Micael Gustavsson @PyDevMG

I also completed the classical course "Supervised Machine Learning: Regression and Classification" by Andrew Ng at DeepLearning.AI

Been a long time since I posted something. Time to change that now. Since the last post I have: - Completed a ML project (linear regression) - Completed a dbt/BigQuery project (Data Engineering) - Completed Apache Spark SQL and Databricks course

Happy to have finished week 7, the final week of the course. It was an expansion about BigQuery Studio and what you can do with it in terms of AI (both assistant and ML/Python/Pandas/Notebooks). The last lab was locked due to an error, but had an excellent lab. Great course!!

Just finished week 6 of the course "BigQuery for Data Analysts" on Coursera. This was about how to use Dataform in order to create SQL data pipelines. Setting up repository, workspace, and giving out permissions. A bit on the Data Engineer side of things. But good to know.

Just finished week 5 of the course "BigQuery for Data Analysts" on Coursera. This time we get to catch a glimps of how to visualize data from BigQuery in Looker Studio. I already know Tableau, so Looker Studio wasn't that much different. However, probably takes time to master it.

Week 4 was finished and was about how to ingest New Datasets into BigQuery, i.e. to import and export dataset to BigQuery. I really injoy the labs that are hands-on and quite productive. Great tutorial with real-life examples. Great!

So, I've finished week 2 and week 3. Week 2 is about getting to know the BigQuery user interface and start running SQL queries to understand and prep your data. Week 3 is about data integrity and what tools you can use - all Google Cloud of course :) I've really enjoyed it.

The first module of BigQuery for Data Analyst is mostly selling the EDW platform. Nevertheless, good to get an overview of it. Later on, in module 2, you get to see the UI of BigQuery and start using fairly advanced SQL queries. Nice tutorial.

I just recently started up a new course to delve into Google's BigQuery Enterprise Data Warehouse and their Looker Studios. Excited to check it out to see Google's side of things in the Data Analytics world. coursera.org/learn/bigquery…

Sooo, been awhile since I posted. I finished the course in Statistics and I have moved on to a computer science course in Algorithms while reading up on old course material in linear algebra. It's really cool to go back and look at it with mature eyes :) coursera.org/learn/algorith…

I went back to the statistics course, coursera.org/learn/stanford…, to really understand the topic. I feel I have a much better understanding about Bayesian Analysis/distribution, sampling, SE, and the Central Limit Theorem. Now, off to look at week 4 again. Then it's off to week 5!

I'm looking to do some fintech projects to add to my portfolio, and I'm looking at "Business and Financial Modeling Specialization" at Wharton or similar. Maybe adapt it on my own to Python instead of spreadsheets. But I'll finish statistics first. coursera.org/specialization…

I'm also going to take some time to improve existing projects and add some new ones in my project portfolio at intrepidev.wixsite.com/intrepidev

Been busy lately with recruitment (with me as a candidate). But with half of the statistics course done, I'm thinkning about going back and revisit these weeks to firmly get an even better grip on the material. Maybe restructure some thoughts and concepts.

Week 4 done. Difficult test at the end, but managed to understand it finally after some help by ChatGPT. Week 5 is about confidence intervals and tests of significance (p-values, t-tests, two-sample z-test). Looking forward to it!

Finshed week 3 in style ;) Week 4 is about prediction, correlation, regression, and residuals. Should be fun (tomorrow). Now, time for a well deserved break.

So, week 2 is done. Good information. I liked it. Now on to week 3. It will be about Normal Approximation, Binomial Distributions, Sampling and the Central Limit Theorem, and errors. Should be a good time!

In week 2, the focus is on biases in sampling, probabilities, including Bayes' theorem. Should be interesting!

So, week 1 of the course "Introduction to Statistics" was fairly basic. It introduced the basic descriptive statistical measures such as mean, median, but also talked about the importance of visualizing statistics. Got to see the boxplot or the Box-and-Whisker plot. Good stuff!

10) Analysis of Categorical Data 11) One-Way Analysis of Variance (ANOVA) 12) Multiple Comparisons That's it. Should be fun!