Creating an estimate for a construction project over three years, encompassing hardware costs, software licensing, and personnel expenses for setup and support, requires a detailed and organized approach. Here’s an outline that can guide you through this process:
Estimate Outline for Construction Project
I. Executive Summary
Brief overview of the project
Total estimated cost
Key highlights of the estimate
II. Project Overview
Description of the construction project
Scope and objectives
Duration of the project (3 years)
Key deliverables
III. Hardware Costs
Initial Setup Costs
List of required hardware (computers, servers, networking equipment, etc.)
Unit cost of each hardware item
Total cost for initial setup
Maintenance and Upgrades
Estimated maintenance costs over 3 years
Potential upgrade costs and their triggers
IV. Software Licensing Costs
Initial Licensing Fees
List of required software (CAD, project management tools, etc.)
Cost of licenses (one-time or recurring)
Total initial licensing fees
Ongoing Licensing Fees
Annual or monthly fees over 3 years
Potential cost increases or additional licenses
V. Personnel Costs
Setup Phase
Roles required for setup (IT professionals, software specialists, etc.)
Hourly rates or salaries
Total personnel cost for the setup phase
Operational Support
Ongoing support roles (technical support, software maintenance)
Estimated hours of support per week/month
Cost projections for 3 years
VI. Training Costs
Training programs for software or hardware
Cost per training session or package
Total training cost estimate
VII. Contingency Costs
Percentage of total costs to cover unforeseen expenses
Justification for the selected percentage
VIII. Summary of Costs
Table summarizing all costs:
Hardware Costs
Software Licensing Costs
Personnel Costs
Training Costs
Contingency Costs
Total Estimated Cost
IX. Assumptions and Limitations
Assumptions made during estimation
Limitations or constraints of the estimate
X. Approval and Next Steps
Procedure for approving the estimate
Next steps upon approval
XI. Appendices
Detailed quotes from vendors
Resumes or qualifications of key personnel
Any other relevant documentation
Notes:
Accuracy: Regularly update the estimate as more detailed information becomes available.
Flexibility: Be prepared to adjust the estimate for changes in scope, unexpected delays, or changes in market prices.
Documentation: Keep detailed records of how each cost was estimated for transparency and future reference.
Review: Have the estimate reviewed by key stakeholders and financial experts.
This comprehensive estimate outline will help in effectively presenting and managing the financial aspects of your construction project over its three-year duration.
I think PEP08 is the standard I want to use. Link: PEP08 Guide
Naming Conventions
The naming conventions of Python’s library are a bit of a mess, so we’ll never get this completely consistent – nevertheless, here are the currently recommended naming standards. New modules and packages (including third party frameworks) should be written to these standards, but where an existing library has a different style, internal consistency is preferred. Overriding Principle
Names that are visible to the user as public parts of the API should follow conventions that reflect usage rather than implementation. Descriptive: Naming Styles
There are a lot of different naming styles. It helps to be able to recognize what naming style is being used, independently from what they are used for.
The following naming styles are commonly distinguished:
b (single lowercase letter)
B (single uppercase letter)
lowercase
lower_case_with_underscores
UPPERCASE
UPPER_CASE_WITH_UNDERSCORES
CapitalizedWords (or CapWords, or CamelCase – so named because of the bumpy look of its letters [4]). This is also sometimes known as StudlyCaps.
Note: When using acronyms in CapWords, capitalize all the letters of the acronym. Thus HTTPServerError is better than HttpServerError.
mixedCase (differs from CapitalizedWords by initial lowercase character!)
Capitalized_Words_With_Underscores (ugly!)
There’s also the style of using a short unique prefix to group related names together. This is not used much in Python, but it is mentioned for completeness. For example, the os.stat() function returns a tuple whose items traditionally have names like st_mode, st_size, st_mtime and so on. (This is done to emphasize the correspondence with the fields of the POSIX system call struct, which helps programmers familiar with that.)
The X11 library uses a leading X for all its public functions. In Python, this style is generally deemed unnecessary because attribute and method names are prefixed with an object, and function names are prefixed with a module name.
In addition, the following special forms using leading or trailing underscores are recognized (these can generally be combined with any case convention):
_single_leading_underscore: weak “internal use” indicator. E.g. from M import * does not import objects whose names start with an underscore.
single_trailing_underscore_: used by convention to avoid conflicts with Python keyword, e.g.
tkinter.Toplevel(master, class_='ClassName')
__double_leading_underscore: when naming a class attribute, invokes name mangling (inside class FooBar, __boo becomes _FooBar__boo; see below).
__double_leading_and_trailing_underscore__: “magic” objects or attributes that live in user-controlled namespaces. E.g. __init__, __import__ or __file__. Never invent such names; only use them as documented.
Prescriptive: Naming Conventions
Names to Avoid
Never use the characters ‘l’ (lowercase letter el), ‘O’ (uppercase letter oh), or ‘I’ (uppercase letter eye) as single character variable names.
In some fonts, these characters are indistinguishable from the numerals one and zero. When tempted to use ‘l’, use ‘L’ instead. ASCII Compatibility
Identifiers used in the standard library must be ASCII compatible as described in the policy section of PEP 3131.
Constants
1 2 3
- USERNAME = - PASSWORD = - Uses all caps to denote that the value is not to be changed or modified
Variables
1 2 3
- Names of type variables introduced in PEP 484 should normally use CapWords preferring short names: T, AnyStr, Num. It is recommended to add suffixes \_co or \_contra to the variables used to declare covariant or contravariant behavior correspondingly:
- Uses all caps to denote that the value is not to be changed or modified
Lists
1
- Uses all caps to denote that the value is not to be changed or modified
Dictionaries
1
- Uses all caps to denote that the value is not to be changed or modified
Exception Names
1
- Because exceptions should be classes, the class naming convention applies here. However, you should use the suffix “Error” on your exception names (if the exception actually is an error).
(Let’s hope that these variables are meant for use inside one module only.) The conventions are about the same as those for functions.
Modules that are designed for use via from M import \* should use the **all** mechanism to prevent exporting globals, or use the older convention of prefixing such globals with an underscore (which you might want to do to indicate these globals are “module non-public”). Function and Variable Names Function names should be lowercase, with words separated by underscores as necessary to improve readability. Variable names follow the same convention as function names. mixedCase is allowed only in contexts where that’s already the prevailing style (e.g. threading.py), to retain backwards compatibility. Function and Method Arguments Always use self for the first argument to instance methods. Always use cls for the first argument to class methods. If a function argument’s name clashes with a reserved keyword, it is generally better to append a single trailing underscore rather than use an abbreviation or spelling corruption. Thus class\_ is better than clss. (Perhaps better is to avoid such clashes by using a synonym.) Method Names and Instance Variables Use the function naming rules: lowercase with words separated by underscores as necessary to improve readability. Use one leading underscore only for non-public methods and instance variables. To avoid name clashes with subclasses, use two leading underscores to invoke Python’s name mangling rules. Python mangles these names with the class name: if class Foo has an attribute named **a, it cannot be accessed by Foo.**a. (An insistent user could still gain access by calling Foo.\_Foo\_\_a.) Generally, double leading underscores should be used only to avoid name conflicts with attributes in classes designed to be subclassed. Note: there is some controversy about the use of \_\_names (see below). Constants Constants are usually defined on a module level and written in all capital letters with underscores separating words. Examples include MAX_OVERFLOW and TOTAL. Designing for Inheritance Always decide whether a class’s methods and instance variables (collectively: “attributes”) should be public or non-public. If in doubt, choose non-public; it’s easier to make it public later than to make a public attribute non-public. Public attributes are those that you expect unrelated clients of your class to use, with your commitment to avoid backwards incompatible changes. Non-public attributes are those that are not intended to be used by third parties; you make no guarantees that non-public attributes won’t change or even be removed. We don’t use the term “private” here, since no attribute is really private in Python (without a generally unnecessary amount of work). Another category of attributes are those that are part of the “subclass API” (often called “protected” in other languages). Some classes are designed to be inherited from, either to extend or modify aspects of the class’s behavior. When designing such a class, take care to make explicit decisions about which attributes are public, which are part of the subclass API, and which are truly only to be used by your base class. With this in mind, here are the Pythonic guidelines: Public attributes should have no leading underscores. If your public attribute name collides with a reserved keyword, append a single trailing underscore to your attribute name. This is preferable to an abbreviation or corrupted spelling. (However, notwithstanding this rule, ‘cls’ is the preferred spelling for any variable or argument which is known to be a class, especially the first argument to a class method.) Note 1: See the argument name recommendation above for class methods. For simple public data attributes, it is best to expose just the attribute name, without complicated accessor/mutator methods. Keep in mind that Python provides an easy path to future enhancement, should you find that a simple data attribute needs to grow functional behavior. In that case, use properties to hide functional implementation behind simple data attribute access syntax. Note 1: Try to keep the functional behavior side-effect free, although side-effects such as caching are generally fine. Note 2: Avoid using properties for computationally expensive operations; the attribute notation makes the caller believe that access is (relatively) cheap. If your class is intended to be subclassed, and you have attributes that you do not want subclasses to use, consider naming them with double leading underscores and no trailing underscores. This invokes Python’s name mangling algorithm, where the name of the class is mangled into the attribute name. This helps avoid attribute name collisions should subclasses inadvertently contain attributes with the same name. Note 1: Note that only the simple class name is used in the mangled name, so if a subclass chooses both the same class name and attribute name, you can still get name collisions. Note 2: Name mangling can make certain uses, such as debugging and __getattr__(), less convenient. However the name mangling algorithm is well documented and easy to perform manually. Note 3: Not everyone likes name mangling. Try to balance the need to avoid accidental name clashes with potential use by advanced callers.
Any backwards compatibility guarantees apply only to public interfaces. Accordingly, it is important that users be able to clearly distinguish between public and internal interfaces.
Documented interfaces are considered public, unless the documentation explicitly declares them to be provisional or internal interfaces exempt from the usual backwards compatibility guarantees. All undocumented interfaces should be assumed to be internal.
To better support introspection, modules should explicitly declare the names in their public API using the **all** attribute. Setting **all** to an empty list indicates that the module has no public API.
Even with **all** set appropriately, internal interfaces (packages, modules, classes, functions, attributes or other names) should still be prefixed with a single leading underscore.
An interface is also considered internal if any containing namespace (package, module or class) is considered internal.
Imported names should always be considered an implementation detail. Other modules must not rely on indirect access to such imported names unless they are an explicitly documented part of the containing module’s API, such as os.path or a package’s **init** module that exposes functionality from submodules. Programming Recommendations
Code should be written in a way that does not disadvantage other implementations of Python (PyPy, Jython, IronPython, Cython, Psyco, and such). For example, do not rely on CPython’s efficient implementation of in-place string concatenation for statements in the form a += b or a = a + b. This optimization is fragile even in CPython (it only works for some types) and isn’t present at all in implementations that don’t use refcounting. In performance sensitive parts of the library, the ''.join() form should be used instead. This will ensure that concatenation occurs in linear time across various implementations. Comparisons to singletons like None should always be done with is or is not, never the equality operators. Also, beware of writing if x when you really mean if x is not None – e.g. when testing whether a variable or argument that defaults to None was set to some other value. The other value might have a type (such as a container) that could be false in a boolean context! Use is not operator rather than not ... is. While both expressions are functionally identical, the former is more readable and preferred: # Correct: if foo is not None: # Wrong: if not foo is None: When implementing ordering operations with rich comparisons, it is best to implement all six operations (__eq__, __ne__, __lt__, __le__, __gt__, __ge__) rather than relying on other code to only exercise a particular comparison. To minimize the effort involved, the functools.total_ordering() decorator provides a tool to generate missing comparison methods. PEP 207 indicates that reflexivity rules are assumed by Python. Thus, the interpreter may swap y > x with x < y, y >= x with x <= y, and may swap the arguments of x == y and x != y. The sort() and min() operations are guaranteed to use the < operator and the max() function uses the > operator. However, it is best to implement all six operations so that confusion doesn’t arise in other contexts. Always use a def statement instead of an assignment statement that binds a lambda expression directly to an identifier: # Correct: def f(x): return 2*x # Wrong: f = lambda x: 2*x The first form means that the name of the resulting function object is specifically ‘f’ instead of the generic ‘<lambda>’. This is more useful for tracebacks and string representations in general. The use of the assignment statement eliminates the sole benefit a lambda expression can offer over an explicit def statement (i.e. that it can be embedded inside a larger expression) Derive exceptions from Exception rather than BaseException. Direct inheritance from BaseException is reserved for exceptions where catching them is almost always the wrong thing to do. Design exception hierarchies based on the distinctions that code catching the exceptions is likely to need, rather than the locations where the exceptions are raised. Aim to answer the question “What went wrong?” programmatically, rather than only stating that “A problem occurred” (see PEP 3151 for an example of this lesson being learned for the builtin exception hierarchy) Class naming conventions apply here, although you should add the suffix “Error” to your exception classes if the exception is an error. Non-error exceptions that are used for non-local flow control or other forms of signaling need no special suffix. Use exception chaining appropriately. raise X from Y should be used to indicate explicit replacement without losing the original traceback. When deliberately replacing an inner exception (using raise X from None), ensure that relevant details are transferred to the new exception (such as preserving the attribute name when converting KeyError to AttributeError, or embedding the text of the original exception in the new exception message). When catching exceptions, mention specific exceptions whenever possible instead of using a bare except: clause: try: import platform_specific_module except ImportError: platform_specific_module = None A bare except: clause will catch SystemExit and KeyboardInterrupt exceptions, making it harder to interrupt a program with Control-C, and can disguise other problems. If you want to catch all exceptions that signal program errors, use except Exception: (bare except is equivalent to except BaseException:). A good rule of thumb is to limit use of bare ‘except’ clauses to two cases: If the exception handler will be printing out or logging the traceback; at least the user will be aware that an error has occurred. If the code needs to do some cleanup work, but then lets the exception propagate upwards with raise. try...finally can be a better way to handle this case. When catching operating system errors, prefer the explicit exception hierarchy introduced in Python 3.3 over introspection of errno values. Additionally, for all try/except clauses, limit the try clause to the absolute minimum amount of code necessary. Again, this avoids masking bugs: # Correct: try: value = collection[key] except KeyError: return key_not_found(key) else: return handle_value(value) # Wrong: try: # Too broad! return handle_value(collection[key]) except KeyError: # Will also catch KeyError raised by handle_value() return key_not_found(key) When a resource is local to a particular section of code, use a with statement to ensure it is cleaned up promptly and reliably after use. A try/finally statement is also acceptable. Context managers should be invoked through separate functions or methods whenever they do something other than acquire and release resources: # Correct: with conn.begin_transaction(): do_stuff_in_transaction(conn) # Wrong: with conn: do_stuff_in_transaction(conn) The latter example doesn’t provide any information to indicate that the __enter__ and __exit__ methods are doing something other than closing the connection after a transaction. Being explicit is important in this case. Be consistent in return statements. Either all return statements in a function should return an expression, or none of them should. If any return statement returns an expression, any return statements where no value is returned should explicitly state this as return None, and an explicit return statement should be present at the end of the function (if reachable): # Correct: def foo(x): if x >= 0: return math.sqrt(x) else: return None def bar(x): if x < 0: return None return math.sqrt(x) # Wrong: def foo(x): if x >= 0: return math.sqrt(x) def bar(x): if x < 0: return return math.sqrt(x) Use ''.startswith() and ''.endswith() instead of string slicing to check for prefixes or suffixes. startswith() and endswith() are cleaner and less error prone: # Correct: if foo.startswith('bar'): # Wrong: if foo[:3] == 'bar': Object type comparisons should always use isinstance() instead of comparing types directly: # Correct: if isinstance(obj, int): # Wrong: if type(obj) is type(1): For sequences, (strings, lists, tuples), use the fact that empty sequences are false: # Correct: if not seq: if seq: # Wrong: if len(seq): if not len(seq): Don’t write string literals that rely on significant trailing whitespace. Such trailing whitespace is visually indistinguishable and some editors (or more recently, reindent.py) will trim them. Don’t compare boolean values to True or False using ==: # Correct: if greeting: # Wrong: if greeting == True: Worse: # Wrong: if greeting is True: Use of the flow control statements return/break/continue within the finally suite of a try...finally, where the flow control statement would jump outside the finally suite, is discouraged. This is because such statements will implicitly cancel any active exception that is propagating through the finally suite: # Wrong: def foo(): try: 1 / 0 finally: return 42
Function Annotations
1 2 3 4 5 6 7 8 9 10 11 12 13
With the acceptance of PEP 484, the style rules for function annotations have changed.
Function annotations should use PEP 484 syntax (there are some formatting recommendations for annotations in the previous section). The experimentation with annotation styles that was recommended previously in this PEP is no longer encouraged. However, outside the stdlib, experiments within the rules of PEP 484 are now encouraged. For example, marking up a large third party library or application with PEP 484 style type annotations, reviewing how easy it was to add those annotations, and observing whether their presence increases code understandability. The Python standard library should be conservative in adopting such annotations, but their use is allowed for new code and for big refactorings. For code that wants to make a different use of function annotations it is recommended to put a comment of the form: # type: ignore near the top of the file; this tells type checkers to ignore all annotations. (More fine-grained ways of disabling complaints from type checkers can be found in PEP 484.) Like linters, type checkers are optional, separate tools. Python interpreters by default should not issue any messages due to type checking and should not alter their behavior based on annotations. Users who don’t want to use type checkers are free to ignore them. However, it is expected that users of third party library packages may want to run type checkers over those packages. For this purpose PEP 484 recommends the use of stub files: .pyi files that are read by the type checker in preference of the corresponding .py files. Stub files can be distributed with a library, or separately (with the library author’s permission) through the typeshed repo [5].
PEP 526 introduced variable annotations. The style recommendations for them are similar to those on function annotations described above:
Annotations for module level variables, class and instance variables, and local variables should have a single space after the colon. There should be no space before the colon. If an assignment has a right hand side, then the equality sign should have exactly one space on both sides: # Correct: code: int class Point: coords: Tuple[int, int] label: str = '<unknown>' # Wrong: code:int # No space after colon code : int # Space before colon class Test: result: int=0 # No spaces around equality sign Although the PEP 526 is accepted for Python 3.6, the variable annotation syntax is the preferred syntax for stub files on all versions of Python (see PEP 484 for details).
Creating a portfolio website to display your 3D promotional materials for construction projects is a great way to showcase your skills and attract potential clients. Here’s a plan you can follow to create an effective portfolio:
1. Define Your Objectives:
Purpose: Showcase 3D construction project materials and your skills.
Target Audience: Construction firms, architects, real estate developers, and independent contractors.
Outcome: Generate inquiries and freelance job opportunities.
2. Gather Your Content:
3D Renders: High-resolution images and videos of your best work.
Descriptions: Brief explanations of each project, your role, tools used, and any unique challenges or solutions.
Testimonials: If available, include client testimonials to provide social proof of your skills.
Resume/CV: Outline your professional experience, skills, and any relevant education or certifications.
Contact Information: How clients can reach you, including email, phone number, and professional social media profiles or platforms where you’re active.
Services Offered: Detail the services you provide, such as 3D visualization, virtual walkthroughs, animation, etc.
3. Choose a Website Platform:
Custom Build: Hire a web developer or use your own skills if you’re proficient in web design.
Website Builders: Platforms like Squarespace, Wix, or WordPress with portfolio themes can be a cost-effective and user-friendly option.
4. Design Your Site:
Homepage: Create a visually impactful homepage with a carousel or grid of your featured work.
Portfolio Pages: Organize your 3D materials into categories if you have multiple services or project types.
About Page: Share your story, experience, and what sets you apart.
Contact Page: A form for inquiries as well as your direct contact information.
Responsive Design: Ensure your website is mobile-friendly for users on different devices.
SEO: Optimize your site for search engines to increase visibility.
5. Build the Website:
Homepage: Construct with a focus on your unique selling proposition and a showcase of your latest or most impressive projects.
Portfolio Gallery: Create a user-friendly gallery with options to view projects in detail.
Project Pages: Each project should have its own page with a gallery and the story behind the project.
Blog/Insights: Consider having a blog to share your insights, new trends in 3D visualization, and behind-the-scenes of your projects.
Call-to-Action (CTA): Encourage visitors to contact you for their projects on every page.
6. Optimize User Experience (UX):
Navigation: Make sure your website is easy to navigate with a clear menu.
Loading Times: Optimize image and video sizes to ensure quick loading times.
Interactivity: Include interactive elements like a virtual tour if applicable.
7. Test Your Website:
Cross-Browser Compatibility: Ensure it works across various web browsers.
Mobile Responsiveness: Test on different mobile devices.
Load Testing: Verify that your site can handle traffic without slowing down.
8. Launch the Website:
Soft Launch: Share your site with a small group for feedback.
Revise: Make necessary adjustments based on the feedback.
Official Launch: Announce the launch through your network, social media, and relevant online communities.
9. Marketing and Promotion:
Social Media: Use platforms like LinkedIn, Instagram, and Pinterest to promote your site.
Networking: Attend industry events and join online forums.
Content Marketing: Write articles or create videos that showcase your expertise and can help drive traffic to your site.
10. Maintain and Update:
Regular Updates: Keep your portfolio fresh with new projects and updates.
Analytics: Use tools like Google Analytics to track visitor behavior and make informed improvements.
Security: Regularly update your website’s security features to protect your work and your clients’ information.
11. Legalities:
Copyrights and Permissions: Ensure you have the rights to display all the content on your website.
Privacy Policy: If you collect user data, make sure you have a privacy policy in place.
Terms of Service: Clearly state the terms under which you provide your services.
By following this plan, you’ll be able to create a professional and compelling portfolio website that not only showcases your 3D promotional materials for construction projects but also effectively markets your skills to potential clients.
Creating a portfolio website to display your 3D promotional materials for construction projects is a great way to showcase your skills and attract potential clients. Here’s a plan you can follow to create an effective portfolio:
1. Define Your Objectives:
Purpose: Showcase 3D construction project materials and your skills.
Target Audience: Construction firms, architects, real estate developers, and independent contractors.
Outcome: Generate inquiries and freelance job opportunities.
2. Gather Your Content:
3D Renders: High-resolution images and videos of your best work.
Descriptions: Brief explanations of each project, your role, tools used, and any unique challenges or solutions.
Testimonials: If available, include client testimonials to provide social proof of your skills.
Resume/CV: Outline your professional experience, skills, and any relevant education or certifications.
Contact Information: How clients can reach you, including email, phone number, and professional social media profiles or platforms where you’re active.
Services Offered: Detail the services you provide, such as 3D visualization, virtual walkthroughs, animation, etc.
3. Choose a Website Platform:
Custom Build: Hire a web developer or use your own skills if you’re proficient in web design.
Website Builders: Platforms like Squarespace, Wix, or WordPress with portfolio themes can be a cost-effective and user-friendly option.
4. Design Your Site:
Homepage: Create a visually impactful homepage with a carousel or grid of your featured work.
Portfolio Pages: Organize your 3D materials into categories if you have multiple services or project types.
About Page: Share your story, experience, and what sets you apart.
Contact Page: A form for inquiries as well as your direct contact information.
Responsive Design: Ensure your website is mobile-friendly for users on different devices.
SEO: Optimize your site for search engines to increase visibility.
5. Build the Website:
Homepage: Construct with a focus on your unique selling proposition and a showcase of your latest or most impressive projects.
Portfolio Gallery: Create a user-friendly gallery with options to view projects in detail.
Project Pages: Each project should have its own page with a gallery and the story behind the project.
Blog/Insights: Consider having a blog to share your insights, new trends in 3D visualization, and behind-the-scenes of your projects.
Call-to-Action (CTA): Encourage visitors to contact you for their projects on every page.
6. Optimize User Experience (UX):
Navigation: Make sure your website is easy to navigate with a clear menu.
Loading Times: Optimize image and video sizes to ensure quick loading times.
Interactivity: Include interactive elements like a virtual tour if applicable.
7. Test Your Website:
Cross-Browser Compatibility: Ensure it works across various web browsers.
Mobile Responsiveness: Test on different mobile devices.
Load Testing: Verify that your site can handle traffic without slowing down.
8. Launch the Website:
Soft Launch: Share your site with a small group for feedback.
Revise: Make necessary adjustments based on the feedback.
Official Launch: Announce the launch through your network, social media, and relevant online communities.
9. Marketing and Promotion:
Social Media: Use platforms like LinkedIn, Instagram, and Pinterest to promote your site.
Networking: Attend industry events and join online forums.
Content Marketing: Write articles or create videos that showcase your expertise and can help drive traffic to your site.
10. Maintain and Update:
Regular Updates: Keep your portfolio fresh with new projects and updates.
Analytics: Use tools like Google Analytics to track visitor behavior and make informed improvements.
Security: Regularly update your website’s security features to protect your work and your clients’ information.
11. Legalities:
Copyrights and Permissions: Ensure you have the rights to display all the content on your website.
Privacy Policy: If you collect user data, make sure you have a privacy policy in place.
Terms of Service: Clearly state the terms under which you provide your services.
By following this plan, you’ll be able to create a professional and compelling portfolio website that not only showcases your 3D promotional materials for construction projects but also effectively markets your skills to potential clients.
[Complete Data Wrangling & Data Visualisation With Python](“file:\M:_RESOURCES\LEARNING_PYTHON_DATA_SCIENCE\Complete Data Wrangling & Data Visualisation With Python”)
Teacher
Colt
About this Course Learn how to make better decisions with data in this course on data analysis. We’ll start by looking at what data analysis is, and then we’ll see how we can use data analysis to create better outcomes.
Notes
General
Python2 vs Python3 - Python3 was a major overhaul and not all functions etc will be backward compatible. Python2 will be retired eventually, no longer maintained
Boolean ‘1’ is True, has truthiness, is not empty; while ‘0’ if False, has falsiness, is emptly
is vs == ; is false when not in same location in memory, even if value is equal
Seaborn - matplotlib backend, makes statistical plots
Pandas - matplotlib backend, uses .plot() calls to make static plots
Plotly - both a company and open source lib, for JS, React, R, Python. Creates interactive plats as .html files connected to static data sources.
Dash - allows for plots to interact with each other, components, or update in real time. Dash is open source lib to create full dashboard with multiple components, interactivity and multiple plots.
NumPy
In this course, Python manupulation of lists, arrays, matrices
Pandas
In this course, Pandas to read in datasets and select rows or columns of data
Markdown is a text-to-HTML conversion tool for web writers. Markdown allows you to write using an easy-to-read, easy-to-write plain text format, then convert it to structurally valid XHTML (or HTML).
Here is a heading: # Heading, don’t do this:#Heading
Emphasis
Emphasis, aka italics, with _asterisks_ or _underscores_.
Strong emphasis, aka bold, with **asterisks** or **underscores**.
Combined emphasis with **asterisks and _underscores_**.
Strikethrough uses two tildes. ~~Scratch this.~~
Line Breaks
1 2
First line with two spaces after. And the next line.
Lists
Ordered Lists
1 2 3
1. First item 2. Second item 3. Third item
Unordered Lists
1 2 3
- First item - Second item - Third item
Links
1
Link with text: [link-text](https://www.google.com)
Images
1 2 3
Image with alt text: 
Image without alt text: 
Code Blocks
Inline Code Block
1
Inline `code` has `back-ticks around` it.
Blocks of Code
1 2
var s = "JavaScript syntax highlighting"; alert(s);
1 2
s = "Python syntax highlighting" print s
1 2
No language indicated, so no syntax highlighting. But let's throw in a <b>tag</b>.
Tables
There must be at least 3 dashes separating each header cell. The outer pipes (|) are optional, and you don’t need to make the raw Markdown line up prettily.
To create a taksk lsit start line with square brackets with an empty space. Ex: [ ] and add text for task. To check the task replace the space between the bracket with “x”.
1 2 3
[x] Write the post [ ] Update the website [ ] Contact the user
About this Course Learn how to make better decisions with data in this course on data analysis. We’ll start by looking at what data analysis is, and then we’ll see how we can use data analysis to create better outcomes.
Notes
General
Python2 vs Python3 - Python3 was a major overhaul and not all functions etc will be backward compatible. Python2 will be retired eventually, no longer maintained
Boolean ‘1’ is True, has truthiness, is not empty; while ‘0’ if False, has falsiness, is emptly
is vs == ; is false when not in same location in memory, even if value is equal
Data Types
Lists
List functions(.append-adds 1 item, .extend-adds multiple items)(.insert(2,”purple”- inserts purple in seat 2))
Defined: thislist = ["apple", "banana", "cherry"]
Retrieved: listitem1 = thislist[0]
.pop - pop() method removes the element at the specified position.
Slices - List[start:stop:step]
List Comprehension is used whe iterating over lists, strings, ranges and more data types
nested listat are essential for building more complex data structures like matrices, board games, mazes
swapping is useful when shuffling or sorting
Dictionaries
Get loaded by item - items = {"name": "Eric", "age": 47, "isCool": False} or using dict()
Iterator - an object that can bne iterated upon an returns data. One element at at time using next()
Iterable - Object which will return an Iterator when iter() is called on it.
Generator Functions: uses yield, can yield multiple times, when invoked returns a generator.
ie.sum([x for x in range(100)]) =
Decorator - ‘@’are higher order functions wrapping other functions to enhoance their behavior
Debugging
1 2 3 4 5 6 7 8 9 10
whileTrue: try: # this might be necessary pass# except: # ValueError as err and can print err pass# there was a blank else: pass# input(f"Please enter some info: ") finally: pass# this runs no matter what,so remove if not necessary break
Powershell
mkdir <new directory name> - creates directory
ls - list directory, same as dir
pwd - outputs current location
echo $null >> <new file name>.py - creates new .py file, THEN you still have to change the file to UDP-8
rm -r -fo <directory name> - deletes ENTIRE directory; -r(Recursive for all child direcories); -f(Force prevents verifications & warnings)
Course Progress
Course Introduction
WINDOWS Command Line Fundamentals
WINDOWS Python Setup
Numbers, Operators, and Comments
Variables and Strings
Boolean and Conditional Logic
Rock, Paper, Scissors
Looping in Python
Guessing Game
Lists
Lists Comprehensions
Dictionaries
Dictionary Exercises
Tuples and Sets
Functions Part I
Functions Exercises
Functions Part II
Lambdas and Built-In Functions
Debugging and Error Handling
Modules
OPTIONAL SECTION Making HTTP Requests with Python
Object Oriented Programming
Deck Of Cards Exercise
OOP Part 2 (did not complete 7-11)
Iterators & Generators
Decorators (did not complete 7-14)
Testing With Python (did not complete 3-11)
File IO (did not complete any)
Working With CSV and Pickling (did not complete any)
Web Scraping with BeautifulSoup
Web Scraping Project
Regular Expressions
Python + SQL
Massive Section of Challenges
TO DO:
What did I learn / what do I need to review
[x] Print statements and variable assigning [x] Variables and simple variable types(Numbers, String, List, Tuple, Dictionary) [x] Complex variables(Long, Float, List, Tuple, Dictionary) [x] Input and Ouput [x] Loops(For, While) [x] Conditional if statements [x] Lists [x] Functions and Methods(for lists) [ ] Review “Comprehensions”, they seem useful and I don’t totally understand [ ] Find Youtube on debugging in VScode [ ] Go back and finish the (Did on completes)
Ben is an Android teacher at Treehouse with a long history of creating and tinkering with Android apps. He’s an avid learner, loves playing sports, and is a fairly average chef.
About this Course
Learn how to make better decisions with data in this course on data analysis. We’ll start by looking at what data analysis is, and then we’ll see how we can use data analysis to create better outcomes.
What you’ll learn
Cleaning and preparing data in spreadsheets
Summarizing data with formula
Normal distributions and standard deviations
Simple visualizations in spreadsheets
Presenting findings
Introducing Data Analysis
From the daily weather forecast to the nightly sports results, data is all around us. Let’s take a look at how we can analyse data to draw conclusions and uncover hidden insights!
There’s a lot of ways we can look at our data. We can look at it as rows in columns in a spreadsheet, or we can try and represent it visually as a chart. We can even summarize our data into just a few numbers to help us make decision making less of a chore and more of a step-by-step process!
There are a lot of ways to perform an analysis of data. In this stage, we’ll talk about metrics and look at one way to work through the data analysis process.
Purpose: To analyze data on 3 MyCloud NASs for move to new UnRaid server
Requirements of this app: Build in Powershell Dialog box to ask which directory to analyze Return every file Name, Location, Size Export to log.csv Timestamp and rename the log file Open the folder once operation completed
