Python: Data Science and Machine Learning
Python 2 Course Bundle Extensive course from a Python & Data Science expert | CPD Accredited | Lifetime Access
Course Central
Summary
- Certificate of completion - Free
- Tutor is available to students
Overview
We'll start by covering some basic Python concepts, then move on to more advanced topics like working with data, building models, and making predictions. By the end of this course, you'll have a good understanding of Python and be able to use it to tackle real-world data science problems.
This 2-course bundle consists of the Following Courses:
- Course 1: Python Programming
- Course 2: Data Science & Machine Learning With Python
This course starts with the basic knowledge of Python programming, Data Science & Machine Learning with Python and gradually shares expertise knowledge. In this course you will get a complete idea of Python programming, Data Science & Machine Learning with Python with key concepts, strategies and in-depth knowledge. This course is completely an online course. You can access this course from any part of the world with just a smart device and the internet.
By the end of this bundle course, you will get complete knowledge and marketable skills. This course also comes with an Accredited certificate, which will add extra value to your resume and help you stand out in the job market. In short, This bundle is the perfect course to fast-track your career. So, what are you waiting for? Enrol thisbundle course today!
What you will learn from this course?
From the Python Programming Course, you will learn
- How to become a professional Python Developer
- How to use the basic Python structures: strings, lists, and dictionaries
- How to use Python Object-Oriented Programming (OOP)
- How to use core programming tools such as functions and loops
- How to land your first job as a Python Developer
- How to write Python scripts to perform automated actions
- How to create your own Python programs from scratch
- How to use variables to store, retrieve and calculate information
From the Data Science & Machine Learning with Python course, you will learn
- Become a professional Data Scientist, Data Engineer, Data Analyst or Consultant
- How to create a resume and land your first job as a Data Scientist
- How to write complex Python programs for practical industry scenarios
- Learn to use NumPy for Numerical Data
- Supervised vs Unsupervised Machine Learning
- Machine Learning Concepts and Algorithms
- Use Python to clean, analyze, and visualize data
- Statistics for Data Science
- Learn data cleaning, processing, wrangling and manipulation
- How to use Python for Data Science
- Learn Plotting in Python (graphs, charts, plots, histograms etc)
- Machine Learning and it's various practical applications
- Learn Regression, Classification, Clustering and Sci-kit learn
- K-Means Clustering
- Building Custom Data Solutions
- Probability and Hypothesis Testing
Why Choose this bundle course?
- Accredited by CPD
- Conducted by industry experts
- Get Instant E-certificate
- Fully online, interactive course with Professional voice-over
- Developed by qualified professionals
- Self-paced learning and laptop, tablet, smartphone-friendly
- 24/7 Tutor Support
And you will also get these gifts
- Free PDF Certificate
- Lifetime Course Access
CPD
Course media
Description
Course Curriculum
*** Course 1: Python Programming Course ***
Introduction to Python Programming
- Intro To Python
- What is Python Programming?
- Who is This Python Course For?
- Python Programming Marketplace
- Python Job Opportunities
- How To Land a Python Job Without a Degree
- Python Programmer Job Roles
- Python from A-Z Course Structure
Getting Familiar with Python
- Getting Familiar with Python Section Overview
- Installing Python on Windows
- Anaconda and Jupyter Notebooks Part 1
- Anaconda and Jupyter Notebooks Part 2
- Python Syntax
- Line Structure
- Line Structure Exercise
- Comments
- Joining Lines
- Multiple Statements on a Single Line
- Indentation
Basic Data Types
- Basic Data Types
- Python Variables
- Integers and Floats on Python
- String Overview
- String Manipulation
- String Indexing
- String Slicing
- Boolean
- Printing
- Mini Project 1 : Letter Counter
Python Operators
- Python Operators
- Arithmetic Operators
- Assignment Operators
- Comparison Operators
- Logical Operators
- Identity Operators
- Membership Operators
- Bitwise Operators
Advanced-Data Types
- Python Advanced Data Types Section Overview
- List Overview
- List Slicing and Indexing
- Tuples
- Sets
- Dictionaries
- When to use each one?
- Compound Data Types
Control Flow Part 1
- Control Flow Part 1
- Intro to Control Flow
- Basic Conditional Statements in Python
- More Conditional Statements
- For Loops
- While Loops in Python
Control Flow Part 2
- Control Flow Part 2
- Break Statements in Python
- Continue Statements
- Zip Function
- Enumerate Function
- List Comprehension
Python Functions
- Python Functions
- Intro to Functions
- Python help Function
- Defining Functions
- Variable Scope
- Doc Strings
User Input and Error Handling
- User Input and Error Handling
- Introduction to error handling
- User Input
- Syntax Errors
- Exceptions
- Handling Exceptions Part 1
- Handling Exceptions Part 2
Python Advanced Functions
- Python Advanced Functions
- Lambda Functions
- Functions args and kwargs
- Iterators
- Generators and Yield
- Map Function
- Filter Function
Python Scripting and Libraries
- Python Scripting and Libraries
- What is a script?
- What is an IDE?
- What is a text editor?
- From Jupyter Notebook to VScode Part 1
- From Jupyter Notebook to VScode Part 2
- Importing Scripts
- Standard Libraries
- Third Party Libraries
NumPy
- NumPy Section Overview
- Intro to NumPy
- Why use NumPy?
- NumPy Arrays
- Reshaping, Accessing, and Modifying
- Slicing and Copying
- Inserting, Appending, and Deleting
- Array Logical Indexing
- Broadcasting
Pandas
- Intro to Pandas
- Pandas Series
- Pandas Series Manipulation
- Pandas DataFrame
- Pandas DataFrame Manipulation
- Dealing with Missing Values
Intro to OOP
- Functional vs OOP
- OOP Key Definitions
- Create your First Class
- How to Create and Use Objects
- How to Modify Attributes
Advanced OOP
- Python Decorators
- Property Decorator
- Class Method Decorator
- Static Methods
- Inheritance from A to Z
Starting a Career in Python
- Python Career Section Overview
- ***Getting Started with Freelancing
- Building A Brand
- ***Personal Branding
- Importance of Having Website/Blog
- Do’s and Don’ts of Networking
- **Top Freelance Websites
- Creating A Python Developer Resume
***Course 2: Data Science & Machine Learning With Python***
Introduction
- Who is This Course For?
- Data Science + Machine Learning Marketplace
- Data Science Job Opportunities
Data Science & Machine Learning Concepts
- Why We Use Python?
- What is Data Science?
- What is Machine Learning?
Python For Data Science
- What is Programming?
- Why Python for Data Science?
- What is Jupyter?
- What is Google Colab?
- Python Variables, Booleans and None
- Python Operators
- Python Numbers & Booleans
- Python Strings
- Python Conditional Statements
- Python For Loops and While Loops
- Python Lists
Statistics for Data Science
- Intro To Statistics
- Descriptive Statistics
- Measure of Variability
- Measure of Variability Continued
Probability & Hypothesis Testing
- What Exactly is Probability?
- Expected Values
- Relative Frequency
NumPy Data Analysis
- Intro NumPy Array Data Types
- NumPy Arrays
- NumPy Arrays Basics
Pandas Data Analysis
- Introduction to Pandas
- Introduction to Pandas Continued
Python Data Visualization
- Data Visualization Overview
- Different Data Visualization Libraries in Python
- Python Data Visualization Implementation
Machine Learning
- Introduction To Machine Learning
Data Loading & Exploration
- Exploratory Data Analysis
Data Cleaning
- Feature Scaling
- Data Cleaning
Feature Selecting and Engineering
- Feature Engineering
Linear and Logistic Regression
- Linear Regression Intro
- Gradient Descent
- Linear Regression + Correlation Methods
K Nearest Neighbors
- KNN Overview
- parametric vs non-parametric models
- EDA on Iris Dataset
Decision Trees
- Decision Trees Section Overview
- EDA on Adult Dataset
- What is Entropy and Information Gain?
Ensemble Learning and Random Forests
- Ensemble Learning Section Overview
- What is Ensemble Learning?
- What is Bootstrap Sampling?
Support Vector Machines
- SVM Outline
- SVM intuition
- Hard vs Soft Margins
K-means
- Unsupervised Machine Learning Intro
- Unsupervised Machine Learning Continued
Certificates
Course Central is proud to offer a Certificate of Completion to all who complete courses successfully. Course Central tracks the learner’s course progress. However, the learner is responsible for validating the completion and understanding of the course. All Certificates of Completion can be validated from the Course Central website using the validation code.
Transcripts
A Transcript for the course with completed module details can be requested for as little as £4.99. Please note that all course Certificates and Transcripts will be titled as published on the Course Central platform.
Who is this course for?
This course is ideal for those who work in or aspire to work in the following professions:
- Python Developer
- Python Engineer
- Web Designer
- Web Developer
- Software Developer
- Cyber Security Manager
- Database Manager
- Anyone who wants to learn Python from Scratch
Requirements
No previous knowledge of Python is required and this course is open to all with no formal entry requirements.
Career path
This course will lead you to many different career opportunities, here are few prospects:
- Python Programmer £59,237 per annum
- Python Software Engineer- £60,000 per annum
- Professional Python Developer-£89,977 per annum
Questions and answers
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Certificates
Certificate of completion
Digital certificate - Included
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FAQs
What does study method mean?
Study method describes the format in which the course will be delivered. At Reed Courses, courses are delivered in a number of ways, including online courses, where the course content can be accessed online remotely, and classroom courses, where courses are delivered in person at a classroom venue.
What are CPD hours/points?
CPD stands for Continuing Professional Development. If you work in certain professions or for certain companies, your employer may require you to complete a number of CPD hours or points, per year. You can find a range of CPD courses on Reed Courses, many of which can be completed online.
What is a ‘regulated qualification’?
A regulated qualification is delivered by a learning institution which is regulated by a government body. In England, the government body which regulates courses is Ofqual. Ofqual regulated qualifications sit on the Regulated Qualifications Framework (RQF), which can help students understand how different qualifications in different fields compare to each other. The framework also helps students to understand what qualifications they need to progress towards a higher learning goal, such as a university degree or equivalent higher education award.
What is an ‘endorsed’ course?
An endorsed course is a skills based course which has been checked over and approved by an independent awarding body. Endorsed courses are not regulated so do not result in a qualification - however, the student can usually purchase a certificate showing the awarding body’s logo if they wish. Certain awarding bodies - such as Quality Licence Scheme and TQUK - have developed endorsement schemes as a way to help students select the best skills based courses for them.
