Data Science with Python – ML and AI

Data Science  Training in UK is a comprehensive course that covers the essential tools and techniques used in data science. The course is designed for individuals who have some programming experience and want to learn how to use Python to analyze data and build predictive models.

The course covers the following topics:

1. Introduction to Python and Data Science
2. Working with Data in Python
3. Data Manipulation with Pandas
4. Data Visualization with Matplotlib and Seaborn
5. Statistical Analysis with SciPy and StatsModels
6. Machine Learning with Scikit-Learn
7. Deep Learning with TensorFlow and Keras
8. Big Data Analytics with PySpark
9. Deployment and Productionization

Throughout the course, students will gain hands-on experience working with real-world datasets and building machine learning models using Python. By the end of the course, students will have a solid understanding of the Python programming language and be able to apply their knowledge to solve complex data science problems.

• Python for Data Science Syllabus
  The detailed Syllabus for Data Science with Python is given below.
  Python
   Introduction
   Core Python Introduction of Python
   Installing Python IDES – Python IDLE and Anaconda
   Data-types in Python
   Variables in Python:
   Initialization Declaration and Use Typecasting in Python
   Operators in Python: Assignment, Logical, Arithmetic
   Taking User Input Conditional Statements: If else and Nested, If else and elif
   Collections: List, Tuple, Sets and Dictionary
   Loops in Python: For Loop, While Loop and Nested Loops
   String Manipulation: Basic Operations, Slicing
   Data structure in Python
   Set
   List
   Tuples
   Dictionary
  Machine Learning
  Python Modules
  NumPy
  Panda
  SciPy
  Python Matplotlib
  Statistics Mean Median Mode
  Standard Deviation
  Percentile Data
  Normal Data Distribution
  Scatter Plot
  Linear Regression
  Polynomial Regression
  Multiple Regression
  Scale Train/Test
  Decision Tree
  Confusion Matrix
  Hierarchical Clustering
  Logistic Regression
  Grid Search
  Categorical Data K-means

Month 1: Fundamentals of Data Science and Python Programming

Week 1: Introduction to Data Science and Python

Day 1:

Introduction to Data Science

Overview of the Data Science Process

Introduction to Python

Setting Up the Environment (Python, Jupyter Notebooks)

Basic Python Programming

Day 2:

Python Data Structures (Lists, Tuples, Dictionaries)

Exercises on Data Structures

Basic Operations with Data Structures

Day 3:

Introduction to Libraries: NumPy, Pandas

NumPy Arrays and Operations

Data Manipulation with Pandas (Series, DataFrames)

Advanced Pandas (Merging, Joining, Grouping)

Week 2: Data Visualization

Day 1:

Data Visualization with Matplotlib

Creating Basic Plots with Matplotlib

Data Visualization with Seaborn

Advanced Visualizations with Seaborn

Day 2:

Introduction to Statistical Analysis

Descriptive Statistics with Pandas

Day 3:

Statistical Analysis and Distributions

Week 3: Data Cleaning and Preprocessing

Day 1:

Data Cleaning Techniques

Handling Missing Data

Handling Duplicates and Outliers

Day 2:

Dealing with Categorical Data

Encoding Techniques

Day 3:

Data Transformation (scaling, normalization)

Feature Engineering

Dimensionality Reduction

Week 4: : Introduction to Machine Learning

Day 1:

Overview of Machine Learning

Types of Machine Learning (supervised, unsupervised, reinforcement)

Day 2:

Supervised Learning: Regression

Linear Regression

Day 3:

Supervised Learning: Classification

Logistic Regression

Month 2:

Week 1: Advanced Supervised Learning

Day 1:

Decision Trees

Random Forests

Day 2:

Support Vector Machines

Model Evaluation Techniques

Day 3:

Hyperparameter Tuning

Grid Search

Week 2: Unsupervised Learning

Day 1:

Clustering Algorithms

K-Means Clustering

Day 2:

Hierarchical Clustering

DBSCAN

Day 3:

Association Rule Learning

Apriori Algorithm

Week 3: Model Deployment

Day 1:

Model Deployment Strategies

Introduction to Flask for Model Deployment

Deploying a Model with Flask

Day 2:

Introduction to Docker

Containerizing a Data Science Application

Deploying a Containerized Application with Docker

Day 3:

Advanced Deployment and Monitoring

Week 4: Project Work

Day 1:

Project Introduction

Data Collection and Preparation

Day 2:

Model Building and Evaluation

Iterating on the Model

Day 3:

Finalizing the Project