Switch from Mechanical Engineering to Data Science in 8 Weeks

A simple 8-week plan to learn data science basics and build your first real project.

Weekly Hours: 10
Estimated Weeks: 8

Phases

Get Started with Data Science

Begin your transition by learning what data science is and why it matters. Get to know popular tools and basic concepts. This phase will prepare you for hands-on learning.

2 weeks

Understand what data science is
Learn about data science uses in real life
Install Python and data tools
Set up your project workspace

Basic Python (printing numbers, variables)
Data science foundations (finding trends)
Setting up workspace (Anaconda, Jupyter Notebook)
Researching case studies (e.g., bike-sharing analysis)

Intro to Data Science courses
Official Python Tutorial
Beginner Jupyter Guides
Data Science Case Study Playlists

Finish a basic Python exercise set (print, variables, input)
Set up Jupyter Notebook environment and run a demo notebook
Summarize uses of data science in engineering as a 1-page doc

Learn Data Wrangling and Exploration

Now, dive into handling real data. This phase covers exploring, cleaning, and understanding data sets. You'll practice with easy-to-find sample data.

2 weeks

Work with data files (CSV, Excel)
Clean and prepare messy data
Explore and visualize data
Summarize main discovery methods

Using pandas (read CSV, filter rows)
Simple data cleaning (drop empty rows, fix typos)
Making charts (matplotlib, bar graph)
Exploring data (mean, median, finding unusual values)

Pandas Documentation
Practical Data Cleaning Tutorials
Sample Data Sets
Beginner Data Visualization Tutorials

Create charts for a given dataset (matplotlib, Jupyter)
Clean a raw dataset and describe your steps (Markdown report)
Share a notebook showing basic summaries and at least one chart

Core Statistics and Problem Solving

Build your understanding of statistics. Learn how to spot patterns and use math to answer questions with data.

2 weeks

Review core statistics (mean, correlation, outliers)
Use data to form simple questions
Interpret charts and trends
Practice breaking down problems

Calculating mean/median (pandas functions)
Identifying correlations (scatter plots)
Spotting outliers (box plots)
Explaining findings (simple text summary)

Intro to Statistics Guides
Practical Data Analysis Examples
Data Question Workbooks
Chart Interpretation Tutorials

Analyze a dataset for correlations and make a scatter plot
Write a 1-page summary of findings using chart screenshots
Answer three example data questions using statistics in a notebook

Machine Learning Basics

Try simple machine learning. Learn how computers make predictions, train your first model, and review results.

1 weeks

Understand what machine learning is
Train and test a simple model
Interpret results of a model
Document your process

Using Scikit-learn (fit a model)
Making predictions (predict method)
Measuring accuracy (score method)
Basic model testing (split data)

Scikit-learn Quickstart
Beginner Machine Learning Courses
Sample ML Tutorials
Python ML Example Notebooks

Train a basic classifier (scikit-learn, e.g., Iris data example)
Write code to test and show model accuracy
Document how you trained and tested your model

Showcase Project: Engineering Data Dashboard

Bring it all together by making a simple, real-world demo. Build and share an interactive dashboard based on real engineering data, showing your data science skills from start to finish.

1 weeks

Pick and collect a simple engineering dataset
Clean, analyze, and visualize the data
Create an interactive dashboard
Test and share your results

Project planning (define scope)
Dashboard building (Streamlit app for pump failure prediction)
Testing data functions (run code with try/except)
Sharing project online (publish dashboard, README doc)

Streamlit Tutorials
Data Science Project Templates
Sample Engineering Data Sets
Testing and Documentation Guides

Create an interactive dashboard (Streamlit) using an engineering dataset
Write sample tests for dashboard data functions
Publish dashboard online and add a README with instructions
Earn a passing badge from basic auto tests (visible CI result)

Weekly Plan

Week	Focus	Why	Tasks	Deliverables
1	Understand data science basics and install tools	You need basics before hands-on work.	Watch videos on data science roles (YouTube), Install Python (Anaconda installer), Set up Jupyter Notebook (Anaconda Navigator), Try out basic Python examples (Jupyter), Write a short note on how data science is used in engineering	A running Jupyter Notebook with basic Python code, A 1-page reflection on data science for mechanical engineers
2	Practice simple Python and workspace tasks	Comfort with your tools makes learning easier.	Finish Python beginner exercises (variables, loops), Download and review engineering data sample (CSV file), Open data in Jupyter using pandas, Write code to print basic stats (mean, min, max), Summarize early ideas for a final project	A notebook with Python exercises and data summary, A short, informal project idea outline
3	Explore and clean real data files	Data cleaning is a key skill.	Load provided dataset in pandas, Find and remove missing data, Fix obvious data errors (e.g., wrong units), Make a bar chart (matplotlib), Document what you did as notebook comments	Cleaned dataset notebook with explanations, Chart images saved to your folder
4	Visualize data and explore patterns	Good visuals help you spot trends.	Make a histogram to show value spread, Make at least one more chart (scatter plot), Write a few sentences describing each chart, Research what similar problems others have solved, Plan 2–3 key questions your project can answer	Notebook with at least two charts and descriptions, List of project questions
5	Practice statistics and simple analysis	Statistics help answer questions objectively.	Calculate basic stats (mean, median, mode) in pandas, Find correlations between features (scatter plot, correlation score), Write a short summary of results, Brainstorm one way to improve your analysis	Notebook with calculated stats and correlation examples, A 1-page plain summary with suggestions
6	Try machine learning for the first time	Small ML models show data science in action.	Pick a simple ML problem (e.g., classify pump failures), Split dataset into train and test groups, Train a basic model (scikit-learn DecisionTreeClassifier), Check and print accuracy results, Write notes on what worked and what didn’t	Notebook with model training, predictions, and results, Lesson-learned notes
7	Build a simple engineering data dashboard	A project shows off your new skills.	Plan features for your dashboard (draw a layout on paper), Set up a Streamlit app (Python), Add data charts and model results, Write instructions and notes in your app, Test the app and fix errors	A working Streamlit app with charts and predictions, Written usage instructions
8	Test, document, and share your project	Testing and sharing show professionalism.	Write simple tests for your dashboard functions (assert statements), Set up online deployment (Streamlit Cloud or similar), Add a README with project details and instructions, Share the project link with a friend for feedback, Display test results (screenshot or CI badge)	Dashboard link (public, online), README file and visible test summary