Week 1: Basic fundamentals
Day 1: Introduction to Data Analytics, Data Science, Data Engineering and AI.
Data is raw unorganized collection of facts, observations or symbols.
Types of data analytics and the data careers in this fields.
- Prescriptive Analytics: What should we do about it? This is a branch of data that deals with prediction of future outcomes but also recommends the optimal course of action to take.
- Descriptive Analytics: What happened? Summarizes raw historical data into easily readable metrics, charts and reports.
- Predictive Analytics: What is likely to happen? Use historical data alongside statistical models and machine learning algorithms to identify trends and to forecast future probabilities.
- Diagnostic Analytics: Why did it happen? Involves drilling down into historical data to identify the root causes of specific trends, anomalies, or performance dips.
Tools that were installed and their purpose in data.
DBeaver : This is a data base management tool.
Vscode: This is an intergrated development environment (IDE), this is where the coding takes place.
Python: This is the most preffered language for data analysis.
Github & Git: Git is a tool used to keep track of the changes made to a file and Github is the repository that has a copy of the files that are in the IDE.
Day 2: Dealing with DBMS & Git
I managed to connect two databases to DBeaver. The first was aiven- I had set up postgresql and the credentials were generated which was straighforward. The second was postgresql which was running locally on my pc, which required the database name - postgres, password and port: 5432
I already had a github account and it was connected to the git on my local machine. I managed to learn a few more git commands that I hadn't used before like git status.
