Data Science with Python

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Curriculum

About The Course

Welcome to our comprehensive “Data Science with Python” training, where you’ll embark on a transformative journey into the world of data exploration, analysis, and modeling using the powerful Python programming language. In this dynamic e-learning course, we’ve curated a curriculum designed to equip you with the essential skills required for a successful career in data science.

Course Curriculam

Introduction to Data Science and Python Basics

Overview of Data Science and its Applications
Importance of Data in Decision-making
Role of a Data Scientist
Basics of Python Programming
Data Types and Variables
Control Structures (if statements, loops)
Functions and Modules
Introduction to NumPy, Pandas, Matplotlib, and Seaborn
Basic operations with NumPy Arrays
Data Manipulation with Pandas
Data visualization with Matplotlib and Seaborn

Data Wrangling and Preprocessing

Machine Learning Fundamentals

Overview of Machine Learning Concepts
Types of machine learning: supervised, unsupervised, and reinforcement learning
Model Training, Evaluation, and Deployment
Linear Regression
Logistic Regression
Decision Trees and Random Forests
Support Vector Machines
k-Nearest Neighbors
K-Means Clustering
Hierarchical Clustering
Principal Component Analysis (PCA)

Advanced Machine Learning

Data Science Applications and Case Studies

Project

KSh120000

Course Information

Course level: Intermediate
Instructor: Academy
Lesson: 48
Duration: 26Weeks
Last Updated: Jan 29, 2024
Quizze: 0

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Data Science with Python

About The Course

Course Curriculam

Introduction to Data Science and Python Basics

Overview of Data Science and its Applications

Importance of Data in Decision-making

Role of a Data Scientist

Basics of Python Programming

Data Types and Variables

Control Structures (if statements, loops)

Functions and Modules

Introduction to NumPy, Pandas, Matplotlib, and Seaborn

Basic operations with NumPy Arrays

Data Manipulation with Pandas

Data visualization with Matplotlib and Seaborn

Data Wrangling and Preprocessing

Identifying and handling missing data

Dealing with Duplicate Entries

Correcting Data Types and Formats

Descriptive Statistics: mean, median, mode, variance, etc.

Exploratory Data Analysis (EDA)

Visualization Techniques for EDA

Creating new Features

Handling Categorical Variables

Feature Scaling and Normalization

Machine Learning Fundamentals

Overview of Machine Learning Concepts

Types of machine learning: supervised, unsupervised, and reinforcement learning

Model Training, Evaluation, and Deployment

Linear Regression

Logistic Regression

Decision Trees and Random Forests

Support Vector Machines

k-Nearest Neighbors

K-Means Clustering

Hierarchical Clustering

Principal Component Analysis (PCA)

Advanced Machine Learning

Cross-validation

Grid Search and Random Search for Hyperparameter Tuning

Model evaluation metrics: accuracy, precision, recall, F1 score

Bagging and Boosting

Introduction to XGBoost and RandomForest

Basics of artificial neural networks

Deep learning frameworks: TensorFlow and PyTorch

Building a simple neural network in Python

Data Science Applications and Case Studies

Overview of industry use cases

Case studies and practical applications

Privacy Concerns

Bias and Fairness in Machine Learning Models

Project

Applying Learned Concepts to Solve a Real-world Problem

Project Presentation and Documentation

Version Control with Git and GitHub

Collaboration Tools for Data Science Teams

Best Practices in Writing Clean and Efficient Code

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