Recalling the lesson in our 1st class last week, we reviewed the Intro to Business Analytics and the basic viewing properties of data in a table. Before the lesson took off, our professor asked us if we would see ourselves as a Data Analyst in the future. I answered yes but I’ll be just understanding the know-hows of data analysis so that I’ll just be receiving the reports considering I’m helping running our family business.
The first video introduced business analytics, its purpose, data sources, and terms. It emphasized transforming data into insights for decision-making, explained the wisdom hierarchy, showcased applications across industries, discussed historical development, roles of analysts, and the importance of skills. Tools, from coding-based software to graphical interfaces, were mentioned. Data exploration, preparation, and model building were also covered.
In the next video, it covers the foundations of business analytics, specifically the concepts of selecting, filtering, and sorting data. It explains the structure of data files, where variables (fields, attributes, columns) run horizontally and observations (records, tuples, rows) vertically. It advises on selecting relevant variables by avoiding sparsely populated or redundant fields. Variable types are discussed, including numeric (discrete and continuous) and categorical (text, Boolean) types, along with examples. The process of assigning proper data types and sizes to variables is outlined. Filtering is introduced as a method to omit specific rows of data based on conditions, with emphasis on understanding the level of detail and identifying duplicate observations. Sorting is explained as reordering data based on ascending or descending values of one or more fields.
The next video explores data preparation using formulas, highlighting Same Row and Multi Row types. It explains creating new variables from existing fields via math, string manipulation, date extraction, conditionals, and comparisons. Formulas perform arithmetic, text transformation, and date manipulation. They craft Boolean variables and handle multi-outcome cases. Multi Row formulas involve running totals, lag values, and window functions. The video also introduces table unions and joins, emphasizing primary and foreign keys and explaining inner and left outer joins.
The last video presented explains aggregation, including its alias as a pivot table. Aggregation involves summarizing data into lower dimensions by using operations like sum, average, and count. Basic aggregation can be performed on the table's level of detail. Examples show calculations for various questions, like units sold, total revenue, and averages, using formulas applied to fields. The video further discusses aggregation by dimensions like person, store, and product. Crosstabs, also known as pivot tables, are explained. They allow comparisons of measures across multiple dimensions, visually presented in a table format. Transposing is introduced as the opposite process, converting wide-format data into narrow format. These techniques help in summarizing, visualizing, and analyzing data more effectively.
These topics are just what we need to learn or recall in order to proceed in the future topics.
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