Knowledge Evaluation and Visualization Studying Reflection
As a part of my ongoing studying journey in knowledge evaluation, Iโve deepened my understanding of the essential distinction between categorical and numerical knowledge, which performs a big position in figuring out the evaluation and visualization strategies I exploit. Categorical knowledge, equivalent to gender, marital standing, or eye colour, entails classifying data into distinct teams, which are sometimes used for qualitative evaluation. However, numerical knowledge offers with portions and permits extra complicated mathematical operations, equivalent to calculating averages or different abstract statistics. The flexibility to distinguish between these knowledge varieties is crucial for selecting the suitable analytical strategies.
Iโve additionally expanded my abilities by studying purchase datasets from Kaggle, a superb platform for acquiring real-world knowledge. Navigating Kaggle has allowed me to observe downloading related datasets, reviewing their construction, and utilizing them for hands-on initiatives, which has been invaluable for gaining sensible expertise in knowledge evaluation.
As well as, I improved my proficiency in writing SQL queries via SQLiteOnline. This browser-based software permits me to execute SQL instructions with out establishing a neighborhood database. I practiced key SQL capabilities equivalent to `SELECT`, `JOIN`, `GROUP BY`, and `ORDER BY`, which have helped me extract, filter, and summarize knowledge effectively. Having the ability to rapidly check SQL queries on-line has offered a handy solution to improve my abilities.
One other thrilling software I began exploring is Google Gemini, a platform that integrates superior analytics and AI capabilities. It facilitates the seamless evaluation of enormous datasets and provides predictive analytics options. Whereas I’m nonetheless within the early levels of utilizing it, I can already see its potential in serving to me deal with extra complicated knowledge duties and predictive modeling. I look ahead to integrating Google Gemini into future initiatives.
Furthermore, I continued to refine my Excel abilities, specializing in key capabilities like `SUM` and `AVERAGE`. These capabilities have confirmed important for summarizing giant datasets and offering insights at a look. The `SUM` perform permits for fast calculations of totals, whereas the `AVERAGE` perform helps in figuring out tendencies and comparisons by calculating the imply. Mastering these capabilities has improved each my effectivity and accuracy in working with knowledge.
These foundational abilities shall be essential as I proceed to work on extra complicated initiatives and purpose to current knowledge in a transparent, significant, and impactful method.
Kingsley Onyekosor
Fellow ID: FE/23/62353521
Cohort 2 – Knowledge Analytics and Visualization
Studying Group: Rivers
ALC Quantum Enterprise Faculty
Week 13 Reflection
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