Turn raw data into clear, actionable insights with Python, pandas, and modern data tools.
Why This Workshop Matters
- The fastest-growing roles in AI and analytics all have one thing in common: they rely on Python for working with data. Recent analyses of job postings show that Python is explicitly mentioned in the majority of data scientist roles (around 70–80% of postings), confirming its position as the de facto language of data science.
- At the same time, the core “data science stack” has become remarkably consistent across top universities and platforms: Jupyter Notebooks, NumPy, pandas, Matplotlib/Seaborn, and scikit-learn. Almost every serious beginner-friendly curriculum builds on these tools, because they are exactly what practitioners use in real projects.
- Yet most professionals still live in spreadsheets and dashboards. They can look at reports, but not build the analysis themselves. This workshop is designed to close that gap: it gives participants a practical foundation in Python for data work, so they can move from “reading reports” to creating analyses, visualizations, and basic models on their own.
Call us to learn more about this python for data science workshop
Overview
Purpose
Teach participants how to use Python as a practical tool for data analysis: loading, cleaning, exploring, visualizing, and preparing data for machine learning.
Format
- 2-day, in-person, instructor-led workshop
- Highly interactive: short theory → live coding → guided exercises → mini-project
Level
- Beginner–friendly
- No prior Python or machine learning required
Core Tools
- Python + Jupyter Notebooks
- NumPy
- pandas
- Matplotlib / Seaborn
- Light touch of scikit-learn for a simple model
Who This Workshop Is For
This workshop is ideal for:
- Beginners and career-switchers who want a practical starting point in data and AI
- Students and fresh graduates in AI, CS, IT, business, or engineering
- Data / BI analysts who mostly use Excel or BI tools and want to move into Python
- Developers who know programming but are new to the data science ecosystem
- Product managers, consultants, and founders who want to understand data hands-on so they can work better with technical teams
If you can use a computer confidently and are comfortable with basic arithmetic, you’re ready.
What Participants Will Learn
By the end of the workshop, participants will be able to:
- Use Jupyter Notebooks to write and run Python code interactively
- Work with NumPy arrays for fast numerical operations
- Load data from CSV/Excel/JSON into pandas DataFrames and inspect it properly
- Clean and transform data: handle missing values, filter rows, create new columns
- Perform exploratory data analysis (EDA) to find patterns and answer simple questions
- Build clear visualizations with Matplotlib/Seaborn (distributions, trends, relationships)
- Prepare a dataset for machine learning and train a very simple model with scikit-learn
Most importantly, they leave with working notebooks and a small project they can reuse in interviews and real work.
Workshop Agenda
Day 1 – Python & Data Foundations
Session 1 – Getting Started with Python & Jupyter
- Why Python is the dominant language for data science and AI
- Intro to the environment: Jupyter Notebooks, cells, running code, saving work
- Python basics focused on data work: variables, lists, dictionaries, simple functions
Hands-on
- Run your first notebook
- Write small Python snippets to compute simple statistics
Session 2 – Numerical Computing with NumPy
- What arrays are and why they matter for data
- Creating, indexing, and slicing NumPy arrays
- Vectorized operations vs manual loops
Hands-on
- Create 1D and 2D arrays
- Replace a loop with a NumPy operation and see the speed and simplicity
Session 3 – Working with DataFrames in pandas
- Loading CSV/Excel/JSON files into DataFrames
- Inspecting data: head(), info(), describe()
- Selecting and renaming columns, fixing data types
Hands-on
- Load a real-world dataset (e.g. sales, HR, or public open data)
- Answer basic questions: row counts, missing values, column types
Session 4 – Data Cleaning & Simple Transformations
- Dealing with missing values: dropping vs filling
- Filtering rows with conditions
- Creating new features from existing columns (ratios, flags, date parts)
Hands-on
- Clean a messy dataset: fill or drop missing values, fix obvious errors
- Create derived columns such as total amounts, age groups, or time features
Day 2 – Exploratory Analysis, Visualization & Mini-Project
Session 5 – Exploratory Data Analysis (EDA)
- Descriptive statistics: mean, median, quantiles
- Grouping and aggregation with groupby
- Simple correlation checks
Hands-on
- Build an EDA notebook on a provided dataset
- Summarize the main patterns and answer 2–3 concrete questions (e.g. “Which region performs best?”, “Which category has the highest churn?”)
Session 6 – Data Visualization with Matplotlib & Seaborn
- Choosing the right chart: histograms, bar charts, line charts, scatter plots
- Plotting distributions and trends
- Adding titles, labels, and readable formatting
Hands-on
- Visualize key variables and their relationships
- Create at least one visualization that clearly answers a business-style question
Session 7 – From Data to a Simple Model
- Concept of features and target variables
- Train/test split in scikit-learn
- Training a simple regression or classification model
- Evaluating performance with a basic metric (e.g. accuracy or RMSE)
Hands-on
- Take a cleaned dataset
- Define features and target, split into train/test, train a simple model
- Print and interpret the evaluation metric
Session 8 – Mini-Project & Showcase
Participants work in pairs or small groups on a small end-to-end analysis:
- Load & clean their chosen dataset
- Perform EDA and summarize key findings
- Create 2–3 meaningful visualizations
- (Optional) Train a simple model if appropriate
Each group does a short, informal demo (3–5 minutes) showing:
- The question they tried to answer
- What they did in Python
- The main insight they discovered
This becomes their first portfolio-ready notebook in Python for Data Science.
Call us to learn more about this python for data science workshop
Example Hands-On Labs
On the website, you can highlight sample labs like:
- Sales Insights Lab – Clean a sales dataset, analyze revenue by product and region, and visualize trends over time.
- People Analytics Lab – Explore HR data to see how tenure, department, and salary relate to attrition.
First Model Lab – Use a small dataset to train a simple prediction model and see how model performance changes with different features.
Key Outcomes for Participants
After the workshop, participants will:
- Have a practical foundation in Python for data analysis, not just theory
- Be able to move beyond spreadsheets into real code-driven analysis
- Understand how to prepare data for machine learning and how this connects to more advanced AI/ML training
- Own several reusable notebooks and a mini-project they can show in interviews or use as starting points for future work
Be ready to continue into more advanced courses such as an AI & Machine Learning Fundamentals Bootcamp or specialized tracks (NLP, computer vision, MLOps)
FAQs
1. Do I need to know Python already?
Answer: No. We start from the basics and guide you through the concepts step by step. Curiosity and willingness to code are more important than prior experience.
2. Will this be very mathematical?
Answer: We use just enough math to make the ideas clear. The focus is on intuition and practice rather than heavy formulas
3. Is this only for people who want to become data scientists?
Answer: Not at all. It’s for anyone who wants to become more powerful with data: analysts, developers, product people, founders, and students.
4. How is this different from an AI / ML course?
Answer: This workshop focuses on Python + data: cleaning, analyzing, and visualizing. We only introduce a very simple model to show the next step. A dedicated AI/ML course goes much deeper into algorithms, model evaluation, and deployment.
5. What do I need to bring?
Answer: A laptop (if not provided by the organizer) and your interest in data. We provide datasets, environments, and all learning materials.
Ready to Take the Next Step?
- Schedule a call with our trainer
Haris Aamir
Trainer
Lincoln School