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# DSCI 100 Data Science Assignment Sample UBC Canada

**DSCI 100 Data Science Assignment Answers** is designed to provide you with the fundamental knowledge and skills you need to start your data science career. In **DSCI 100 Assessment Sample**, you will learn about the basics of data mining, including how to select and prepare data, identify patterns and trends, and build models to predict future events. You will also learn how to use machine learning algorithms to make predictions and recommendations.

In addition to the **DSCI 100 Assignment Answers** content, you will also have access to a wealth of online resources, including tutorials, templates, and example code. The **DSCI 100 Assessment Answers** is an excellent way to prepare for a career in data science.

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**Assignment Activity 1: Read data using computation from various sources (local and remote plain text files, spreadsheets and databases)**

There are a number of ways to read data using computation from various sources. Local and remote plain text files, spreadsheets, and databases can all be accessed and read using the appropriate software. The data can then be processed and analyzed as needed.

Some common software programs that can be used to read data from various sources include Microsoft Excel, Access, and SQL Server. Other programs that can be used include MySQL, Oracle, and PostgreSQL.

Once the data has been read, it can be processed and analyzed using a variety of methods. This may involve using mathematical and statistical methods to find patterns and trends. The data can also be visualized using tools such as charts and graphs.

Machine learning algorithms can also be used to find correlations and make predictions about future events. After the data has been processed and analyzed, it can be used to make decisions or help improve the bottom line of an organization.

**Assignment Activity 2: Wrangle data from their original format into a fit-for-purpose format.**

There are a few different ways to wrangle data, depending on the format of the data and what you want to use it for. If the data is in a plain text format, it can be wrangled into a more usable format by either parsing it into columns or by converting it to a tab-delimited file. If the data is in a spreadsheet, it can be wrangled by converting it to a tab-delimited file or by using formulas to extract the desired information. If the data is in a database, it can be wrangled by extracting the data into a spreadsheet or by using SQL queries to extract the desired information. The process of wrangling data involves four main steps:

- Gathering data from various sources (this could be manual entry, web scraping, etc.)
- Cleaning up the data to get rid of any invalid or incorrect entries
- Formatting the data so it can be used for your specific purpose (this could involve creating new columns, reorganizing existing data, etc.)
- Saving the wrangled data in a new format (usually a CSV or Excel file) so you can use it later on.

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**Assignment Activity 3: Identify the most common types of research/statistical questions and map them to the appropriate type of data analysis.**

There are many different types of research and statistical questions, but some of the most common ones are:

- Descriptive Statistics: This type of analysis is used to summarize data. It includes measures such as mean, median, mode, and standard deviation.
- Correlation Analysis: This type of analysis is used to determine the strength of a relationship between two variables. It produces a coefficient that ranges from -1 to +1. A value of +1 indicates a perfect positive correlation, while a value of -1 indicates a perfect negative correlation.
- Regression Analysis: This type of analysis is used to determine the strength and direction of the linear relationship between two variables. It produces an equation that can be used to make predictions about future events.
- Time Series Analysis: This type of analysis is used to examine data over time. It can be used to identify trends and patterns.

These are just a few of the most common types of research and statistical questions. There are many other types of questions that can be asked, and each one will require its own specific type of data analysis.

**Assignment Activity 4: Create, and interpret, meaningful tables from wrangled data.**

There are a few steps involved in creating meaningful tables from wrangled data. First, you need to identify what information you want to include in the table. Second, you need to wrangle the data into the desired format. Finally, you need to interpret the data in the table to draw conclusions.

When it comes to interpretation, it is important to keep in mind that not all data is created equal. Some data points may be more reliable than others, and some may be more relevant to your specific question. With that in mind, it is important to exercise caution when drawing conclusions from data tables.

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**Assignment Activity 5: Create, and interpret, impactful figures from wrangled data.**

There are a few things to keep in mind when creating impactful figures from wrangled data. First, choose an appropriate visualization for the data – a bar chart for categorical data, a line chart for numerical data, etc. Second, make sure the figure is well-labeled and easy to interpret. Third, consider adding annotations or other forms of commentary to help explain the figure to your audience. Finally, remember that sometimes less is more – don’t try to cram too much information into one figure. By following these tips, you can create figures that are both visually appealing and informative.

**Assignment Activity 6: Collaborate with others using version control**

When it comes to data wrangling, collaboration is key. By working with others, you can share resources, compare results, and create a more accurate picture of the data. The best way to collaborate is by using version control software. This allows everyone involved in the project to track changes, make suggestions, and resolve conflicts. There are many different version control software programs available, so it is important to choose one that is right for your project.

Some of the most popular version control software programs include Git, Mercurial, and Subversion. Each one has its own strengths and weaknesses, so it is important to choose the one that best fits your needs.

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**Assignment Activity 7: Apply, and interpret the output of simple classifier and regression models.**

Classification and regression are two of the most basic types of machine learning models. Classification models are used to predict which category a given observation belongs to, while regression models are used to predict a numerical value for a given observation.

In order to apply a classifier or regression model, you first need to train the model on a set of data. The training data is used to teach the model how to correctly predict the target variable. Once the model has been trained, it can be applied to new data in order to make predictions.

The output of a classification or regression model is usually expressed as a percentage or probability. This number tells you how likely it is that the model will correctly predict the target variable for a given observation. It is important to remember that these numbers should not be taken too seriously – they are only estimates and should be used for comparison purposes only.

**Assignment Activity 8: Make and evaluate predictions using a simple classifier and a regression model**

Once you have trained a classifier or regression model, you can use it to make predictions. To do this, you need to provide the model with a set of data that it has never seen before. The model will then use what it has learned from the training data to make predictions about the target variable.

It is important to evaluate the predictions made by a classifier or regression model. This can be done in a number of ways, including precision, recall, and accuracy. Precision tells you how likely the model is to correctly predict the target variable for a given observation, recall tells you how many of the observations the model gets correct, and accuracy tells you how often the model is correct overall.

By evaluating the predictions made by a machine learning model, you can get a better understanding of how well the model is performing. This information can then be used to improve the model or to choose a different model altogether.

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**Assignment Activity 9: Apply, and interpret the output of, a simple clustering algorithm**

Clustering algorithms are a great way to group data points together. They can be used to find groups of similar items or to cluster data points based on some similarity metric. There are many different clustering algorithms, but they all have one goal in common: to group data points together in a meaningful way.

The output of a clustering algorithm is usually expressed as a number of clusters. This number tells you how many groups the algorithm has found. It is important to remember that these numbers should not be taken too seriously – they are only estimates and should be used for comparison purposes only.

One popular clustering algorithm is the k-means algorithm. This algorithm works by finding the center point of each cluster, and then grouping data points around those center points. The k-means algorithm is easy to interpret and apply, and it can be used on a variety of different datasets.

**Assignment Activity 10: Distinguish between in-sample prediction, out-of-sample prediction, and cross-validation.**

In-sample prediction is when you use a machine learning model to make predictions on the same data that was used to train the model. This is not a very realistic way of evaluating a model, because it doesn’t give you an accurate representation of how the model would perform on new data.

Out-of-sample prediction is when you use a machine learning model to make predictions on data that was not used to train the model. This is a more realistic way of evaluating a model because it allows you to see how the model would perform on new data.

Cross-validation is when you split the data into two parts: the training set and the testing set. The training set is used to train the model, and the testing set is used to evaluate the model. This allows you to get a more accurate estimate of how well the model is performing.

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**Assignment Activity 11: Calculate a point estimate in the context of statistical inference and explain how that relates to the population quantity being estimated.**

A point estimate is an estimate of a population quantity that is calculated using a single observation. This observation is usually chosen at random from the population. Point estimates can be used to calculate confidence intervals and to perform tests of statistical significance.

The advantage of using a point estimate is that it is easy to calculate and it gives you a good idea of the direction of the population parameter. The disadvantage of using a point estimate is that it is not very accurate and it can be influenced by outliers.

The point estimate is important because it allows us to draw conclusions about population parameters even though we don’t have access to the entire population. It’s like a snapshot of the data that gives us an idea of what the population value might be. The point estimate can be influenced by the size of the sample, the distribution of the data, and how confident we want to be in our estimation.

**Assignment Activity 12: Accomplish all of the above-using workflows and communication strategies that are sensible, clear, reproducible, and shareable.**

When working with data, it is important to use workflows and communication strategies that are sensible, clear, reproducible, and shareable. This will help ensure that your results are accurate and can be reproduced by others.

- Sensible: Make sure your workflow makes sense for the task at hand. For example, if you are trying to find the average of a list of numbers, you would use a different workflow than if you were trying to find the median.
- Clear: Make sure your steps are easy to follow and that all of your assumptions are spelled out. This will help others understand what you are doing and how your results were obtained.
- Reproducible: Make sure your workflow can be easily reproduced by others. This will help ensure that your results are accurate and can be trusted.
- Shareable: Make sure your workflow is easy to share with others. This will help ensure that others can use your results in their own work.

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