Python for Data Science

Python for Data Science

Duration: 5 Days

Description

Five days is enough to go from “I know Python basics” to “I can run a full data science workflow,” and this course is structured to do exactly that without wasted motion. It moves through the standard data science stack in the order you’d actually use it on a real project: NumPy for numerical computation, Pandas for the cleaning and manipulation work that consumes most of a real analyst’s time, Matplotlib and Seaborn for turning results into something a stakeholder can act on, and then into applied machine learning with Scikit-learn — regression, classification, decision trees, clustering — along with the model evaluation and hyperparameter tuning skills needed to know whether a model is actually good. A closing module on data ethics and bias reflects a genuine industry expectation now, not an afterthought, and completing this course also prepares participants for the PCAD™ Certified Associate in Data Analytics with Python certification.

Audience

While this course is commonly taken by analysts and aspiring data scientists, it’s written for developers specifically — the machine learning and modeling modules assume you’re comfortable programmatically, not just conceptually, with the material. For a software developer, the payoff is concrete: the ability to build data-driven features directly into an application instead of handing that work off to a separate analytics team, and a credible path toward data engineering or ML engineering roles that most job postings gate behind exactly this skill set. Developers already comfortable with core Python who want the full analytical toolchain — from raw data to a working, evaluated model — in one structured week will get the most out of this course.

Objectives

  • Utilize Python Fundamentals
  • Manipulate Data with the NumPy Library
  • Analyze Data with the Pandas Library
  • Visualize Data using the Matplotlib and Seaborn Libraries
  • Understand the Fundamentals of Machine Learning using the Scikit-learn Library
  • Appreciate the Differences Between Machine Learning and Deep Learning
  • Use Data Wrangling and Cleaning
  • Evaluate and Optimize Learning Models using Hyperparameter Tuning
  • Acknowledge Data Ethics and Bias

Prerequisites

Participants should have a fundamental understanding of Python programming, including variables, data types, loops, functions, and conditional statements. Familiarity with basic mathematical concepts like algebra, calculus, and statistics will aid in understanding data analysis and machine learning concepts. While not mandatory, prior exposure to Jupyter Notebooks, a popular data science development environment, would be beneficial for a smoother learning experience. This course assumes no prior knowledge of data science, making it suitable for beginners in the field.

Related Python Courses

See the full Python training roadmap and course directory for how this fits into a broader learning path.

Want a lighter, faster entry into this material first? PCED Introduction to Python Data Analytics covers the foundational analytics skills in less time. For deeper focus on presenting results, pair this with Python Data Visualization. Developers who want the full certification track should also review PCAD Python Data Analytics. This course also pairs naturally with our AI track — see Python Foundations and Python OpenAI API for Developers.

Course Outline

Module 1: Python Fundamentals

  • Introduction to Python and its Ecosystem
  • Data Types and Variables
  • Control Flow and Loops
  • Functions and Modules
  • File Handling in Python

Module 2: Data Manipulation with NumPy

  • Introduction to NumPy Arrays
  • Array Operations and Broadcasting
  • Indexing and Slicing Arrays
  • Basic Statistical Operations with NumPy

Module 3: Data Analysis with Pandas

  • Introduction to Pandas and its Data Structures (Series, DataFrame)
  • Data Cleaning and Preprocessing
  • Handling Missing Data
  • Data Aggregation and Grouping
  • Combining and Merging Datasets
  • Data Reshaping and Pivoting

Module 4: Data Visualization with Matplotlib and Seaborn

  • Introduction to Data Visualization
  • Creating Line Plots, Scatter Plots, and Bar Plots with Matplotlib
  • Customizing Plots and Adding Annotations
  • Introduction to Seaborn for Statistical Data Visualization
  • Creating Advanced Plots like Heatmaps, Pair Plots, and Box Plots

Module 5: Data Wrangling and Cleaning

  • Identifying and Handling Outliers
  • Dealing with Duplicate Data
  • Data Normalization and Scaling
  • Feature Engineering and Transformation
  • Introduction to Data Pipelines

Module 6: Introduction to Machine Learning

  • Understanding the Fundamentals of Machine Learning
  • Supervised vs. Unsupervised Learning
  • Training and Testing Data
  • Model Evaluation Metrics

Module 7: Machine Learning with Scikit-learn

  • Introduction to Scikit-learn Library
  • Linear Regression for Prediction
  • Logistic Regression for Classification
  • Decision Trees and Random Forests
  • k-Nearest Neighbors (k-NN) algorithm
  • Clustering with k-Means Algorithm

Module 8: Model Evaluation and Hyperparameter Tuning

  • Cross-Validation for Model Evaluation
  • Overfitting and Underfitting
  • Hyperparameter Tuning with Grid Search and Random Search

Module 9: Introduction to Deep Learning

  • Basics of Neural Networks
  • Introduction to TensorFlow or PyTorch
  • Building Simple Neural Networks

Module 10: Introduction to Data Ethics and Bias

  • Understanding Data Ethics and Privacy Concerns
  • Recognizing and Mitigating Biases in Data Science Projects