Forem: Ben Goulding

Your First Python Project

Ben Goulding — Tue, 02 Jan 2024 14:54:01 +0000

You've done it, you've mastered functions, variables, strings, conditional statements, indefinite loops & definite loops...but now what?

It's time you knock together your first Python project, a number guessing game.

A word of warning though, NASA - National Aeronautics and Space Administration may come knocking at the door after you upload this masterpiece to GitHub, the next Margaret Hamilton they'll think...you've been warned.

Here's the project brief;

"Listen, I like numbers, I like guessing and I like games...do you think you could make me a number guessing game? Nice one, thanks." - Ben Goulding

Say no more, let's go.

First things first, we're going to need a random number. Luckily for us Python can simulate a random number with the help of the suitably named module called...random. Let's get that imported;

import random

If you haven't come across modules before, here's a brief TL/DR;

"A Python module is a file containing Python code. This file typically consists of functions, classes, variables, and runnable code. Modules are used to organise and reuse code in Python programming." - Mr C.GPT

Job done, put the kettle on, we're nearly there.

For reasons that will become apparent shortly, our best bet is to create a function that will hold the vast majority of the game logic.

import random

def playthemgames():

Feel free to name the function how you like, for this demonstration I've gone for 'playthemgames'.

Now we need the outer 'while' loop, this will come in handy when we want to replay the game, for those times where you just cant get enough.

import random

def playthemgames():

    while True:
        random_number = random.randint(1, 100)

You'll notice that this is actually an infinite loop, with thanks to the 'True', it will never be 'False' hence the infinite.

Within this outer loop, we have also created the 'random' number, with the help of the 'random' module we imported at the start.

The 'random_number' variable will be assigned a simulated random number, I say it's simulated as it can never truly be random due to the deterministic nature that they are generated within Python. That's all for another time, but if you want to look into it Google something along the lines of "random number seeds in Python".

On the 'random' module we imported we can access the '.randint()' method. This method accepts two arguments, the first being the starting minimum integer and the second being the maximum integer value you want to generate to. These values are inclusive meaning that they can be generated as the random number.

Now we need to add the second nested 'while' loop, this one in particular will allow the player to keep guessing until the correct number has been found.

import random

def playthemgames():

    while True:
        random_number = random.randint(1, 100)

        while True:
            user_input = input("Please enter a number between 1-100: ")
            if user_input.lower() == "stop":
                return

You'll see that we are now asking for some user input;

user_input = input("Please enter a number between 1-100: ")

As you've probably guessed, this get's the user's guess or their command to stop the game.

if user_input.lower() == "stop":
    return

The 'if' conditional is here to check is the user has entered "stop". If "stop" was entered by the user it would be assigned to the 'user_input' variable where the conditional would match the equality '==' and exit the function 'return', effectively ending the game. The '.lower()' method on the 'user_input' variable is there to account for Pythons case sensitivity incase the user was to enter "sTOp", "Stop" or some other fruity case concoction.

Moving on...

import random

def playthemgames():
    while True:
        random_number = random.randint(1,100)

        while True:
            user_input = input("Please enter a number between 1-100: ")
            if user_input.lower() == "stop":
                return

            try:
                int_guess = int(user_input)
                if int_guess < random_number:
                    print("Too low!")

                elif int_guess > random_number:
                    print("Too high!")

                elif int_guess == random_number:
                    print("You got it right!")
                    break


            except ValueError:
                print("Invalid Entry - Please input a number.")

What you now see in the block above is the bulk of the game logic, I imagine this looks very similar in both style and size to Rockstar Games upcoming GTA IV source code.

I will briefly address the 'Try/Except' in case you haven't stumbled across this yet.

try:
    ...
except ValueError:
    print("Invalid Entry - Please input a number.")

Within the 'Try/Except' I've nested the logic that attempts to convert the user's input to an integer. If the conversion fails (the user enters something that's not a number), a 'ValueError' is caught, and a message is printed. It would be worthwhile looking into this in greater detail, have a gander here: https://docs.python.org/3/tutorial/errors.html

int_guess = int(user_input)
if int_guess < random_number:
    print("Too low!")
elif int_guess > random_number:
    print("Too high!")
elif int_guess == random_number:
    print("You got it right!")
    break

The user's guess is now compared with the random number. Feedback is provided if the guess is too low or too high. If the guess is correct, a message of your choosing is displayed, and the inner loop is broken. No prize money or leaderboard here though...yet.

Onwards and upwards!

import random

def playthemgames():
    while True:
        random_number = random.randint(1,100)

        while True:
            user_input = input("Please enter a number between 1-100: ")
            if user_input.lower() == "stop":
                return

            try:
                int_guess = int(user_input)
                if int_guess < random_number:
                    print("Too low!")

                elif int_guess > random_number:
                    print("Too high!")

                elif int_guess == random_number:
                    print("You got it right!")
                    break


            except ValueError:
                print("Invalid Entry - Please input a number.")

        print("Game Over")
        play_again = input("Play again? Y/N: ")
        if play_again.lower() != "y":
            break

print("Game Over")
play_again = input("Play again? Y/N: ")
if play_again.lower() != "y":
    break

After a round ends (either through a correct guess or the user typing "stop"), the game prints "Game Over". It then asks if the user wants to play again, if the user responds with anything other than 'y', the outer loop breaks, ending the game. Once again, notice the use of the '.lower()' method.

Nearly there...

import random

def playthemgames():
    while True:
        random_number = random.randint(1,100)

        while True:
            user_input = input("Please enter a number between 1-100: ")
            if user_input.lower() == "stop":
                return

            try:
                int_guess = int(user_input)
                if int_guess < random_number:
                    print("Too low!")

                elif int_guess > random_number:
                    print("Too high!")

                elif int_guess == random_number:
                    print("You got it right!")
                    break


            except ValueError:
                print("Invalid Entry - Please input a number.")

        print("Game Over")
        play_again = input("Play again? Y/N: ")
        if play_again.lower() != "y":
            break



start_game = input("Start Game? Y/N: ")
if start_game.lower() == "y":
    playthemgames()

As you would have noticed, everything that has been written up to now has been included within the 'playthemgames()' function.

We need to actually write the code that will start the game, this wont be within the function.

start_game = input("Start Game? Y/N: ")
if start_game.lower() == "y":
    playthemgames()
else:
    exit()

Initially, the script prompts the user to start the game. If they respond with 'y', the 'playthemgames' function is called, starting the game. If not, 'exit()' is called to terminate the script altogether.

And there we have it, your first Python project, short and sweet, much like myself.

In all seriousness, I hope you've found this somewhat useful and it's opened your mind to the beginnings of control flow and how to construct a simple script. There are many different ways this same result could have been achieved, that's the beauty of programming. At the end of the day and especially so if you are just starting out, if it get's the job done then the jobs a good'un.

If you would like further tutorials then please let me know, this is my first attempt and I'm more than happy to delve deeper into more complex topics or alternatively put together a basic Python tutorial that assumes no prior knowledge of programming.

Speak again soon!

The Feynman Technique: A How-to Guide for Lifelong Learners and Students

Ben Goulding — Sat, 03 Dec 2022 09:59:00 +0000

The Feynman Technique is an effective way for lifelong learners and students to learn and retain information. It was developed by Nobel Prize-winning physicist Richard Feynman as a way to make learning easier and more efficient. The technique involves breaking down a concept into its component parts, simplifying it, and teaching it to someone else. This guide will provide a step-by-step guide to using the Feynman Technique to learn and retain information.

Step 1: Choose a Concept
The first step in using the Feynman Technique is to choose a concept that you are trying to learn. Once you have identified the concept, break it down into smaller concepts that can be more easily understood. This will help you gain a better understanding of the overall concept and how it works.

Step 2: Explain the Concept to a Layman
The next step is to explain the concept to a non-expert. This can be done by using simple language and visuals to make the concept easier to understand. This will help you identify any gaps in your understanding of the concept and will also help you identify any misconceptions about the concept.

Step 3: Identify Gaps in Your Understanding
Once you have explained the concept to a non-expert, it is important to identify any gaps in your understanding of the concept. These gaps can then be filled in by researching the topic further or asking an expert for advice.

Step 4: Identify Misconceptions
It is also important to identify any misconceptions that you may have about the concept. Misconceptions can be corrected by researching the topic further or asking an expert for advice.

Step 5: Simplify the Concept
The next step is to simplify the concept. This can be done by using analogies, stories, and visuals to make the concept easier to understand. This will also make it easier for you to explain the concept to someone else.

Step 6: Teach the Concept
The final step is to teach the concept to someone else. Explain the concept in a simple, straightforward way and ask questions throughout the process. This will help ensure that you have fully understood the concept and can explain it effectively to others.

Conclusion
The Feynman Technique is an effective way for lifelong learners and students to learn and retain information. By breaking down a concept into its component parts, simplifying it, and teaching it to someone else, you can better understand the overall concept and how it works. The Feynman Technique can help you learn and retain information more efficiently, while also improving your communication skills.

How do you actually clean data in Excel?

Ben Goulding — Mon, 21 Nov 2022 17:14:50 +0000

What is data cleaning?

Data cleaning is the process of identifying and correcting inaccuracies and inconsistencies in data. Excel is a powerful tool for data cleaning because it offers many features for working with data, such as filtering, sorting, and conditional formatting.
When cleaning data in Excel, you will want to look for these errors and correct them accordingly. There are many ways to clean data in Excel, so it is important to find the method that works best for you and your dataset.

Errors can include invalid values, missing values, and duplicates. When cleaning data in Excel, be sure to check all cells that contain information relevant to your analysis.
Be patient while cleaning data in Excel; sometimes it may take several rounds of corrections before the dataset is accurate and consistent.

What do you need to look out for when cleaning data in Excel?

Start by identifying the source of your data. This will help you determine what type of cleaning is necessary.
Once you know the source of your data, open it in Excel and take a look at the overall structure. Is it organised in a way that makes sense? If not, you may need to reorganise it before proceeding with cleaning.

Next, start looking for any obvious errors or inconsistencies. These could be things like incorrect values, missing data, or formatting issues.

Once you've identified potential errors, decide how you want to handle them. Do you want to delete them, correct them, or just flag them for further review?

Finally, save your cleaned data set in a new file so you don't accidentally overwrite your original data set

How do you deal with empty values?

If you want to replace all empty cells in a worksheet with zeroes, use the Excel FillDown function. To find and select all the empty cells, type "=FillDown" into the cell where you want to start replacing values, press Enter, and then type "0" into each cell where you want to insert zeroes.

The easiest way to replace empty values with a specific text string is to use the Replace function. Type "Replace," followed by the text string you want to use for replacement, into the Cells box on the Home tab of your workbook and press Enter.

To deal with cells that contain only zeroes or nothing at all, use the IF function. Type "<>" (without any other characters) into the first cell in a row or column that you want to check for blank values, and then type 1 if there are any non-zero values in that cell, or 0 if there are no values in that cell at all.

You can also use special symbols such as "$" (dollar sign) or "/" (slash), which represent blank spaces within text strings, as part of conditional statements such as IF statements in Excel formulas.
To delete blank rows or columns from an Excel worksheet, highlight those rows or columns using either your mouse or keyboard shortcuts, and then choose Home > Delete > All Rows or Home > Delete > All Columns from your workbook's menus

How do you deal with duplicate records?

To clean data in Excel, you first need to identify which cells contain duplicate records. You can do this by using the COUNTIF function to count the number of times a value appears in a range of cells. If there are duplicates, you can delete them by selecting the cells and then pressing the Delete key on your keyboard.

You can also use the Data > Remove Duplicates command in Excel to remove duplicate values from your data set. Be sure to check for duplicates regularly, as they can introduce errors into your data analysis.
How do you deal with outlier values?
First, you need to identify where your outlier values are. This can be done by using the Excel functions of Min, Max, and Median which will return the lowest and highest value in a given range of cells.

Once you've identified the outliers, you need to decide how to deal with them. There are a few different ways to do this including:

a) Deleting them: If the outliers represent a significant portion of your data set then deleting them may be the best course of action.

b) Transforming them: Sometimes it's easier to transform an outlier value into something more representative before dealing with it. For example, if an outlier is extremely high or low compared to the rest of your data set, you could round it down or up accordingly before continuing with step 2.

c) Ignoring Them: Another option is simply ignoring the outliers altogether - they won't have any effect on your final analysis and they'll only take up space on your spreadsheet.

There are a few different methods for dealing with outliers depending on their severity:

a) Rounding Down/Up: If an outlier is severely off-base (e.g., it's much higher than all other values), you could round it down (to 0) or up (to 3rd decimal place). This will make it more comparable to other values in the data set while still retaining its individual identity.

b) Multiplying By a Known Value: If an outlier is relatively minor compared to other values in your data set, you can multiply it by a known value such as 1 or 100 before proceeding with steps 2&3 below. This will ensure that all instances of that value remain consistent within your data set regardless of its original magnitude.

c) Replacing The Outlier with a Resembling Value: Occasionally one instance of an outlier can be replaced by another similar but less extreme value without affecting overall accuracy or validity of your dataset. For example, if cell G2 contains an extremely high value compared to all others (e.g., 10x higher), and G1 contains a lower but still acceptable number (5x), then G2 could potentially be replaced with G1+500 so that both numbers are closer towards average within the data set without affecting its original meaning completely.(For more information on replacing outliers see our blog post here.)

How do you ensure consistency across variables?

The first step is to ensure that all of the data is entered in a consistent format. This means, for example, that all dates are entered in the same format (e.g. mm/dd/yyyy or dd/mm/yyyy) and that all numerical values use the same decimal point character (e.g. . or ,).

Once you have ensured that the data is entered in a consistent format, you can then start to look for any obvious errors or outliers. These can be anything from typos to incorrect values that don't make sense in context.

Once you have identified any errors or outliers, you need to decide how to deal with them. This will often involve making judgment calls on whether to correct the errors or simply remove them from the dataset altogether.

After dealing with errors and outliers, you should take a look at the overall distribution of your data variables. This will help you determine where further cleaning may be required.

Conclusion

In conclusion, data cleaning in Excel is not as difficult as it may seem at first. With a little practice, you'll be able to clean your data quickly and efficiently.

There are a few things to keep in mind when cleaning data in Excel: make sure to check for errors, duplicate values, and missing values.

Data cleansing is an important step in any data analysis process, so take the time to learn how to do it properly.

Level Up Your Data Visualisations

Ben Goulding — Fri, 11 Nov 2022 01:35:26 +0000

Define the purpose of your data visualisation.

Data visualisations should have a clear purpose - they should help you to understand or communicate something about your data.

A good data visualisation is story-driven, meaning that it guides the viewer through the data in a logical way. This makes it easy for viewers to understand what is being shown and why. Clear and concise visuals are essential in order to avoid confusion and make the information more easily accessible. In order to create effective visuals, it is important to first understand your audience and your data. Knowing what you want to communicate and how best to do so will result in better visuals.

Identify the key metrics and KPIs to include.

It is important to be clear and concise when creating data visualisations, as too much information can clutter the message you are trying to communicate. A good rule of thumb is to keep your data visualisation no more than two pages long, with each page featuring only one metric or KPI.

Data visualisations should be story-driven, meaning they should help tell a specific narrative that you want your audience to understand. When deciding what metrics and KPIs to include in your data visualisation, it is important to consider what will best support the storytelling objective you are trying to achieve.

In order for data visualisations to be effective, they must be easily understandable by viewers. This means using colours, shapes, and other visuals cues that will help convey the message of the data more clearly. It is also important not to overload viewers with too much information at once; instead, focus on providing just enough information so that viewers can follow along without difficulty.

When creating data visualisations it is also important take advantage of interactivity features such as drill down menus and charts that allow viewers further exploration into specific details within the data set. By making use of these features, you can make your data visualisations more engaging and informative for your audience.

Present the data effectively using charts and visuals suited to the type of data you're working with.

Data visualisations are an important tool for data analysis. They can help to make complex data sets more understandable and accessible, by simplifying the information and making it easier to see key trends.

It's important to ensure that your data visualisations are clear, concise, and tell a story that is easy to understand. The type of chart or visualisation you use should be suited to the data you're working with - using the wrong type of chart can make your data difficult to interpret.

Pay attention to the details in your data visualisations, such as labels, colours, and axes - these can all impact how easily others can understand your visuals. Less is often more when it comes to data visualisations - too much information can be overwhelming and make it harder to see the key points you're trying to communicate.

Keep in mind that charts and visuals are only one way of presenting information - other methods such as tables or text may also be useful for specific cases or datasets. Experiment with different types of media until you find one that works best for your dataset and audience!

Eliminate clutter and noise from your visualisation.

First and foremost, it is important to create clean and clear data visualisations in order to effectively communicate information. This can be achieved by using story-driven visualisations, which allow for a more efficient flow of information and a better understanding of the data being displayed.

Story-driven visualisations are effective tools for communicating complex information in an easy-to-understand format. By taking advantage of visuals that help viewers connect the dots, it becomes much easier to grasp the overall concept being conveyed.

Clutter and noise can easily take over data visualisations, obscuring important details and preventing viewers from fully comprehending the information being presented. To avoid this problem, make sure to keep your visuals simple and concise while still displaying all relevant data points clearly.

Tips for creating effective data visualisations can be found by following some simple guidelines such as keeping content organized into sections, using effective formatting techniques, etc.. By following these tips, you will be able to create visually engaging displays that convey your message effectively without detracting from its quality or clarity.

Use layout to focus attention on the most important insights and trends upfront.

Layout can be used to create a clean and simple visual for easy interpretation. By having all key information front and centre, you can direct viewers' attention to the most important data points first. This will help ensure that people understand your visual quickly and can make relevant comparisons or interpretations.

Keep your data visualisations clean and simple for easy interpretation.

When creating data visualisations, it is important that you keep them as clear as possible so that they are easily understood by readers. By using a minimalistic design style, you can make your visuals more effective in communicating information effectively. In addition, avoid clutter which could obscure crucial details; instead, focus on key findings only.

Make sure your data visualisations are story-driven and informative.

Your data visualisations should not just be charts and graphs – they should tell a story! By utilising compelling visuals with well-written accompanying text, you can encourage users to explore further by immersing them in the context of the data you have presented。This will help deepen their understanding of what is happening within the dataset and increase their engagement with your content overall.

Always test your data visualisations before sharing them with others.

Before releasing any data visualisation into the world, always make sure that it is properly tested – even if you plan on sharing it exclusively with friends or colleagues! There is no harm in running an initial version through a few rounds of beta testing before public release; this way, you can iron out any kinks without worrying about damaging reputations or damaging relationships.

Tell a clear story with your data visualisation that inspires action or facilitates change.

Data visualisations are an important tool for conveying information clearly and concisely. They can help tell stories that inspire action or facilitate change, and play a key role in communicating complex information.

Storytelling is an important part of communication, and data visualisations can be used to create compelling narratives. This can help drive understanding and engagement with the information being presented, and can even lead to changes in behaviour.

Clear and well-organised data visualisations are essential for effectiveness. If a data visualisation is cluttered or difficult to understand, it will likely not be effective at driving meaningful results.

Creating effective data visualisations involves Challenges both big (like making sure your data is accurate and up-to-date) and small (like ensuring your visualisation layout is easy on the eyes). However, by taking these challenges into account, you can create powerful visuals that convey your message effectively.

Tips for creating successful visualisations;

1) Stay organised – Make sure all your data sources are properly referenced so viewers know where they’re coming from

2) Use icons/images wisely – Icons are a great way to quickly communicate key concepts without having to write long descriptions

3) Don’t be afraid of colour – Using vibrant colours in your graphs can really stand out

4) Keep things simple – When possible, try to keep graphs as simple as possible so that they’re easier to understand

I hope these visualisation fundaments can be of use to you, I would recommend creating a checklist to work through prior to creating and client-facing visualisation to aid in delivering a clear, concise pitch that can be understood by everyone.

I wish you all the best.

Make a Change - The Growth Mindset

Ben Goulding — Wed, 09 Nov 2022 21:24:55 +0000

Individuals with fixed mindsets believe that their abilities and talents are static and cannot be changed. This can lead to feelings of frustration and defeatism when faced with challenges.

Students who have a fixed mindset may give up easily when faced with setbacks. For example, if they are studying for an exam, they may become discouraged if they do not achieve the desired results right away.

A growth mindset has been shown to be enormously beneficial in terms of motivation, resilience in the face of setbacks, and success in academic and professional settings. People who adopt this mindset see challenges as opportunities to learn more about themselves and grow as individuals.

Making the switch from a fixed to a growth mindset is not always easy - it requires effort and commitment on behalf of the individual. However, adopting this mentality can have a profound impact on both one's career development and overall satisfaction with life.

What is the difference between a fixed mindset and a growth mindset?

A fixed mindset is the belief that your abilities and talents are set in stone, whereas a growth mindset is the belief that you can always improve and develop new skills. People with a fixed mindset tend to give up easily when they encounter difficulties, because they think they can't improve. On the other hand, people with a growth mindset persist through challenges and setbacks. People with a growth mindset often focus on their own successes or failures, rather than on the process of learning itself. There's a greater interest placed on the journey over the destination. Finally, people with a fixed mindset often view others in terms of competition, while people with a growth mindset see others as potential collaborators and allies.

How can adopting the growth mindset help you in your studies, work and life?

A growth mindset can help you to better understand and learn from your mistakes. When you have a growth mindset, you view setbacks not as defeat but as an opportunity to learn and grow. This makes it easier for you to bounce back from failure and improve your performance overall.

Resilient in the face of setbacks. When you have a growth mindset, you don't take failures too personally and instead see them as opportunities for learning and improvement. This makes it much harder for setbacks to derail your progress and makes you stronger in the long run.

Look at failure as an opportunity for learning and growth. When you believe that success is achievable through hard work, effort and perseverance rather than luck or fate, then you are far more likely to be successful in achieving your goals. Numerous studies have shown that people with a growth mindset generally perform better than those who do not.

Set challenging goals for yourself and push to achieve them even when faced with difficulty or adversity. By seeing challenges as opportunities rather than threats, you are more likely to succeed in reaching your goals no matter how tough they may seem at first glance.

Adopting a Growth Mindset has been linked with increased happiness, wellbeing and self-confidence – making it one of the most beneficial mindsets available!

What are some tips for making the switch from a fixed to a growth mindset?

It can be tough to make the switch from a fixed to a growth mindset, but it's worth it! Here are some tips:

Accept that intelligence is not fixed – we all have the ability to learn and grow.

Be open to criticism and feedback – instead of seeing it as a personal attack, view it as an opportunity to improve.

Practice “growth mindset” activities – such as setting challenging goals and achieving them regardless of how difficult the journey to get there may appear to be.

I hope some of the points raised here can be of use to you, I understand it's easier said than done by try to treat failure as a learning experience and keep pushing through. Don't lose sight of your end goal, I wish you all the best.

Increase Your Productivity, Tackle Procrastination

Ben Goulding — Mon, 07 Nov 2022 10:39:23 +0000

I speak from personal experience when I say productivity and procrastination can be incredibly hard to deal with, it's often the case that you will make one step forward and then take two steps back. To help combat this I have outlined three different principles below that you may find beneficial and wish to research further.

I would like to add that there is no quick fix, it takes purposeful, intentional actions that you can uphold consistently, and only then will you start to notice differences.

Newton's First Law of Motion

If you want to increase your productivity, you need to tackle procrastination head-on. Procrastination is often the result of feeling overwhelmed by a task, which can be caused by looking at it from too large of a scope or feeling like you don't have the skills necessary to complete it. Breaking a task down into smaller, more manageable pieces can help reduce the feeling of being overwhelmed and make it easier to get started. Once you start working on a task, Newton's First Law of Motion can help keep you focused and motivated. The First Law states that an object in motion will stay in motion unless acted upon by an outside force. In other words, once you get started on a task, it becomes easier to keep going and see it through to completion.

Parkinson's Law

Parkinson's Law states that work expands to fill the time available for its completion, which can have a negative impact on productivity.

However, there are ways to overcome this tendency and increase productivity, such as breaking up tasks into smaller chunks, setting deadlines, and using time-management techniques.

By understanding Parkinson's Law and how it affects us, we can be better equipped to manage our time and achieve our goals.

Implementing even small changes in our daily routine can make a big difference in our overall productivity levels.

The Pareto Principle

The Pareto Principle is a principle that states that 80% of the effects come from 20% of the causes. This principle can be applied to productivity and procrastination – for example, 80% of your productivity may come from just 20% of your effort.

To increase your productivity, you should focus on the tasks that give you the most results. By focusing on the most important tasks, you can make sure that you are using your time effectively and avoiding procrastination.

To tackle procrastination, use the Pareto Principle to help identify which tasks are most important to you. By focusing on these tasks, you can make progress toward your goals and avoid procrastination altogether.

Further Advice

Understand your triggers: what causes you to procrastinate? Is it a certain task, type of task, time of day, etc.? Once you know your triggers, you can start to develop strategies for avoiding or coping with them.

Set small, achievable goals: rather than trying to do everything at once, focus on completing one small task at a time. This will help you avoid feeling overwhelmed and increase your sense of accomplishment.

Develop a routine: establish set times for working on tasks and stick to them as much as possible. This will help train your brain to associate certain activities with certain times of day, making it easier to get into the flow state.

Take breaks: when you start feeling overwhelmed or bogged down, take a few minutes to step away from your work and clear your head. Sometimes all it takes is a short break to refresh yourself and get back on track.

Learning How to Learn

Ben Goulding — Sat, 05 Nov 2022 22:52:29 +0000

When it comes to self-teaching it's often the case that you will fall into hours of unproductive study. It may even be the case that you have forgotten how to study completely! The three methods outlined below will get you back on track...

*What is chunking and why is it an effective learning technique?
*
Chunking is a learning technique that can help you better understand and remember information. By breaking down information into smaller, more manageable pieces, you can better process and recall the information. There are a few different ways to chunk information, such as using mnemonic devices or creating meaningful groups. Ultimately, chunking is an effective learning technique because it helps you process and recall information more effectively.

*How can you use retrieval to improve your learning?
*
Retrieval is an important part of learning and can be used to help improve your memory. Here are some tips on how to use retrieval effectively:

Make sure to vary the types of retrieval exercises you do. This will challenge your brain in new ways and help you learn more information.

Use retrieval techniques when you are struggling with a task. By using strategies like mental imagery or flashcards, you can help yourself remember the information better.

Think about what you want to remember when trying to retrieve information. If you know the material very well, try using visualisation techniques or mnemonic devices to help yourself remember it better.

Be patient with yourself – learning something new takes time and practice! don't get discouraged if you find it hard at first; keep practicing, and eventually retrieval will become easier for you!

*Why is spaced repetition a powerful tool for learning?
*
Spaced repetition is a powerful tool for learning because it allows you to review information at increasingly longer intervals. By spacing out your review, you can better retain the information and have it available when you need it. Additionally, spaced repetition is a great way to learn new material, as it allows you to focus on one thing at a time and gradually build up your understanding. Finally, spaced repetition is flexible and can be adapted to suit your individual needs – so if you find yourself struggling with a particular concept, you can space out your reviews more frequently until you feel confident with the material.

I hope these methods prove to be beneficial on your self-taught journey, I wish you all the best and good luck with your studies!

The Underpinnings of Data Analysis

Ben Goulding — Sat, 05 Nov 2022 00:44:32 +0000

The basics of data analysis: what it is and why it's important.

Data analysis is the process of inspecting, cleansing, transforming, and modelling data with the goal of discovering useful information. As organisations increasingly rely on data-driven decision-making, a basic understanding of data analysis techniques is essential for anyone looking to make sense of data and use it to improve their organisation's performance. There are a variety of different data analysis techniques, each with its own strengths and weaknesses. Understanding the basics of data analysis can help you choose the right technique for your specific needs and goals.

The data analysis process: how to go about conducting effective analyses.

Before you can begin any data analysis, you first need to understand what you are hoping to achieve. Are you looking for specific trends in your data? Trying to identify relationships between variables? Figuring out how a certain event impacted another variable? Defining the problem and the goals of the analysis will help guide you in collecting and organizing your data.

Once you have collected all of the relevant data sets, it's time to start sorting and organizing them into useful pieces. Try to group related data, separate extraneous information, and make sure that each piece of data is as accurate as possible. It's also important to keep track of which variables were used in which analyses so that you can properly interpret your results.

Once your data has been sorted and organised, it's time to choose the right methods of analysis based on what type of data you have gathered and what type of information you are looking for. There are dozens (if not hundreds) of different statistical tests available, so it can be difficult to decide which one is best suited for your situation. However, understanding which types of analyses are available will help narrow down your options!

Finally, it's important to carry out your analysis and interpret your findings using context clues from the original question or hypothesis. Sometimes simply comparing two groups or analysing individual values can provide enough insight for a proper conclusion!

Essential skills for data analysts: what you need to know to be successful in this field.

In order to be successful as a data analyst, you need to have strong critical thinking skills, mathematical and statistical analysis abilities, and exposure to some computer science concepts could potentially prove beneficial. Additionally, you need to be able to effectively communicate your findings to those who may not have a background in data analysis. To work with large and complex data sets, problem-solving skills are essential. Finally, continuing learning is important for any field – especially for a field like data analysis which is constantly evolving and changing.