Shin Nix

It has been quite a while since I wrote an update regarding my Coursehero tutor account. I am personally grateful for the platform as it gave me the opportunity to earn during the trying times of the pandemic and provided the means for me to improve my work-from-home setup. I still remember working with just my laptop sitting on a plastic chair answering questions whenever I have the opportunity. I loved the flexibility and the nature of the work where I got to select the questions that I would answer based on my expertise. It served as an avenue for focused work. During my stint as a tutor, frustrations motivated me to study coding, especially statistics and probability problems that get asked quite often with just different givens. This prompted me to enroll in a Udemy Course entitled, "R Programming for Statistics and Data Science". Using this knowledge, I was now able to answer questions in Statistics and Probability that require R as a programming language.  Then, I stud

In this lesson, the topic discussed was regarding drill-downs. I can compare it to a "Prezi" equivalent in Power BI. In Prezi, we can zoom in and out of a powerpoint presentation to see details encapsulated in a mother topic. In drill downs, we are shown a summarized graph of the data but if we want to know some of the nitty-gritty stuff within that graph, we can "drill down" further to see the specifics within that summary. As an example, we have the following summarized visualization for the Price by Product and Store. If we activate the drill-down icon and click one of the bars i.e. Costco, we will be able to see the total price of the products that were bought at Costco as shown below. We can further drill down on specific quarters to see how the product sold in those quarters. In this case, more purchases of bottled water were recorded during the first quarter. Note: Drill-downs are specifically useful when conducting presentations and stakeholders want to know

In this blog, I will be changing my approach to how I write the blog. In the previous blogs on Power BI, I was simultaneously doing what Alex is doing in Power BI and then, whenever I encounter issues, I will be writing it directly in the blog and then continued where I left off with the video once I am done. Here, I will watch the video first, take note of important keywords and lessons that Alex emphasizes, and then replicate what he did on the video. This lesson focuses on DAX or Data Analytics eXpressions. Based on what I understood on the video, DAX can be likened to the formulas associated with MS Excel. And, since Power BI is also a Microsoft product, there are DAX that are similar, if not, the same with what can be found in MS Excel (One example is the IF expression in Power BI). In this exercise, the process of using DAX was explained starting with creating "New Measure" and then using various DAX to be able to visualize the intended outcome in a table. For example,

I am now in the third tutorial on Power BI. In this lesson, the topic is on creating and managing relationships. Important concepts that were introduced are cardinality i.e. many-to-1 (*-1) or (one-to-many (1-*), cross-filter where if we select "Both", this means that we can think of many separate tables as one table. I have these three separate tables. Apocalypse Sales: Apocalypse Store: Customer_Information: Notice that each of the values in the Customer ID column of the third table can be seen in the Cust ID column of the first table. But notice that there are only four rows in Customer_Information table while there are many rows in the Apocalypse Sales table. The cardinality of the relationship means having unique or multiple instances per value for the joining field between two tables. This means that the cardinality of the relationship from Apocalypse Sales and Customer_information in terms of Customer ID = Cust ID  is Many-to-one. Conversely, the cardinality of the rel

Power Query is a  preliminary step in subjecting the raw data to Power BI to create the necessary visualizations. In Power Query, we can transform the data, rename/add/delete columns, change data type among other things. To be familiar with Power Query, we will convert the following Summary Table to the Purchase Tracker table right below it. Summary Table: Purchase Tracker: The first thing that we will do is to import the data from Excel to Power BI. We will only select the "Pivot Table" and the "Purchase Overview" sheets and then click "Transform" so that we will be led into the Power Query Editor. In this particular exercise, we will do most of the transformations on the Purchase Overview sheet and the reason we included the Pivot table is to serve as a reference. We will now perform data cleaning by removing rows that have "null" in its entries. Performing the steps that were outlined on the video, we were able to transform the Purchase Overvi

Just want to pass by here and update you regarding learning about the data visualization tool - Power BI. By the way, BI is an acronym for business intelligence. I am using the YouTube videos from Alex the Analyst. Hopefully, this works out. I am already finished with video #1 and appreciating how this tool was able to create visualizations (graphs) just from clicks and a few editing and navigation in the software. Currently, I realized that the ones presented can still be done using MS Excel although the same graphs could be quite tedious in Excel than in this platform.

  Challenge: Clothing Alterations Given data: CREATE TABLE clothes (     id INTEGER PRIMARY KEY AUTOINCREMENT,     type TEXT,     design TEXT);      INSERT INTO clothes (type, design)     VALUES ("dress", "pink polka dots"); INSERT INTO clothes (type, design)     VALUES ("pants", "rainbow tie-dye"); INSERT INTO clothes (type, design)     VALUES ("blazer", "black sequin"); Step 1 We've created a database of clothes, and decided we need a price column. Use ALTER to add a 'price' column to the table. Then select all the columns in each row to see what your table looks like now. Code: ALTER TABLE clothes ADD price REAL; SELECT * FROM clothes; Query Results: id type design price 1 dress pink polka dots NULL 2 pants rainbow tie-dye NULL 3 blazer black sequin NULL Step 2 Now assign each item a price, using UPDATE - item 1 should be 10 dollars, item 2 should be 20 dollars, item 3 should be 30 dollars. When you're done,

So far, we are only "reading" from a given database or table using the SELECT command of SQL. In today's lesson, we will now start "writing" into a given database using the UPDATE and DELETE commands. Challenge: Dynamic Documents Given data: CREATE table documents (     id INTEGER PRIMARY KEY AUTOINCREMENT,     title TEXT,     content TEXT,     author TEXT);      INSERT INTO documents (author, title, content)     VALUES ("Puff T.M. Dragon", "Fancy Stuff", "Ceiling wax, dragon wings, etc."); INSERT INTO documents (author, title, content)     VALUES ("Puff T.M. Dragon", "Living Things", "They're located in the left ear, you know."); INSERT INTO documents (author, title, content)     VALUES ("Jackie Paper", "Pirate Recipes", "Cherry pie, apple pie, blueberry pie."); INSERT INTO documents (author, title, content)     VALUES ("Jackie Paper", "Boat Supplies&quo

Challenge: Famous People Instructions: In this project, you’re going to make your own table with some small set of “famous people”, then make more tables about things they do and join those to create nice human-readable lists. For example, here are types of famous people and the questions your data could answer: Movie stars: What movies are they in? Are they married to each other? Singers: What songs did they write? Where are they from? Authors: What books did they write? Fictional characters: How are they related to other characters? What books do they show up in? Created Tables: /* Create table about the people and what they do here */ CREATE TABLE famous_people (id INTEGER PRIMARY KEY AUTOINCREMENT, first_name TEXT, last_name TEXT); INSERT INTO famous_people(first_name,last_name) VALUES ("Amber", "Heard"); INSERT INTO famous_people(first_name,last_name) VALUES ("Johnny", "Depp"); INSERT INTO famous_people(first_name,last_name) VALUES ("An

Challenge: FriendBook Given: CREATE TABLE persons (     id INTEGER PRIMARY KEY AUTOINCREMENT,     fullname TEXT,     age INTEGER);      INSERT INTO persons (fullname, age) VALUES ("Bobby McBobbyFace", "12"); INSERT INTO persons (fullname, age) VALUES ("Lucy BoBucie", "25"); INSERT INTO persons (fullname, age) VALUES ("Banana FoFanna", "14"); INSERT INTO persons (fullname, age) VALUES ("Shish Kabob", "20"); INSERT INTO persons (fullname, age) VALUES ("Fluffy Sparkles", "8"); CREATE table hobbies (     id INTEGER PRIMARY KEY AUTOINCREMENT,     person_id INTEGER,     name TEXT);      INSERT INTO hobbies (person_id, name) VALUES (1, "drawing"); INSERT INTO hobbies (person_id, name) VALUES (1, "coding"); INSERT INTO hobbies (person_id, name) VALUES (2, "dancing"); INSERT INTO hobbies (person_id, name) VALUES (2, "coding"); INSERT INTO hobbies (person_id

Challenge: Sequels in SQL Given data: CREATE TABLE movies (id INTEGER PRIMARY KEY AUTOINCREMENT,     title TEXT,     released INTEGER,     sequel_id INTEGER); INSERT INTO movies      VALUES (1, "Harry Potter and the Philosopher's Stone", 2001, 2); INSERT INTO movies      VALUES (2, "Harry Potter and the Chamber of Secrets", 2002, 3); INSERT INTO movies      VALUES (3, "Harry Potter and the Prisoner of Azkaban", 2004, 4); INSERT INTO movies      VALUES (4, "Harry Potter and the Goblet of Fire", 2005, 5); INSERT INTO movies      VALUES (5, "Harry Potter and the Order of the Phoenix ", 2007, 6); INSERT INTO movies      VALUES (6, "Harry Potter and the Half-Blood Prince", 2009, 7); INSERT INTO movies      VALUES (7, "Harry Potter and the Deathly Hallows – Part 1", 2010, 8); INSERT INTO movies      VALUES (8, "Harry Potter and the Deathly Hallows – Part 2", 2011, NULL); Step 1 We've created a table with