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What is Artificial Intelligence?
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Artificial Intelligence (GEN AI) generate multiple type contents like Text, Images, Audio, Video & synthetic data. Generative AI refers to a category of artificial intelligence algorithms designed to generate new content, such as text, images, music, or even code, that resembles human-created content.
There are multiple GEN AI examples as.
Generative AI is transforming industries by automating creative processes, enhancing productivity, and enabling new forms of expression. However, it also raises ethical concerns, such as the potential for misuse in creating fake content or infringing on intellectual property rights.
AI acts like human intelligence machines that are programmed as thinking and learning like humans
AI is the broadest concept, encompasses any technique that enables machines to mimic human intelligence. This includes reasoning, problem-solving, understanding natural language, perception, and more.
AI is a collection of two words: “Artificial” & “Intelligence”
Artificial = That is created by humans or non-natural things
“Intelligence” = The ability to understand or think accordingly.
What is GEN(Generative):
GEN(Generative) AI can generate multiple type contents like Text, Images, Audio, Video & synthetic data
In project management, resources and time are two of the most critical constraints that influence how a project is planned and executed. The relationship between these constraints can vary depending on the project’s priorities. Let’s break down the two scenarios you mentioned:
Aspect | Resource is a Constraint, Time is Not | Resource is Not a Constraint, Time is |
---|---|---|
Primary Focus | Efficient resource utilization | Meeting the deadline |
Resource Availability | Limited | Plentiful |
Time Flexibility | Flexible | Fixed |
Cost Implications | Lower cost, but longer duration | Higher cost, but shorter duration |
Project Management Techniques | Resource leveling, prioritization | Project crashing, fast-tracking |
Understanding these constraints helps project managers make informed decisions about resource allocation, scheduling, and risk management to achieve project goals effectively.
Project Crashing is a project management technique used to shorten the project duration by adding additional resources to critical path tasks. It is typically employed when a project is behind schedule or when there is a need to meet an earlier deadline. However, crashing often comes at an increased cost, as it involves allocating more resources (e.g., labor, equipment, or materials) to complete tasks faster.
Suppose a project has the following tasks on the critical path:
Task | Normal Time (Days) | Crash Time (Days) | Normal Cost ($) | Crash Cost ($) |
---|---|---|---|---|
A | 5 | 3 | 500 | 800 |
B | 7 | 5 | 1000 | 1400 |
C | 4 | 2 | 600 | 1000 |
2. Choose the Most Cost-Effective Task:
3. Crash Task A:
4. Update Project Duration:
By carefully analyzing the trade-offs between time and cost, project managers can use crashing to effectively manage project timelines while minimizing negative impacts.
In project management, estimation is a critical process for predicting the time, cost, resources, and effort required to complete a project. Different estimation techniques are used depending on the project’s complexity, available data, and the stage of the project lifecycle. Below are the key estimation techniques used in project management:
By selecting the appropriate estimation technique(s), project managers can improve the accuracy of their estimates and set realistic expectations for stakeholders.
Project scheduling is a critical aspect of project management that involves planning, organizing, and managing tasks and resources to ensure the project is completed on time. Below is a step-by-step explanation of how to create and manage a project schedule:
The Multidimensional data cube is a multi-dimensional array of data used for OLAP (Online Analytical Processing)
A Multidimensional data cube allows data to be viewed in multiple dimensions.
Roll-up is an aggregation operation that summarizes data by climbing up a concept hierarchy or by dimension reduction. It’s like zooming out to see a broader view.
Example: Sales data cube with dimensions: Location (City), Time (Month), and Product.
A roll-up operation might aggregate sales data from the city level to the country level.
Below Data Before Roll-Up:
Below Data After Roll-Up:
Drill-down is the reverse of roll-up. It provides more detailed data by descending a concept hierarchy or adding dimensions. It’s like zooming in to see finer details.
Example: Using the same sales data cube, a drill-down operation might break down sales data from the country level to the city level.
Below Data Before Drill-Down:
Below Data After Drill-Down:
Slice selects a single dimension from the data cube, creating a sub-cube by fixing a value for one dimension.
Selection on one dimension of the given cube, resulting in a sub cube.
Example: If we want to analyze sales data for January only, we perform a slice operation on the Time dimension.
Below Data Before Slice:
Below Data After Slice:
Dice selects two or more dimensions to create a sub-cube by fixing values for those dimensions.
Selection on two or more dimension of the given cube, resulting in a sub cube.
Example: If we want to analyze sales data for January and February in New York and Los Angeles, we perform a dice operation.
Below Data Before Dice:
Below Data After Dice:
A Multidimensional Data Model:: It is defined as a Data Model that allows data to be organized and viewed in multiple dimensions, such as time, item, branch, and location, enabling organizations to analyze relationships between different perspectives and entities efficiently.
A multidimensional data model views data in the form of a data cube, which allows data to be modeled and viewed in multiple dimensions. The key components are:
Data Cube:: It is a multi-dimensional data structure. A data cube is organized by its dimensions (as Products, States, Date)
A data cube allows data to be viewed in multiple dimensions.
A Retail store that wants to analyze its sales data. The dimensions could be:
The following things need to be followed
Ensure your data is structured correctly.
Step [2] –
[2.1] – Select the Data: Highlight the entire dataset (including headers).
[2.2] – Go to “Insert” Tab: Click on the “Insert” tab in Excel.
[2.3] – Choose Pivot Table:
[2.4]- Select Pivot Table Location:
Note:: For best result always try to select New Worksheet
[2.5] – Click “OK.”
Step [3] – Build the Pivot Table
Filters (Optional): Add a field to the Filters area to filter data dynamically.
A blank Pivot Table field list appears.
Drag and Drop Fields:
Rows: Drag a categorical field (e.g., Physical store, Country Name) into the “Rows” area.
Values: Drag a numerical field (e.g., List Price, Actual Price) into the “Values” area.
Columns (Optional): Drag another field (e.g., Date) to see data across columns.
Step [4] – Physical Store in Rows & List Price in Values, by default List Price display sum of list price corresponding to physical store.
[4.1] – Grand Total = Sum of all Physical Store List Price
[4.2] – Right Click any of (rows) Numeric value of (Sum of List Price)–>display
[4.3] – Summarize Values By
[a] – If Select Summarize Values By –> Count , result as
[b] – If Select Summarize Values By –> Average , result as
Once Select — Summarize Values By –> Average
[c] – If Select Summarize Values By –> Max , result as
Once Select — Summarize Values By –> Max
[d] – If Select Summarize Values By –> Min , result as
Once Select — Summarize Values By –> Min
[4.4] – Show Values As
–Once Select — Show Values As –> % of Grand Total
[4.5] – Sort->Smallest To Largest or Sort->Largest To Smallest
[a] Sort->Smallest To Largest result
[b] Sort->Largest To Smallest result