Beginner Roadmap: Land Your First Data Scientist Job

Step-by-step plan to help students with no experience get their first data science job, focusing on skills, a showcase project, and job application basics.

Weekly Hours: 10
Estimated Weeks: 8

Phases

Phase 1: Understand Basics and Tools

Start with the background you need for data science. Get comfortable with simple statistics, Python programming, and using Jupyter notebooks. Try out basic data exploration using real-life datasets.

2 weeks

Learn basic statistics concepts
Write simple Python code
Use Jupyter notebooks for data tasks
Read and explore datasets

Calculate mean/median/mode (on a small CSV file)
Write loops and functions in Python (e.g., sum a list)
Import and view data (pandas, Jupyter)
Draw charts (matplotlib for bar chart)

Intro to Python (video or textbook)
Online notebook tool
Data Science for Beginners tutorial
Simple dataset samples

Complete one data summary in Jupyter notebook (on iris.csv)
Share a notebook showing stats and chart (uploaded to GitHub)
Write code to load and inspect data (pandas, example: print first five rows)
Explain mean/median/mode in writing (one example per stat)

Phase 2: Dig Into Data & Learn Libraries

Move deeper into working with and cleaning data. Start learning about important Python libraries like pandas and scikit-learn. Practice exploring and changing real datasets.

2 weeks

Handle missing values in data
Combine and filter datasets
Use pandas for common tasks
Try scikit-learn basics

Clean missing values (e.g., fillna in pandas)
Select rows by condition (example: age > 30)
Build basic model (scikit-learn for linear regression)
Split data into train/test sets

pandas documentation
Beginner scikit-learn guide
Hands-on cleaning tutorial
Open dataset for practice

Clean and prepare a used car price dataset (Jupyter, pandas)
Build a simple prediction model (scikit-learn LinearRegression)
Share notebook with clean code and comments (GitHub)
Describe changes made to data (written summary)

Phase 3: Build and Test a Real Project

Start a real, small project to show your skills. Practice understanding a problem, building a model, and sharing your results. Use what you've learned so far.

2 weeks

Pick and plan a showcase project
Apply data cleaning and modeling
Check and improve your model
Test your work

Document your steps (write clear README)
Tune model settings (change model parameters, e.g., alpha in Ridge)
Run model evaluation (e.g., accuracy or RMSE)
Write tests for functions (pytest)

Sample data science projects
Model evaluation guide
Python testing tutorial
GitHub workflow basics

Publish a complete project notebook (predict used car prices)
Add test scripts for core functions (pytest, example: data cleaning step)
Write and upload README (explain: problem, steps, results)
Show model results with score or chart

Phase 4: Share and Apply for Jobs

Learn how to show your work and apply for junior data science roles. Build a resume, prepare for common interview questions, and set up an online portfolio.

1 weeks

Update your resume
Create or polish your LinkedIn profile
Practice talking about your project
Apply to real entry-level jobs

Write a clear project summary (1 paragraph)
List real skills on your resume (pandas, regression, cleaning)
Answer basic interview questions (example: 'Describe missing data problems')

Resume guide for data jobs
LinkedIn profile tips
Entry-level job boards
Interview question lists

Publish GitHub repo with README, notebook, and tests (public, with CI badge)
Complete at least 3 job applications (screenshot each submission)
Record 1-minute project summary video (YouTube or Loom link)

Phase 5: Reflect and Improve

Take time to look back at what you learned. Find gaps in your skills and plan your next steps. Practice feedback by asking a friend or mentor to review your work.

1 weeks

Review and update your showcase project
Gather feedback from others
Identify skill gaps
Set a personal learning goal

Organize and update code (refactor one function for clarity)
Respond to feedback (edit README after review)
Plan future learning (list one area, e.g., deep learning basics)

Code review checklist
Peer feedback forums
Personal progress tracker

Revise project or README using feedback (commit changes to GitHub)
Write short reflection (100 words, what went well and area to improve)

Weekly Plan

Week	Focus	Why	Tasks	Deliverables
1	Learn Python and Basics of Statistics	You need these to use data and tools confidently.	Complete basic Python exercises (variables, if statements, for loops), Read about mean, median, mode (Statistics 101 article), Install and open Jupyter notebook (Anaconda or online), Practice small statistics examples in notebook (calculate average age in a list)	Python summary notebook (mean, median, mode on iris.csv), Screenshot of Jupyter notebook running
2	Explore Data in Jupyter and Make Simple Charts	It's important to see and understand data visually.	Load sample dataset in notebook (iris.csv, pandas), Explore dataset with pandas (head, describe functions), Create simple charts (matplotlib, bar and scatter plot), Write a short summary of observations	Notebook with 2 charts and short summary (uploaded to GitHub), List of three findings from dataset
3	Clean and Prepare Real-world Data	Real data is often messy and needs fixing.	Download used car prices dataset (from Kaggle or similar site), Identify missing or incorrect values (pandas isnull, info), Fix or fill missing values (fillna or dropna in pandas), Save cleaned dataset	Cleaned dataset CSV, Notebook summarizing cleaning steps and code
4	Build and Test a Simple Model	Models help you make predictions or insights from data.	Split dataset into training and test sets (scikit-learn train_test_split), Train simple model (LinearRegression), Make predictions and check results (e.g., RMSE, accuracy), Write and run basic code test (pytest, one cleaning function)	Notebook with model and accuracy score, Test result output (pytest pass/fail log)
5	Enhance Project and Add Clear Explanation	A clear explanation makes your project easy to understand.	Document each step in the notebook (add markdown cells), Write project README (problem to solution summary), Check code for clarity (refactor as needed), Push all work to GitHub	GitHub repo link with project and README, README with steps and sample results
6	Test and Share Project Publicly	Public projects show you can finish and present work.	Add more tests for functions (pytest), Set up free continuous integration (Github Actions for running tests), Send project link to a peer for feedback, Update README with test badge	CI badge shown on GitHub README, Test summary (screenshot or log file)
7	Prepare for Job Search	You need to show and talk about your skills.	Update your resume with project and skills, Polish LinkedIn profile (add project link), Practice explaining your project in simple terms, Apply to three entry-level data jobs	Resume PDF with data science project, Proof of job applications (screenshots or list)
8	Reflect, Get Feedback, and Plan Next Steps	Looking back and improving is key to ongoing growth.	Ask mentor or friend to review your GitHub project, Revise project or README after review, Write a short reflection on what you learned, List one skill to learn next	Short reflection note (100 words), Updated README or code based on feedback

Daily Plan

Monday

Watch 20 minutes of intro video (Python, statistics basics)
Do a small coding task in notebook (write function for mean)
Read one article on data exploration

Tuesday

Practice coding with pandas (load dataset, view head/tail)
Try drawing a bar chart (matplotlib)
Summarize data findings in your notes

Wednesday

Clean data in notebook (fill or drop missing values)
Write code to select specific data (filter for values > 1000)
Save your cleaned data file

Thursday

Split cleaned data into train/test sets
Train a simple model (LinearRegression in scikit-learn)
Write one code test (pytest for cleaning function)

Friday

Update project README (describe project goal and steps)
Push latest code to GitHub
Review and comment on a peer’s project (if possible)