SCA MENTORING PROGRAM: WHAT I’VE LEARNT IN JUST A MONTH

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I’m participating in She Code Africa — Admin mentoring program, data science learning path. I hope that at the end of the three months program I would be skilled to find a junior data science job and also be confident to learn independently. I’ll be briefly sharing what I have gained in just the first month.

The first month was an introduction to fundamental tools that we would dwell on in the following months. These tools included SQL, tableau, python and git.

SQL

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SQL is fully defined as structured query language and it is the most popular query language used to manipulate databases. Databases contain large amounts of data stored on disk. SQL makes it easy to query data without necessarily writing codes. I focused on MySQL which is a lightweight database.

Fun-fact: MySQL DBMS is included in python module.

These are the summaries of the areas I touched on SQL:

1. Learnt basic syntax of MySQL; filtering, aggregating and sorting results.
2. Learnt how to perform summary statistics on databases using MySQL.
3. Learnt how to query data using MySQL.
4. Learnt how to join multiple tables using MySQL.
5. Learnt to create temporary tables using with.
6. Learnt how to write conditional statements on MySQL.

The project I worked on using MySQL: Performed query on chinook music database

TABLEAU

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Tableau is basically a visualization tool. It’s a great means of visualizing a discovery you made from manipulating some sets of data for others to see and understand. Summary statistics aren’t enough to recognize the similarities or contrast between data. In fact, I learnt how four datasets had the same mean, standard deviation and line of best fit yet had different curves when plotted. This phenomenon is called Anscombe’s Quartet. Hence the importance of plotting your data. Also, there’s power in forming a story from your data. The clip below is a solid illustration of this.

Hans Rosling’s 200 Countries, 200 Years, 4 Minutes — The Joy of Stats

These are the summaries of the areas I touched on Tableau:

1. The power of telling a story using data visualization.
2. Why visualization is important.
3. How to have a good visual.
4. Explained exploratory data and explanatory data.
5. Learnt the different types of data and the best visuals to represent them.
6. Learnt the best visuals to represent contrasting data types i.e contrast between categorical and numeric data.
7. Learnt how to build visuals and create a story on tableau.

The project I worked on using tableau: visualized us demographics

PYTHON

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Python is one of the most widely used tools in data science. It is also one of the most loved programming languages. This is probably because it has a lot of libraries that make visualizing, manipulating, performing statistical calculations or cleansing data procedures easy.

These are the summaries of the areas I touched on python:

1. python’s syntax.
2. Python’s data types.
3. Python’s operators.
4. Python’s control flow.
5. Python’s conditional statements.
6. Python’s functions.

The project I worked on using Python: wrote two programs that used the random library.

GIT

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Git is a means of saving different versions of your software project on a remote site or local repo. Github is a platform that hosts git repositories. There are a lot of advantages of using git and GitHub. One is that you can save your project on a remote site i.e a location different from your local computer. This would allow you to have access to your project if your laptop becomes unavailable. Another is that using git would grant you access to different versions of your project so that you can easily fall back on an old version of your code if the edited one becomes invalid. Also, git is useful when you’re working on a project with a large number of contributors all over the world.

These are the summaries of the areas I touched on git:

1. Learnt about git commit.
2. Learnt about git repository.
3. Learnt about git remotes.
4. Learnt about git hash.
5. Learnt about branches on git.
6. Learnt about how to deal with merge conflicts.

The projects I worked on using git: I pushed my three previous projects to GitHub using the knowledge I gained about git.

In the next month, I should learn how to use python’s libraries: NumPy, pandas, matplotlib and seaborn to analyze and visualize data. Finger-crossed that I’ll fulfil this.