Data Science Fundamentals
Data science is mostly careful thinking with numbers. This course takes you through the whole workflow in plain words: turning a vague request into a question data can answer, finding and cleaning data, describing it with the right summary, choosing honest charts, telling correlation from causation, reading a simple A/B test and understanding what a first predictive model really does. Examples work in a spreadsheet, with optional short Python snippets for those who want to try code. It ends with a written plan for your first end-to-end project.
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What you will be able to do
- Turn a vague request into a clear, measurable question
- Clean a messy dataset and describe it with the right summaries
- Choose charts that tell the truth and explain what they show
- Plan a small end-to-end project from question to one-page report
Who this course is for
- Analysts and office workers who live in spreadsheets and want to go further
- Career changers who want an honest first look at data science
- Managers who need to read and question data reports with confidence
The 8 lessons
In a deliberate order. Each lesson explains one topic in plain language, gives you a template or a table you can use, and ends with a short assignment on your own work.
- 01
What data science is, and what it is not
You will be able to describe the six steps of a data project, tell the main data roles apart and set up a simple way to follow this course.
- 02
Asking good questions and finding data
You will be able to turn a vague request into a measurable question, recognize the main data types, lay a table out in tidy form and check whether you may use a dataset.
- 03
Cleaning data you can trust
You will be able to find and fix duplicates, missing values, inconsistent categories, messy dates and mixed units, and to check outliers instead of deleting them, in a spreadsheet or with pandas.
- 04
Describing data: averages, spread and groups
You will be able to summarize a numeric column with the right average and a measure of spread, read the shape of a distribution and build group summaries with a pivot table or pandas.
- 05
Visualizing data clearly and honestly
You will be able to choose the right chart for a question, set honest axes, remove clutter, label properly and use color to carry meaning, in a spreadsheet or with matplotlib.
- 06
Relationships, causes and simple experiments
You will be able to tell correlation from causation, spot likely confounders, plan a simple A/B test and explain why a statistically significant result may still not matter.
- 07
A first look at prediction models
You will be able to explain what a model is, tell regression from classification, test a model fairly, read error and accuracy sensibly and run a tiny scikit-learn example.
- 08
Your first end-to-end data project
You will be able to plan, run and present a small data project from question to one-page report, using a reusable plan table and an honesty checklist.
What is included
- 8 complete written lessons, about 102 minutes of reading in total
- 8 templates, checklists and plans in clear tables
- 5 short code examples you can run, all optional
- A practical assignment at the end of every lesson
- A written plan or template to keep in the final lesson
- Printable, or save any lesson as PDF
- Progress tracking in your library
- Lifetime access, including future updates
How you receive it
Right after payment you see your access button, and the same personal link is emailed to you. Open it on any device to read every lesson. There is no account and no password to remember.
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Education, not professional advice. Our courses are general education in written form. They do not give legal, tax, financial or other professional advice, do not lead to an accredited certificate and do not guarantee any job, income or result. Read the educational disclaimer.
Want all five courses?
The Complete Library includes this course and the other four, 40 lessons in all, for $147. That saves $88 against buying them one by one.
