolment trend in a university may be a steady climb on average over the past 50 years. This trend will be visible despite having a few years of loss or stagnant enrolment followed by years of rapid growth. 2. Cyclical effect(C) - This has relatively long-term patterns of oscillation in the data. These cycles may take many years. For example- There are various long cycles in business economics which take lot of years. 3. Seasonal effect(S) – Patterns of ups and downs which can be predicted and occurs at repeatable intervals year on year. For example – Weather forecast shows seasonal variations in temperature. 4. Error effect(N) - Every set of data has errors. These are random variations and occurs due to factors which cannot be controlled. The time series(Y) consists of the product of the individual factors and is represented as below: Time Series = Long Term Trend X Cyclical Effect X Seasonal Effect X Error Effect Y= T x C x S x N
  4. 8 steps of change management process 1. Establish a sense of urgency If we can create a scenario where individuals are aware of an existing problem and can see a possible solution the support for change will rise. 2. Form a powerful coalition Assemble a team with enough power and influence in the organisation to lead the change effort. It’s not possible to lead the whole change process alone, and therefore coalition is important. The coalition will consist of a range of people with different skills and experience to maximise its effectiveness. This will help in spreading messages throughout the organisation and ensure there is enough support for the change organisation wide. 3. Create a vision Create a vision which is easy to understand and generate support from the whole organisation. To have maximum effect it also needs to be inspirational. 4. Communicate the vision Use every possible way to communicate the vision. We can utilise the coalition we have built up to continuously communicate this message in their networks in every area of the business 5. Empower Others Identify and Remove obstacles to the change. Change systems or structures that undermine the change without disrupting any other business areas. 6. Create short term wins Change processes will take a while to reap benefits so it is important to demonstrate the advantages of the change process by creating some quick wins which is useful for motivation. Plan visible quick wins. Implement and recognise employees involved. 7. Consolidate Improvements and build on change Change the policies and procedures which does not fit the vision. Engage in recruiting and promoting employees who can support in implementing the desired vision. 8. Institutionalise Changes Continuously articulate the connections between the new way of working and corporate success. Weave new corporate culture into leadership development and succession planning. Neglecting any of the above steps may be enough for the whole initiative to fail.
  5. In today’s world it is very important to understand the data for actionable insights. Data visualization is of utmost importance which helps us to understand the patterns, insights and multiple layers of the data. It not only simplifies the understanding of the data but also helps in getting nice visualisations which are eye catchy. Data visualization charts like bar charts, scatterplots, line charts, waterfalls, funnels, geographical maps, etc. are extremely important. They let us understand the information by looking at them. Normally we would have to read spreadsheets or reports to understand the data. Data Visualization allows analysts to create effective visual data models according to their needs and specifications conveniently. Few of the tools are as below: 1. Tableau It is a data visualization tool used by data analysts, data scientists etc. to visualize the data and get insights based on it. 2. SAP Analytics Cloud It uses business intelligence and data analytics capabilities that evaluates your data and create visualizations predicting outcomes. It provides tools that helps to identify possible errors in the data and categorizing different data measures and dimensions. 3. Microsoft Power BI It is a data visualization platform that focusses primarily on creating a data-driven intelligence. It offers self-service analytics tools which can be used to analyse and share the data. 4. Looker Looker tool that can go in-depth in the data and analyze it to obtain useful insights. Looker provides real-time data dashboards for more in-depth analysis enabling instant decisions. 5. Zoho Analytics It is a Business Intelligence and Data Analytics software that helps create data visualizations. It can obtain data from multiple sources and mesh it together to create multidimensional data visualizations. 6. Sisense It is a business intelligence-based data visualization system and provides various tools that allow data analysts to simplify complex data and obtain insights. 7. IBM Cognos Analytics It is an AI based business intelligence platform supporting data analytics along with visualisation. 8. Qlik Sense It helps companies to become data-driven enterprises by providing an associative data analytics engine, sophisticated Artificial Intelligence system, and scalable multi-cloud architecture allowing to deploy any combination of SaaS, on-premises or a private cloud. 9. Domo It is a business intelligence model that contains multiple data visualization tools providing a consolidated platform where data analysis can be performed thus creating interactive data visualizations enabling users to easily understand your data conclusions.
  6. Refactoring implies improving the internal structure of an existing program source code, while safeguarding its external behaviour. It simply refers to particular behavior preserving transformation. It is not limited only to writing the code again fix the bugs Improve aspects of software like its interface etc. Refactoring actions are inclined towards the below to maintain the desired preserving conditions Duplicates- removing duplicates enables to change the code in the future Clarity - Renaming and redistributing responsibilities captures much more concepts thus improving reading and comprehension time Below are the benefits of refactoring: it improves objective attributes of code corelating with ease of maintenance It helps code understanding It encourages developers to think and understand design decisions, It favors emergence of reusable design patterns and code modules
  7. Visual controls are a subset of visual management. Visual management is a process by which information is conveyed via visuals displays. It is designed to create a visual workplace with controls communicating without words and interruptions in process. Visual controls convey information and provide direction as to what action to take based on the nature of the visual signal. Color-coding is the simplest form of visual control on equipment. Flashing red light indicates there is some problem and an inspection or intervention may be necessary. Flashing green light conveys that the equipment is working as planned, and no control action is required. Visual controls are designed to drive a specific behaviour or action. There is a hierarchy of different displays and controls, as represented as below. Three benefits of visual controls 1. Easy and quick to understand information When looking at an Excel, it is easy and quick to spot an issue if the cell is coloured red. 2. Improve communications Assuming everyone knows the meaning of red light. Whenever we see red everyone should understand what has happened as well as the action needed to correct the situation. 3. Keep the flow moving smoothly An overhead sign of “Enter Here” and “Exit Here” drives the desired behaviour of people entering and exiting. Visual Controls helps to identify problems, reduce waste, reduce inventory, shorten lead times, create a safe working environment, reduce production costs and may even increase your profits.
  8. INVEST is a guide for meaningful stories and helps to assess the quality of the user stories. If the user story fails to meet any one of these below criteria, the team must reword or rewrite it. I – Independent – user stories should be independent from each other so each of them can be delivered separately N – Negotiable – user stories should have scope of negotiation and further discussion V – Valuable – User Stories should do value addition E – Estimable – User Stories should be easy to understand and could be estimated basis tasks S – Small – User Stories should not be too big, ideally it should not take more than 40 hours of work T – Testable – User Stories should have acceptance criteria to test whether they fulfill the desired requirements or not
  9. THREE-POINT ESTIMATE Three-point estimate(E) looks at the below 3 values 1. Most Optimistic Estimate – O 2. Most Likely Estimate – M 3. Least Likely Estimate - L It follows triangular distribution and is based on the formula E=(O+M+L)/3 PERT (Project Evaluation Review Technique) It is used to identify the time it takes to finish a particular activity or task. It helps in the proper scheduling and coordination of all tasks throughout a project. It also helps in keeping track of the progress or lack of the overall project. There are four definitions of time used in PERT: 1. Optimistic time – The least amount of time it takes to complete a task. 2. Pessimistic time – The maximum amount of time taken to complete a task. 3. Most likely time –The most reasonable estimate of time it should take to complete a task. 4. Expected time – The best estimate of time required to complete a task.
  10. Hick's law subdivide the total collection of choices into categories, eliminating about half of the remaining choices at each step, rather than considering each and every choice one-by-one. It empowers project managers to subdivide the total collection of choices into categories while evaluating reaction time rather than considering each and every choice one-by-one.
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