Premium career track · Coming soon
Data Scientist Career Track
From your first line of Python to machine learning models and dashboards that answer real business questions.
- 19 modules
- 111+ lessons
- 6 modules free today
- 🎓 certificate
Get the early-bird price when it opens. Start the free modules today.
What you will be able to do
- Clean, explore and visualise any dataset with Pandas, Matplotlib and Seaborn
- Use statistics to test ideas instead of guessing
- Build, evaluate and improve machine learning models with scikit-learn
- Query data with SQL and present it in Power BI dashboards
- Finish 6 portfolio projects you can show in interviews
Syllabus
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1 Python for Data Science ✅ Free now
Variables, data types, loops, functions, files and errors: the Python you need every day.
Start the free course (20 lessons) ❯ -
2 NumPy: fast numbers ✅ Free now
Arrays, shapes, slicing, broadcasting and vectorised maths.
Start the free course (6 lessons) ❯ -
3 Pandas: data analysis ✅ Free now
Load, filter, clean, group, join and reshape tables of data.
Start the free course (9 lessons) ❯ -
4 Data Visualization ✅ Free now
Charts that tell a story with Matplotlib and Seaborn.
Start the free course (5 lessons) ❯ -
5 Machine Learning Basics ✅ Free now
What a model is, train/test split, your first regression and classifier with scikit-learn.
Start the free course (5 lessons) ❯ -
6 SQL for Data Analysis ✅ Free now
SELECT, JOIN, GROUP BY and window functions for analytics.
Start the free course (15 lessons) ❯ -
7 Statistics and Probability ⭐ Premium · Ready
Averages and spread, probability and Bayes, the normal curve, confidence intervals, p-values and correlation: 6 animated lessons with tested code.
- Mean, median, spread and the five-number summary
- Probability, conditional probability and Bayes’ theorem
- Distributions: binomial, normal and the central limit theorem
- Sampling and confidence intervals
- Hypothesis tests: z, t, chi-square and p-values
- Correlation vs causation
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8 Exploratory Data Analysis and Feature Engineering ⭐ Premium · Ready
EDA checklist, missing values and outliers, encoding and scaling, and feature engineering: 4 animated lessons with runnable, tested code.
- An EDA Checklist for Any New Dataset
- Missing Values and Outliers: Find, Decide, Fix
- Encoding Categories and Scaling Numbers
- Feature Engineering: Creating Features Models Can Learn From
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9 Machine Learning in Depth ⭐ Premium · Ready
Regression, trees and boosting, k-NN, Naive Bayes, SVM, clustering, PCA, metrics, tuning, imbalance and explainability: 7 animated lessons with runnable, tested code.
- Linear and Logistic Regression, Step by Step
- Decision Trees, Random Forests and Gradient Boosting
- k-Nearest Neighbours, Naive Bayes and SVM
- Clustering with k-Means and DBSCAN
- PCA and Dimensionality Reduction
- Metrics, Cross-Validation and Hyper-Parameter Tuning
- Imbalanced Data and Model Explainability
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10 NoSQL with MongoDB ⭐ Premium · Ready
Documents and data modelling, CRUD with PyMongo, indexes and aggregation pipelines: 3 animated lessons with runnable, tested code.
- NoSQL and MongoDB: Documents, Collections and JSON
- CRUD and Queries with PyMongo
- The MongoDB Aggregation Pipeline
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11 Dashboards with Power BI ⭐ Premium · Ready
Data models and Power Query, DAX measures, and interactive reports with slicers and drill-through: 3 animated lessons with runnable, tested code.
- Power BI: Connecting Data and Building the Data Model
- Power BI Measures and DAX Basics
- Interactive Power BI Reports: Slicers, Drill-Down and Design
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12 Time Series Forecasting ⭐ Premium · Ready
Trend and seasonality, stationarity and differencing, ARIMA and honest forecast evaluation: 3 animated lessons with runnable, tested code.
- Time Series: Trend, Seasonality and Moving Averages
- Stationarity, Differencing and Autocorrelation
- ARIMA Forecasting in Python
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13 Big Data with Apache Spark ⭐ Premium · Ready
Why big data needs Spark, DataFrames and Spark SQL, and machine learning with MLlib: 3 animated lessons with runnable, tested code.
- Big Data: Why One Computer Isn't Enough (Hadoop, MapReduce, Spark)
- PySpark DataFrames and Spark SQL
- Machine Learning at Scale with Spark MLlib
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14 Deep Learning Foundations ⭐ Premium · Ready
Neurons and layers, your first Keras network, CNNs for images, RNNs and LSTMs, and PyTorch: 5 animated lessons with runnable, tested code.
- Neurons, Layers and How a Neural Network Learns
- Your First Neural Network with Keras (TensorFlow)
- CNNs for Images: Convolution, Pooling and Transfer Learning
- RNNs and LSTMs for Sequences
- The Same Models in PyTorch
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15 NLP: Working with Text ⭐ Premium · Ready
Cleaning and tokenising, TF-IDF and similarity, sentiment analysis and transformers: 3 animated lessons with runnable, tested code.
- NLP Basics: Cleaning and Tokenising Text
- TF-IDF, Text Similarity and Search
- Sentiment Analysis: From TF-IDF to Transformers
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16 Computer Vision ⭐ Premium · Ready
Images as arrays, filters, edges and contours, and object detection with YOLO: 3 animated lessons with runnable, tested code.
- Computer Vision Basics: Images as Arrays
- Filters, Edges and Contours
- Object Detection with YOLO
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17 Reinforcement Learning Basics ⭐ Premium · Ready
Agents, rewards and exploration, and Q-learning you can watch learn: 2 animated lessons with runnable, tested code.
- Reinforcement Learning: Agents, Rewards and Environments
- Q-Learning on a Grid World
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18 MLOps: Deploying Models ⭐ Premium · Ready
Model APIs with FastAPI and Docker, cloud ML platforms, and monitoring for drift: 3 animated lessons with runnable, tested code.
- Serving a Model with FastAPI and Docker
- Cloud ML: AWS SageMaker and Azure Machine Learning
- Monitoring Models in Production
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19 Portfolio Projects ⭐ Premium · Ready
Six end-to-end projects: sales analysis, churn, house prices, recommender, forecasting and an image classifier: 6 animated lessons with runnable, tested code.
- Project 1: Sales Performance Analysis and Dashboard
- Project 2: Customer Churn Prediction
- Project 3: House Price Prediction
- Project 4: Movie Recommendation System
- Project 5: Retail Sales Forecasting
- Project 6: Image Classifier with Transfer Learning