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PyDataSci

Introduction to Data Science with Python logo
Formats:  Asynchronous
Blended
Online
Onsite
Part-time
Level: Intermediate
Prerequisistes:  
Recommended Knowledge

Formats: We offer our training content in a flexible format to suit your needs. Contact Us if you wish to know if we can accommodate your unique requirements.

Level: We are happy to customise course content to suit your skill level and learning goals. Contact us for a customised learning path.

Python for Data Science PyDataSci

Unleash the power of machine learning and artificial intelligence with the key python libraries and projects ones needs to know to become a data science master. Conquer data visiaulisation with Pandas,  Matplotlib and Seaborn. Quickly analyse and present data with Jupyter Notebooks and learn how to use the key algorithms of scikit-learn.

 

Module 1: Introduction to Data Science

  • Overview of Data Science
  • The Data Science Process
  • Roles and Responsibilities of a Data Scientist
  • Introduction to Python for Data Science

Module 2: Python Basics

  • Python Environment Setup
  • Basic Python Syntax
  • Data Types and Variables
  • Control Structures (if statements, loops)
  • Functions and Modules

Module 3: Data Manipulation with Pandas

  • Introduction to Pandas
  • DataFrames and Series
  • Reading and Writing Data
  • Data Cleaning and Preparation
  • Handling Missing Data

Module 4: Data Analysis with NumPy

  • Introduction to NumPy
  • Arrays and Array Operations
  • Mathematical and Statistical Operations
  • Working with Multidimensional Data

Module 5: Data Visualization

  • Introduction to Data Visualization
  • Matplotlib Basics
  • Plotting with Pandas
  • Advanced Visualization with Seaborn
  • Interactive Plots with Plotly

Module 6: Exploratory Data Analysis (EDA)

  • Understanding Data Distributions
  • Descriptive Statistics
  • Identifying Patterns and Relationships
  • Data Transformation and Feature Engineering

Module 7: Introduction to Machine Learning

  • Overview of Machine Learning
  • Supervised vs. Unsupervised Learning
  • Introduction to Scikit-learn
  • Building and Evaluating Machine Learning Models

Module 8: Practical Data Science Projects

  • End-to-End Data Science Project
  • Data Collection and Cleaning
  • Model Building and Evaluation
  • Reporting and Presentation of Results

Module 9: Capstone Project

  • Real-World Data Science Problem
  • Applying Techniques Learned
  • Presenting Findings and Solutions

Module 10: Advanced Topics (Optional)

  • Introduction to Deep Learning with TensorFlow/Keras
  • Time Series Analysis
  • Natural Language Processing (NLP)
  • Big Data Tools and Technologies (e.g., Spark)

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ZA

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