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Data Science Tutorial

43 topics · Free · Certificate on completion

Learn Data Science — from data collection and cleaning to modelling, visualisation and deployment.

Your learning path

Follow the path from a question to data cleaning, exploration, analysis, and communication. Pay attention to missing values, sampling, and the limits of the evidence.

Put the lessons into practice

Use a small dataset to document cleaning decisions and interpret summaries. Check units and denominators, distinguish correlation from causation, and explain uncertainty alongside the result.

What you'll learn

01.What is Data Science?02.Data Science Workflow 03.Types of Data 04.Data Collection 05.Data Cleaning 06.Exploratory Data Analysis (EDA)07.NumPy Basics 08.pandas Basics 09.Reading & Writing Data 10.Filtering & Sorting 11.GroupBy & Aggregation 12.Merging & Joining 13.Handling Missing Values 14.Data Visualization (matplotlib)15.Seaborn Basics 16.Descriptive Statistics 17.Probability Basics 18.Common Distributions 19.Hypothesis Testing 20.Correlation vs Causation 21.Sampling & Bias 22.Feature Engineering 23.Intro to Machine Learning 24.Regression vs Classification 25.Train / Test Split 26.Model Evaluation 27.Cross-Validation 28.Overfitting & Underfitting 29.Linear Regression 30.Logistic Regression 31.Decision Trees 32.Random Forest 33.Clustering with K-Means 34.Dimensionality Reduction (PCA)35.Time Series Basics 36.Intro to NLP 37.Intro to Deep Learning 38.Data Storytelling 39.Dashboards & BI Tools 40.SQL for Data Science 41.Model Deployment 42.Ethics & Privacy 43.Data Science Career