Problem Solving as a Data Scientist

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Critically think about your role as a data scientist and what you have learned in this program as you answer the following prompts: A current business problem that your organization faces that you can work towards solving with the skillsets and experiences you’ve gained in this program? If you are not currently employed, what is a different real-world problem that you might solve? What technologies / languages need to be “brought to bear” on the problem?

One of the problems that I can see my organization facing in the future is product testing, metric measuring, and forecasting. I work in a project office in army aviation. At some point we test new features and new air vehicle models. I have already discussed working with the test engineering team to build dashboard to display test metrics and help with predictive models. This program has given me the foundation needed to assist with those topics. It has also allowed me to be more deeply invested in my job and use my skills in more areas than one. I am also currently a data manager so I put together a lot of reports and plans for how we handle all forms of data in our office. This program has already helped me to run reports more efficiently and provide better insight into issues with data.

I work as a ski lift operator at Breckenridge Ski Resort, under the umbrella of Vail Resorts, in Breckenridge, Colorado.

An interesting business analysis that I could work with using tools in this program is something I have actually spoken to the senior manager of mountain operations about. Our mountain has an app that gives updates on all facets of the resort. It provides live updates on the wait time of each lift on the mountain, volume of people inside the restaurants scattered across the mountains, snow conditions, and various other points of interest. The way in which wait time is calculated and predicted is very interesting. Using data from the number of passes scanned at base lifts, people actively tracking their ride on our app, and purchases at restaurants, predictive analysis can be used to provide information on the number of people out and how long it will take to get food or to ride any of the 30 lifts across the mountain. Once the information is collected, we turn to the dashboard interface on the app and can portray the data in any number of ways.

As the mountain expands and the data collection gets smoother, the predictive methods are continuing to be better and better and provide the data in a clearer manner. It was cool to discuss this, as it is not at all part of my day to day work, but something I have interest in and maybe something to work towards within the company in the future.

I work for a medical logistics company, and one area that falls under my responsibility is maintaining our handset and scanner inventory. One of the critical problems I encountered was holding a stable stock of devices for drivers in the field. There is an extremely high turnover of devices due to being destroyed, lost or stolen, or added work. Recently, I wrote some queries in SQL. These included ten years of data, geographical results of where tablets were utilized nationwide, and the growth of the business. Our company does not use Power BI, so I had to create a dashboard in Excel. I used Pivot Tables, slicers, and pretty colors of the data to show total units, historical purchases, predictability of new geographical areas, and trends on when assets were to be purchased. I made my presentation to management. Everyone was impressed with what the data showed. Management ignored my recommendations. We still have stock issues, and all my forecasting is proving accurate. Real-world reality – “You can lead a horse to water…..”

Many emergency departments throughout the country and around the world are experiencing long wait times. At our hospital, we are no exception. In order to resolve the problem, we need to identify the barriers. One of the challenges is having the right amount of staff and resources when the department experiences a surge of patients. If we could better predict hourly, daily, and monthly volumes based on historical data, the department may be able to balance staffing and resources.

To help solve this problem, I would need to utilize all the tools available to me, including R, SQL, CCL, PowerBI, Qlik, and real-time data feeds, to create a predictive model that could predict accurate volumes.

I would need to utilize historical weather data, including temperature and conditions such as sun, rain, or snow. I would also need to use moon phase data with daily percentages (to prove or disprove the full moon theory.) I would add calendar events around holidays, school schedules, report cards, end-of grades, government subsidy checks, and other community-type special events that draw large crowds. Most importantly, I need to add historical clinical data that includes dates, times, volumes, and reason for the visit to calculate recommended staffing patterns. I would also add historical epidemic data such as flu and COVID cases to help identify seasonal trends.

I would create a predictive analytic dashboard with historical data and a future forecast that is updated every 30-60 minutes. The combined data would help identify patterns and volumes for a specific time. This would allow the ED management team to see the daily forecast and predictions for the next seven and 30 days. The visualizations below are examples of some of the data and dashboards we currently have around ED volumes.

Predictive Analytics

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Predictive analytics requires structured data in general, so with this in mind. How does graph representation and graph analytics help with the unstructured data before or along with building predictive modeling? what are the trending Graph Database tools? What are the processing challenges with Graph databases compared to structured databases?

Graph representation and graph analytics is a huge help for that first step of understanding unstructured data. Unstructured data is information that isn’t arranged yet by patterns, trends, or any data model. Graph representations allows us to get a visual and draw initial conclusions and further predictions about the data we have in front of us.

Oracle, Neo4j, and Mark Logic are a couple trending graph database tools. We explored Neo4j a little bit in our previous course and it was neat to be able to see relationships and groups in the data we have.

In a graph database, the relationship among parts of our data are stored in the individual record level. Structured databases on the other hand uses these predefined structures to hold it all together. No standardized language is one of the biggest struggles. The other main issue is that sometimes graphs aren’t the best way to show and interpret things.

Graph databases allow a flexible schema or unstructured data to have linkages to different parts of the data. With easy expression of entities and relationships between data, graph databases make it easier for programmers, users and machines to understand the data and find insights. This deeper level of understanding is vital for successful machine learning initiatives, where context-based machine learning is becoming important for feature engineering, machine-based reasoning and inferencing.

What are the trending Graph Database tools?

The top ten graph database tools are listed below from this website (Links to an external site.).

Neo4j, ArangoDB, Amazon Neptune, Dgraph, DataStax,OrientDB, FlockDB, Cassandra and Titan.

What are the processing challenges with Graph databases compared to structured databases?

Specialty graph data engineers are needed to process and model the data
Maintaining the consistency and relationships of the data
Writing graph data queries
Every query you write is not re-usable. If you have 10 questions you want answered from the data you have to write ten different queries

Graph analytics is actually built to analyze both structured and unstructured data.
As the name suggests, graph analytics is a graph model which represents entities and relationships. We see unstructured data in social media, mobile, the Internet, and the huge volume that needs to be processed in a fraction of seconds. This makes it difficult to make the data structured (like relational data) and define the proper semantic layer.

Using a graph database here like NoSQL makes it more accessible, and dynamic with a low cost for new data to be integrated into the existing system. It has the capability to process and retrieve a high volume of data from multiple sources. Due to high flexibility, it becomes more accessible to interlink data, derives meaningful analysis, and make better decisions. (Link (Links to an external site.))

Some of the trending Graph database tools are : (Link (Links to an external site.))

Neo4j: This we have covered in detail in the previous courses.
AWS Neptune
Dgraph
Cassandra
Graph QL

Since we are dealing with highly interconnected data of both structured and unstructured with any volume we face the below problems (Link (Links to an external site.))
1. To Maintain consistency of data
2. Modelling the interconnected data
3. Writing the graph query
From my previous program I knew about knowledge graphs and deep learning but I was unaware of graph analytics, which is similar to knowledge graphs. Knowledge graphs are three dimensional graphs with for example text graphs words are along two axes and the third axis provides the distance in meaning between the words. These graphs along with convolutional neural network models were used to develop original images and text based on data seeds (Chollet, 2018). The concept of utilizing graph analytics with predictive modeling is a similar methodology. The graph analytics can provide degree centrality, shortest or longest path, community Ids and pagerank results which are similar to the knowledge graph distance between word meanings and clustering algorithms.

The only Graph Database tool I have used is Neo4j. I have heard of Oracle Spatial and Graph. The following article provides ranking of current graph databases https://www.predictiveanalyticstoday.com/top-graph-databases/ (Links to an external site.). Their top ones are ArangoDB, Neo4j, OrientDB, and AllegroGraph (Predictive Analytics Today, 2022).

With my limited experience, the issues with graph databases are that they are resource intensive and consume a significant amount of time to run complex queries. For one of my queries, I needed 64 gigabytes of RAM and a week to run the code. However, their syntax for complicated queries is easier than using PL/SQL with cursor code. PL/SQL code that is tens of lines is just a couple of lines in Neo4j, but the tradeoff is the time to run the complex query.

Research for data science article synopsis

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Research for a data science article synopsis or blog post that details a complicated real-world problem that was solved (or remains unsolved) using data analytics or predictive analytics.
Include details about the problem space itself, the technologies that were used to solve the problem, and what difficulties they encountered. If the problem remains unsolved, be sure to mention why and the obstacles that haven’t been overcome.
Provide a link to the article/blog post you are discussing. Finally:

Source: https://www.business-standard.com/article/companies/2022-to-see-rise-in-adoption-of-chatbots-hiring-in-data-analytics-report-122011001150_1.html (Links to an external site.)
One problem solved by data analytics is chatbots and remote work opportunities. From this article, we can see that chatbots are the solution to customer service needs. We probably all encounter chatbots nearly every day. The utilization of Chatbots allows for a better, more focused customer service experience. From my experience, there are chatbots that can pick up on keywords and phrases and yield an answer that is fitting to what the consumer is searching for.

UPS created a handheld route optimization tool called ORION (Links to an external site.). ORION provided drivers with the most efficient route for deliveries and pickups. To create this route UPS had to make custom detailed maps. The reason they couldn’t use map services like Bing or Google is because some drop off and delivery locations are not addresses but they are locations. Over time they have enhanced ORION to provide real time updates based on traffic and unknown pickup/drop off commitments when the original route was planned.

After collecting all of this logistical data, UPS was have to review the data and provider even further optimization. In 1959 George Dantzig created the vehicle routing problem which can be used to organize many things. For UPS finding the shortest route was instrumental but later finding the most optimized route which shortened total drive time became the most important. What UPS found after reviewing the data is that left turns have a much higher risk of accidents and a much longer wait time for traffic to clear to make the left turn. UPS found that if they optimized their route using right turns and only taking left turns when absolutely needed, it saved 10 million gallons of fuel, avoided the emissions of 20,000 tons of CO2, and delivered 350,000 more packages a year. The optimization model allowed for a max of 10% left turns in the route. As a result in this new optimization UPS was able to reduce the number of trucks by 1,100 and total distance travelled by 28.5 million miles. For a company it’s always great to save money and have a better customer experience.

This is a beautifully described use case from UPS on how we can achieve the most out of Data analytics and find an optimum route for delivery. With the power of analytics, UPS has dramatically changed its mode of operation.

Here is the takeaway from the blog:

Hierarchy of Impact through the use of analytics
Descriptive -> Diagnostics -> Predictive -> Prescriptive

Note: With the growth towards the more advanced feature of analytics the business also grows.

UPS was overwhelmed with great results with the help of analytics. They were able to save 100M miles driven, 8M gallons of fuel saving, 95 % reduction in load training time, 8B fewer manual entry, and 100,000 metric tons of carbon emissions

We should be able to forecast and plan ahead of what would be our demands and how we can meet their demands. With the use of Prescriptive analytics and by the use of the analytical tool and advanced algorithms we can take an even better decision (rather than taking risks in taking a decision )
For e.g.: ORION helps the UPS drivers to assist them on which route to go and which order to deliver in order. All the driver needs to do is follow the algorithm. It helps in route optimization.

And with the help of prescriptive analytics UPS was able to save an additional 300 to 400 M dollars annually

There are still many open questions that UPS is trying to figure out by use of data that has already been collected and analyzed it like if they need more access points, lockers, etc.

Link: https://www.zdnet.com/article/in-the-cloud-everyone-wants-to-be-your-partner/

Exploratory data Analytics EDA

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Exploratory data analytics is needed before building any predictions over the data, we need to understand the nature of the data and see any patterns found in it. So, with this in mind:
Tell Us: What kind of questions would you be able to answer through exploratory analysis?
Give any specific exploratory analysis you have performed. How does this step help in designing the predictive analytics model? Finally:
Exploratory analysis is not necessary about answering questions but generating questions for further investigation. In exploratory analysis we often visualize the data and perform simple data transformations such as sum and count. One question that can be answered in exploratory analysis is “what is the correlation between the variables?”. Using a correlation heat map this can be quickly done.

Exploratory data analytics in my job helps me add a new bond fund to our portfolios. There are over 50 funds available that could be added. i gathered data on all of the funds. I generated a correlation heatmap of the dataset, reduced the dataset down by removing some highly correlated variables then used a k-means cluster analysis to see if there were any definitive groupings of funds that were similar. From there i used one fund per grouping in our portfolio building process to see if that grouping would generate the desired outcome.

Exploratory analysis is crucial in business analytics because it enables practitioners to summarize and report on the main characteristics in a datasets. The major questions for EDA focuses on highlighting the distribution of the data, presence or availability of outliers, correlation between predictors, and the percentage of missing values in the data set. Actually, I compare EDA to detective work and understanding the data before making any statistical inferences.

My current job involves using SQL to handle much of the required deliverables. However, this program has exposed me to various EDA processes in R. An example is the recently completed Laptopsales project on R. The EDA involved understanding the mean, median, and existence of outlier prices in the data. Additionally, making tables of factors and understanding the percentage contribution of each variable is some sort of EDA to me. We do plots to ascertain the time series trends, histograms to understand distributions, and correlation matrices to understand multi-collinearity. I am not clear if unsupervised machine learning approaches like clustering can be considered as EDA.

Exploratory data analytics is a way to explore large data sets to become familiar, look for patterns and understand what the data (information) represents. The goal is to maximize the information in the dataset, extract meaningful information, find outliers that may skew the data, and identify underlying connections or relationships between the information. Once this is determined, the analyst needs to check the data for errors and see how the information trends over time.

When I started working with data in the emergency department, there was no significant dataset we could use to query information. From here, we needed to explore the raw data, find the fields that needed to be sourced and write the CCL code to export to SQL. In my case, I had to do reverse data analysis by asking the questions that needed to be answered and finding the fields that supplied the information.

Inaccurate information or information with large outliers caused by errors can have a negative impact on predictive analytics. It is important to explore the data to make sure the information is accurate. By having accurate historical data, we were able to create a basic predictive model with the ED arrivals. This allows leaders to determine the need to have additional staff report to work or start flexing who are no longer needed. Below is an example of one of our dashboards using predictive modeling based on current arrivals and predicting the trend over the next four hours.

Through exploratory data analytics, I would be able to answer the kinds of questions relating to the different products, profitability, and costs. Some specific questions I would ask through exploratory analytics would be: what is the most profitable product? what is the total revenue for the last year? for the last month? what was our total profit for the last year and month?

I have performed some exploratory analysis on the current dataset and answered some of the questions above. Once the data is pulled into a data analytics program such as Power BI or Tableau, the process becomes quite easy. By pulling in a few fields, I was able to break out profit by product for example. The exploratory step is crucial to designing a predictive analytics model as it helps you refine your questions you’re attempting to answer and helps you understand the current state of the business including certain trends. Then you can do predictive analytics and answer questions such as if current trends hold, where will we be in 6 months? a year? and so on and so forth.

ETL – Extract, Transform, Load

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ETL – Extract, Transform, Load. Raw Data coming from various sources including internal and external sources need to be cleaned and prepared so it can be integrated in a meaningful way before any analytics can be performed over it. There are many tools that can be used to perform the ETL. With this information in mind:

Tell Us:

Some of the trending ETL tools that are out there in the industry and which ones you have used. What are the some of the challenging ETL process you have come across or solved?

What are the limitations to the ETL step itself compared to Graph Data representation? Are there any use of Machine Learning Algorithms to perform ETL? Finally:

I believe one of easiest ETL tools to use is Alteryx. Alteryx allows you to input data and preform many different transformations in the data and then output the results you build. What makes it easy to use is how the program is designed. The user interface is all drag and drop and once your transformation is complete it will look like a process flow which is really easy to read and follow. Alteryx has a great community when you are struggling to google answers on as well. It’s a very expensive program which may limit the amount of people that have used it. I’m currently using Alteryx at my workplace but I don’t have access to an Alteryx Gallery (Server). This would allow me to schedule my workflows so they run automatically.

Alteryx Image.png

One of the challenges I came across during and ETL exercise is storing data. Some of the data at my workplace is a “point in time” data. Meaning the data can only be measured on the day of. What this doesn’t allow is good historical tracking. For example for our customers we know how many of them have an email address today. Lets say today we had 100 email address. Yesterday we don’t know if we had 90 or 100. So what I do is each month I query the email table to join to our customer table and store a monthly history. I use an insert statement to add records to a table I’ve already setup each month. This allows me to measure if the number of emails for our customers have increased or decreased over time.

With a relational database that you would use ETL in a star schema you’re setting up Dimension and Fact tables. In the graph data models you use one transactional data set and tell the software what fields to merge or connect on.

Several ETL (Extract – Transform – Load) tools are available for data management, such as Oracle, Informatica, Stitch, SAS, and Qlik. The hospital uses the Cerner EMR software that is part of Oracle. The Cerner Command Language (CCL) is the primary database query and scripting language used to extract healthcare data from the EMR Oracle database. Programmers can write CCL queries and scripts from the raw data and transform the information into operational reporting formats. Most reports are sent to their Discern Explorer Menu, their proprietary way for the end user to access reports and data. The CLL results can also be sent to other databases for custom SQL queries as well as the data warehouse for real-time dashboards.

Our CCL programmers take care of the CCL code and transform it into the EDW. My part is to validate the accuracy of the data going to the EDW for the end user to extract into reporting. This is often challenging as I need to understand where the source data is coming from, what data points to pull, and what the end result should be.

Relational Databases are great for storing and processing data. This practice has been around for several decades. They are used in Graph databases and do great with online transaction processing, analytical processing, business intelligence, ETLs, and advanced analytics. The information from the database can be processed into visualizations and graphs. RDBMS links data together through modeling. Graph databases offer flexibility and are easier to work with when linking information. However, graph modeling falls short when querying complex relationships.

I could not locate any information on true machining learning algorithms to perform ETL. Still, many algorithms provide a more automated approach to extracting, processing, and transforming the data.

The following is an example of our source data in the EMR and how it is stored, extracted, transformed, and loaded into the EDW for the end user to use for data mining, reporting, and Qlik dashboards.

Data Visualizations That Inspire

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Oftentimes if you’re stuck on how you might precisely create a visual to show something in certain way it can be helpful to look at data visualizations that inspire you.

Find three or more visualizations that are unique and inspire you.

Describe the problem the visualization is solving and what you find it exceptional.

Identify a complex visualization. Include the visualization in your posting and the source as well.
The first complex visualization I was inspired by was the water footprint of the countries of the world. The below screenshot is the complex visualization:

World Water Footprint

The visualization is illustrating which countries use a lot of water and comparing them to ones that use little water per capita. It depicts how little clean water is available to the people of the world. I appreciate this visualization because it graphical displays the data as well as it has the facts listed with each graph and that multiple different types of visualizations are utilized in the entire graphic (Glivinska, 2022).

I have two dogs and because of them I was inspired by the visualization of the prices of hybrid dogs in the United Kingdom. The following screenshot is the visualization:

Hybrid dog prices in the UK

The visualization shows the parent bred types that are combined to make the hybrid dog with their associated average prices in 2019 and 2020. The graphic illustrates the cost of the hybrid dogs, the typical ones that are available for sale in the United Kingdom and the most common parent dog type (The Slow Journalism Company, 2022a). The reason I find this graphical inspirational is that it shows cross breeding of dogs and their associated costs in one graphic that is visually interesting.

The third visualization that I selected is about the amount of gold in the world and its status in availability or usage. The below screenshot is the visualization:

World Amounts of Gold

The graphic indicates that the majority of the gold in the world has already been mined, the amount left to be mined and various uses or stores of gold. It is interesting to note that a significant amount of gold is dissolved in the oceans (The Slow Journalism Company, 2022b). I find this graphic exceptional because of the way that it has grouped the data and illustrated the volumes of gold for comparison purposes.

Reference:

Glivinska, A. (2022, July 5). The 25 Best Data Visualizations of 2020 [Examples]. Medium. URL. https://visme.co/blog/best-data-visualizations/ (Links to an external site.).

The Slow Journalism Company (2022a, July 5). Dog prices infographic: How much is that doggie in the window? Medium. URL. https://www.slow-journalism.com/infographics/dog-prices (Links to an external site.).

The Slow Journalism Company (2022b, July 5). Gold mining infographic: A bunch of carats. Medium. URL. https://www.slow-journalism.com/infographics/gold-mining-infographic

Week 1 – Difficult Data Analysis

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A specific example for a difficult data analysis, querying, or scripting problem that you had to solve, and describe the process by which you solved it.

Were you successful in solving the problem? Why or why not? What did you do really well and what could you have done better?

Working in healthcare analytics can be quite challenging. The emergency department has several KPIs that are measured and compared to other organizations. One of the key performance indicators is admission order to ED discharge. For several years, the time stamp that was used was the arrival to the floor (bed) location. As I reviewed the metrics and time stamps, I realized that several hours could be included in the time, especially if the patient went to the operating room before arriving at the inpatient unit.

The challenge was how do we capture the correct date and time the patient left the emergency department as the source of truth to the time stamp. We determined that a custom UDF was needed to capture the specific ED discharge time stamp. Our EMR vendor, Cerner, needed to create a new time stamp. We also needed to develop a process to capture and source the metric in the CCL code to the SQL reports.

It took about six months to develop, program, and source the code for reporting. A new workflow was developed for the patients that were admitted to the floor, and the ED needed to discharge the ED visit but keep the inpatient encounter active. The new checkout process allowed the ED to discharge the patient from the unit as they moved to the floor, regardless if they went to the OR or another procedural area first.

The change has been relatively successful in capturing the correct date and time. However, it still has its flaws that we need to reevaluate. Suppose a patient’s arrival to the floor timestamp is before the ED checkout time. In that case, there is a possibility the checkout time is incorrect unless there was an accidental electronic move to the room occurred. In this situation, a more detailed audit of the timestamps is necessary.

Each week I am responsible for billing and compensation for our logistic drivers in our company. For reasons unknown to me, billing and compensation were processed in two places: in the proprietary TMS, we use and QuickBooks. The CFO used QuickBooks for his audits and finalizing our business for month-end and year-end statistics. The programmers built the mechanism for our software to talk to QuickBooks. We were off a couple of dollars here and there when things started. Then everything came online, and we began showing discrepancies of thousands of dollars. The programmers washed their hands of it because their code “worked.” I was given the task of getting to the bottom of it. I know the database. I know how it behaves. The queries started. The analysis started. Combing the code, I needed to determine the tables the programmers used. I also had to decide how items were coded as GL Codes to talk to QuickBooks. Presto. That was where the bulk of the issues was found. The employee who set up things in the TMS did not code them properly and were being imported into QuickBook incorrectly. Cost of Goods was coded as services; certain accessorial charges were coded as Courier Revenue.

I was successful in solving the problem. My Achilles heel is accounting. It took me a while to figure out what GL Codes were and how they married to the TMS. I am very good at analyzing data and using SQL. That is why I like being a data detective and finding solutions to problems by analyzing data. Fortunately or unfortunately, I have become the one everyone comes to for these types of issues.

Informative essay topics

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It may appear to the majority of people that the task of writing informative essay topics is a challenging one, but this is not necessarily the case. Simply breaking the process down into its component parts makes it much simpler to complete.

When writing an informative essay, the very first thing you need to do is decide what topic you want to write about. It would appear that this is the most challenging undertaking for most people. We have provided you with a list of fifty interesting subjects to choose from when writing an informative and engaging essay. When you have decided on a subject, you have a focal point around which you can organize your subsequent work.

Shorten your essay

What Constitutes an Informative Essay, Precisely?

In order to write the best draft possible of an informative essay, you need to first have a solid understanding of what an informative essay is and how it differs from other types of essays. An informative essay, as its name suggests, is one that focuses on gathering a great deal of pertinent information that is of use to your audience, but in such a way that it keeps them hooked until the very end of your essay. This type of essay can be distinguished by its use of the word “informative.”

You shouldn’t overlook the importance of including credible evidence in your essay in order to get it ready in a timely manner.

How does one go about picking the best subject matter for an informative essay?

When you have a plan for completing a task, it is much simpler to complete that task, which includes choosing an ideal topic for your essay. In general, informative topics are those that are neither overly specific nor overly general. Therefore, to select the most appropriate subject matter, choose your title with consideration for the readers in mind. Another thing that you should keep in mind is to take into consideration any guidelines that were provided to you by your supervisor. As soon as you have found a happy medium between the two, selecting an informative subject for your essay will be a breeze for you.

50 of the Best Subjects for Informative Essays Written by College Students

The following list of fifty ideas for excellent essay topics is organized into categories for your convenience.

Essay subjects that are packed with action and adventure

When it comes to writing an informative essay on an adventure, there are many different directions you can go in. You could, for instance, write about the experiences you’ve had in your own life or about the experiences of a famous person. Additional adventure-related topics that you could write about include the following:

Putting up a sturdy tent in the wet and muddy woods.

RVing and camping in RV parks

A guide to hiking on the trail that is closest to you.

Your most recent exploits at the thermal springs that you just recently went to

An experience in which you came dangerously close to passing away, along with the important lesson that you took away from it.

Discussions that illuminate various aspects of leadership

The majority of the time, essays on leadership will either discuss a particular scenario or will focus on a single leader. Nevertheless, if you want to make things more interesting, you can write about any one of the following:

An examination of the differences in leadership styles between the antebellum and reconstruction periods in American history

Which form of economic organization, communism or capitalism, makes for a more effective form of government?

During the time of European colonialism, there was exploitation.

What led to the rise of fascism in Europe after World War I

Does a government that is democratic actually allow for complete freedom of speech?

Ideas for essays on making decisions

You are able to write an outstanding essay on the topic of decision making, regardless of whether you are discussing a monumental and earth-shattering decision or a regular, day-to-day decision. The following is a list of some examples of potential topics for you to choose from:

The extraordinary ritual of selecting an outfit for the day and determining how to proceed with it

Your choice of groceries may be affected by prevailing fashions and environments in the supermarket.

How can you choose the best book to read when there are more books being published than at any other time in history?

Should governments provide all of their citizens with free healthcare?

Should a country have its own news outlets run by the government?

Always a fan favorite, here are some interesting essay topics on sports.

Fans of every imaginable sport can be found all over the world, and their passions can run the gamut from cricket to football to basketball. Therefore, writing an essay about your favorite sports can be an interesting topic, regardless of whether you choose to take a humorous or a more serious approach. You could, for example, write about any one of the following topics:

Why leadership is such a crucial quality to possess on the playing field

An article that focuses on one of your favorite athletes.

Pep talks in the locker room can have a significant impact on an athlete’s performance.

The history of FIFA and the reasons why it has become a worldwide phenomenon

The Paralympic Games and the Significance They Hold

Ideas for expository writing about food, including both healthy and unhealthy eating styles

The vast majority of people enjoy eating, regardless of whether they are strict vegetarians or addicted to fast food. You can write at length about an almost infinite number of food-related topics. Writing about subjects connected to food allows you to approach the subject from a number of different angles, which is the activity’s primary benefit. Listed below are some examples of topics:

The concept of what constitutes a healthy diet varies greatly depending on the country of origin.

Fast food has never been more popular than it is in the modern world, where it has reached an all-time high in terms of its prevalence.

Consistently ordering takeout could have negative effects on one’s health.

In industrialized countries, the prevalence of childhood obesity can be attributed to the consumption of foods that are high in calories.

Obesity in children poses a serious threat, particularly in today’s society.

A listing of informative essay topics pertaining to tobacco use

It should come as no surprise that one of the leading causes of death across the entire world is smoking. The following are just a few of the many topics that can be discussed in relation to tobacco use; however, this list represents only a very small portion of the total possible topics:

Should smoking in public places be made illegal because it poses a risk to the general public, or should people who choose to smoke be allowed to do so wherever they like?

In light of the damage that smoking causes to one’s body, cigarette use ought to be completely prohibited.

When they are aware of the risks, why do some people still light up even though they know it’s bad for them?

People who smoke cigarettes consistently report that doing so improves their mood.

The act of passive smoking has ethical ramifications, as well as societal and health-related effects.

Discuss wrongdoing and its repercussions.

Writing an informative essay on any one of the following topics could be helpful if you want to expose the evils of society and talk about the social issues in the hopes of making an impact with your writing. If you want to do this, some topics you might consider include the following:

homicides committed in the name of honor and the total number of victims it claims each year

What leads people to commit minor offenses like shoplifting and what can be done to discourage the behavior are discussed.

Bringing awareness to the issue of domestic violence, which affects a disproportionate number of children and women in many societies in today’s world.

Capital punishments – a barbaric practice

Animal cruelty is at an all-time high, with examples ranging from factory farming to dog fighting for entertainment.

Environmental issues

Due to the whirlwind of extreme weather conditions that Australia has been experiencing, which has resulted in one disaster after another, now is as good of a time as any to bring attention to environmental concerns by reading the following informative essays:

Why, in the 21st century, is renewable energy not utilized to its full potential, despite the fact that it is readily available from sources such as the sun, the wind, and the water?

The current environmental problems that we face, which include a lack of natural resources, a reduction in biodiversity, the destruction of forests, pollution, and rising temperatures, could, in the end, result in the extinction of the human race.

There have been a number of steps taken to curb the effects of global warming, including the implementation of restrictions on emissions and the promotion of renewable forms of energy. Is it possible that these actually work?

Despite the implementation of renewable energy solutions such as electric vehicles, windmills, and solar panels, there are still environmental issues that need to be addressed.

Back in the 20th century, manufacturers first started churning out electric vehicles. Why is it that electric vehicles are not yet readily available to everyone?

Gender issues

Because the #MeToo movement has been gaining steam over the past few months, now would be an excellent time to have a conversation about gender roles and the problems that are associated with them. The following are some examples of essays that you could write for that:

The beginnings of the concept of machismo, as well as its pervasiveness in a variety of contexts, such as the political sphere, sports, and popular culture.

The way gender norms are distributed in your community, as well as the reasons for how they are distributed the way they are.

The significant impact that well-known feminists like Eleanor Roosevelt, Hillary Clinton, and Oprah Winfrey have had on the lives of women as a result of the work that they have done is described.

The beginnings of feminism, its place in the annals of history, the significance it holds in the present day, and the potential impact it could have on the future

There have been instances of matriarchy in both contemporary and historical societies.

Work performed by children

Child labor has been around for years, but there hasn’t been much effort put into finding solutions to the problem. You might consider writing about one of the following subjects in order to elucidate the situation more fully:

Children have the opportunity to develop while also enjoying themselves in developed nations, but this issue is a significant problem in developing nations.

The history of child labor, including its origins and how it came to be practiced so widely

The differences in the prevalence of child labor across different geographic locations.

There are laws prohibiting the employment of children in various countries, but how effective are these laws?

A significant contributor to the employment of children is the problem of unemployment. This is why:

Conclusion

Writing informative essays can be challenging because it is necessary to provide the reader with something of value in each and every sentence. If you fail to do so, there is a significant possibility that the reader will stop reading your essay in the middle of the process. As a result, if you want to write an interesting essay, you should make sure to brainstorm all of your ideas before writing the first draft of your essay.

Want to write a perfect essay?

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A perfect essay is needed To pass your class and graduate with a degree, regardless of the level of education you are currently pursuing, you almost certainly need to write more than a few assignments.

A piece of writing known as an essay analyzes a prompt by contrasting and comparing ideas, considering various viewpoints or angles, and using evidence to support a claim or point.

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Essays are used by various people for a variety of purposes. For instance, college professors include perfect essays in their course materials, and businesses hire freelancers to create persuasion-based writing to market their goods or advance their brands.

A perfect essay must contain certain key components. These components will guarantee that you get a good grade and support your continued education.

Follow these seven key components to successfully plan, write, and edit an essay to get the grade you need to pass your course and get your degree.

#1: Conduct research

In order to write a perfect essay, you must be clear about the claim you are making and possess sufficient background knowledge on the subject to persuade the reader of your point of view.

This information is available from a variety of sources, including:

On the web

Books, videos, or lectures on audio

The instructor’s lecture slides and class notes

information from the library

Once you have all the information you require, be sure to create an outline so that your essay flows naturally from one idea to the next.

# 2: Create an essay outline

The next crucial step in writing the ideal essay is to draft an outline. With the aid of an outline, writers can make sure that every aspect of their essay is addressed and that the various parts flow into one another.

Although each professor will have their own formatting requirements, an outline should typically contain the following:

a thesis statement in the opening

body paragraphs that include topic sentences and details

a summary that includes a restatement of the thesis

The outline need not be exhaustive, but it should include the key concepts that will be covered and the sequence in which they will be discussed. It’s best to arrange your strongest points first and your weaker points last as a general rule of thumb.

Once you’ve finished outlining your essay, it’s time to begin writing the first draft, beginning with the introduction.

No. 3 – The Start

Many students view the introduction as the hardest section of the entire essay.

The topic being discussed in the first paragraph should be introduced in a concise and direct manner. You must write a compelling introduction that will capture your reader’s interest and set the tone for the rest of the essay.

Start your essay with an intriguing first sentence that summarizes the main idea to grab readers’ attention right away. Consider the scenario of writing an essay on global warming. Then a good opening line might be something like, “Global warming is a subject that everyone should be concerned about and taking steps to ensure doesn’t negatively affect our planet.”

Use a transitional word like “in addition” to begin the second sentence, continuing the theme of global warming.

A strong introduction also provides background information on the subject, including any relevant historical context or information about the current political climate. Avoid including too much information in this section of the introduction, though, as you want to save that for the body paragraphs that come after the intro.

The body of your essay can be written after you’ve finished writing the introduction and are happy with the results.

Fourth: The Body

Strong body paragraphs that back up your thesis and offer in-depth analysis are essential to writing an effective essay. The majority of an essay, usually three quarters or more, is devoted to the body of the essay.

A well-structured body paragraph includes an introductory sentence with a transition word at the start, three distinct and logical points that support the first sentence (and offer proof to support the argument), and a concluding sentence that restates the paragraph’s main idea.

Examples of opening sentences for body paragraphs are as follows:

For instance,

Aside from that

Moreover.

Your strongest argument for the paragraph’s opening sentence should be the first logical point. The first point should be followed by two less important ideas that deepen the analysis or support your essay’s thesis. These should also contain clear information and examples of how you arrived at your conclusion, thought, or idea. They should also start with a transitional word.

You have the option of writing in the first person or the third person if your essay is informal or persuasive. In either case, you should vary your sentence construction and write concise, specific paragraphs.

A well-written essay must have proper grammar and spelling, so make sure to proofread your work before submission.

# 5 – The Verdict

Writing a solid conclusion paragraph can occasionally be difficult, just like the introduction. A strong conclusion, on the other hand, gives readers something to reflect on or feel motivated by after they have finished reading.

A concise, direct sentence serves as the basis of a strong conclusion. It may also contain a quote from a well-known individual or a citation to an additional source you consulted while writing your essay, like a book or article on the subject.

As the final paragraph before moving on to another essay or document, this section should be succinct.

To leave a lasting impression on the reader, conclude with a strong statement of your main point. There are numerous ways to conclude an essay; when selecting the one that is most appropriate for your project, keep your audience in mind.

Even though the conclusion is the last paragraph you write, it is not your final opportunity to wow readers with an excellent essay! Many different tasks need to be completed after you’ve finished writing before submitting your final draft.

# 6: Editing and proofreading

Although it may seem difficult, proofreading your essay for grammar and spelling errors is a crucial step in the writing process. This is due to the fact that any writing errors will make it challenging for the reader to concentrate on the essay’s actual subject matter.

Prior to rewriting, revising, or editing, the first draft should always be proofread. Read your writing aloud before proofreading to get an idea of how it sounds. This will help you understand any grammar, word choice, or sentence structure issues that make it difficult to read or understand your essay.

Use any of the many online editing tools, such as spell check and grammar check, after you’ve finished your first draft. These tools help you catch errors that might go unnoticed while having a reader go over your work, but they aren’t perfect. Before moving on to other papers or essays, be sure to proofread each one twice.

It is time to move on to the last stages of your essay writing process once you feel that all necessary revisions have been made and your proofreading marks are accurate.

#7: Go back and read it once more!

Even though it might seem like a waste of time after making all the necessary revisions to sit back and read your essay, doing so is crucial to writing well. After finishing your first draft, you can check for any minor errors that were missed the first time around because you were too overwhelmed, such as:

Spacing mistakes

inaccurate in some contexts

Missing or incorrect punctuation

diction that is inconsistent

Additionally, this is the time to bring in a friend or family member who is unfamiliar with the subject of your essay and ask them to proofread it for errors.

This will enable you to assess the quality of your work objectively and identify any typos, errors, or tonal problems that may have escaped notice during editing.

Once you are satisfied with the way your final draft reads and appears, format the essay in accordance with any instructions provided by your professor. These recommendations may include:

text size

where a title or header should go

the inclusion of any citations

line width

Margin depth

type of paper

document size

Every time, write a perfect essay!

With the right resources and planning, writing the ideal essay need not be difficult. To produce a final product of high quality that wows your readers, you must understand what must occur prior to, during, and after writing.

In conclusion, each of the previously mentioned components is crucial to creating a successful essay. Every time you are required to write an essay or research paper for a class or publication, make sure to include them. Your writing will be compelling and clear if you adhere to these seven steps, enticing readers from beginning to end!

Want to save time writing your essay outline?

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When given an essay assignment, the first thing you should do is write an outline. Writing an essay outline has a lot of advantages. Along with assisting you in organizing your thoughts, it also greatly speeds up the process. Additionally, a student’s time is a valuable resource. Anything that saves time is a blessing with so many classes, activities, and responsibilities, and an outline is just that.

Here are some guidelines and advantages to creating an outline so that you won’t ever want to write an essay without one again if you’ve never done it before or if you want to change up your outline style.

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How Outlines Can Save You Time

Writing an outline can help you save time, which is one of its main advantages. Yet how? Isn’t just sitting down and starting to write faster?

Although creating an outline may appear to be an additional step, it actually facilitates the other steps of essay writing, lowering the need for rewriting and accelerating the editing process.

A time-saving outline includes the following:

starts the writing process off quickly – It can be very difficult to get an essay started. You might be unsure of where to begin or what to talk about. Starting with an outline relieves pressure rather than trying to write the entire essay in one sitting while staring at a blank page. Developing the habit of writing an outline first can help you get your thoughts moving while avoiding the commitment of having to write the entire essay just yet if you frequently experience writer’s block or procrastinate when writing essays. Actually, you’ll discover that after writing your outline, you’re more eager to write your essay and fill in the blanks. Although creating an outline may initially appear to be more work, it actually helps you write essays faster by removing obstacles.

Detailed outlines help you see where your argument is going and better organize your thoughts, which results in tighter arguments. When you write, your arguments will be more concise and well-organized because you took the time to analyze, add, remove, and rearrange your ideas. This saves you a ton of time and keeps you from having to revise your arguments later if you find they weren’t very persuasive.

Better flow – Your essay will flow more naturally from paragraph to paragraph if you create an outline before writing it. Better transitions will be used, and the essay as a whole will be simpler to read and edit. There will be no need for you to waste time attempting to connect your ideas. Everything will be explained to you.

Easy editing – Your arguments will be more cogent and the transitions will be better if you create an outline first. You will discover that there is hardly any editing left to be done. It might require a little word-tinkering, but other than that, it should be the simplest editing process you’ve ever encountered.

How to Save Time by Writing an Essay Outline

Even though essay outlines are straightforward, there are a few preliminary steps you should take in order to create better outlines.

You must first clearly understand the nature of the assignment. The rest of your workflow will be determined by this.

The next step is to gather your research. You’ll jot down ideas and take notes as you conduct your research.

You will select your thesis based on your research.

Knowing the arguments you’ll use to support your thesis is necessary once you have one.

You can now begin writing your outline.

A strong outline for an argumentative essay will resemble this:

Include a hook in the opening sentence of your paragraph (a strong statistic, quote or story). Then, formulate your thesis.

The second paragraph will need your first supporting justification. This is the place to make your strongest case. Summarize it in a sentence or two and include information on the sources you plan to use.

Third paragraph: In this paragraph, you should add more proof for your thesis. Summarize it in a sentence or two and include information on the sources you plan to use.

Include an antithesis in the fourth paragraph. Investigate and take into account the opposing viewpoint and the reasons you don’t agree with it.

The final paragraph (conclusion) looks at the issue’s next steps. What do you envision people saying in 5, 10, or 50 years about it? Include one or two sentences that sum up your ideas.

See, it wasn’t that difficult after all. Additionally, consider the time you’ll save by editing, copying, and pasting while moving arguments and eliminating ideas. You can see exactly how your essay will flow from this outline and identify any weaknesses in your argument before you even begin writing.

Conclusion

Undoubtedly, having a strong outline can save you a ton of time. The entire essay writing process will go more quickly and smoothly if you are organized and know what you want to say. Have fun writing!