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Learning Outcomes After completing this course, learners will be able to: Learn Python for data analysis using NumPy and Pandas Acquire a clear understanding of data visualisation using Matplotlib, Seaborn and Pandas Deepen your knowledge of Python for interactive and geographical potting using Plotly and Cufflinks Understand Python for data science and machine learning Get acquainted with Recommender Systems with Python Enhance your understanding of Python for Natural Language Processing (NLP) Description Whether you are from an engineering background or not you still can efficiently work in the field of data science and the machine learning sector, if you have proficient knowledge of Python. Since Python is the easiest and most used programming language, you can start learning this language now to advance your career with the Data Science And Machine Learning Using Python : A Bootcamp course. This course will give you a thorough understanding of the Python programming language. Moreover, it will show how can you use Python for data analysis and machine learning. Alongside that, from this course, you will get to learn data visualisation, and interactive and geographical plotting by using Python. The course will also provide detailed information on Python for data analysis, Natural Language Processing (NLP) and much more. Upon successful completion of this course, get a CPD- certificate of achievement which will enhance your resume and career. Certificate of Achievement After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for 9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for 15.99, which will reach your doorsteps by post.  Method of Assessment After completing this course, you will be provided with some assessment questions. To pass that assessment, you need to score at least 60%. Our experts will check your assessment and give you feedback accordingly. Career Path After completing this course, you can explore various career options such as Web Developer Software Engineer Data Scientist Machine Learning Engineer Data Analyst In the UK professionals usually get a salary of £25,000 - £30,000 per annum for these positions. Course Content Welcome, Course Introduction & overview, and Environment set-up Welcome & Course Overview 00:07:00 Set-up the Environment for the Course (lecture 1) 00:09:00 Set-up the Environment for the Course (lecture 2) 00:25:00 Two other options to setup environment 00:04:00 Python Essentials Python data types Part 1 00:21:00 Python Data Types Part 2 00:15:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) 00:16:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) 00:20:00 Python Essentials Exercises Overview 00:02:00 Python Essentials Exercises Solutions 00:22:00 Python for Data Analysis using NumPy What is Numpy? A brief introduction and installation instructions. 00:03:00 NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes. 00:28:00 NumPy Essentials - Indexing, slicing, broadcasting & boolean masking 00:26:00 NumPy Essentials - Arithmetic Operations & Universal Functions 00:07:00 NumPy Essentials Exercises Overview 00:02:00 NumPy Essentials Exercises Solutions 00:25:00 Python for Data Analysis using Pandas What is pandas? A brief introduction and installation instructions. 00:02:00 Pandas Introduction 00:02:00 Pandas Essentials - Pandas Data Structures - Series 00:20:00 Pandas Essentials - Pandas Data Structures - DataFrame 00:30:00 Pandas Essentials - Handling Missing Data 00:12:00 Pandas Essentials - Data Wrangling - Combining, merging, joining 00:20:00 Pandas Essentials - Groupby 00:10:00 Pandas Essentials - Useful Methods and Operations 00:26:00 Pandas Essentials - Project 1 (Overview) Customer Purchases Data 00:08:00 Pandas Essentials - Project 1 (Solutions) Customer Purchases Data 00:31:00 Pandas Essentials - Project 2 (Overview) Chicago Payroll Data 00:04:00 Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data 00:18:00 Python for Data Visualization using matplotlib Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach 00:13:00 Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials - Exercises Overview 00:06:00 Matplotlib Essentials - Exercises Solutions 00:21:00 Python for Data Visualization using Seaborn Seaborn - Introduction & Installation 00:04:00 Seaborn - Distribution Plots 00:25:00 Seaborn - Categorical Plots (Part 1) 00:21:00 Seaborn - Categorical Plots (Part 2) 00:16:00 Seborn-Axis Grids 00:25:00 Seaborn - Matrix Plots 00:13:00 Seaborn - Regression Plots 00:11:00 Seaborn - Controlling Figure Aesthetics 00:10:00 Seaborn - Exercises Overview 00:04:00 Seaborn - Exercise Solutions 00:19:00 Python for Data Visualization using pandas Pandas Built-in Data Visualization 00:34:00 Pandas Data Visualization Exercises Overview 00:03:00 Panda Data Visualization Exercises Solutions 00:13:00 Python for interactive & geographical plotting using Plotly and Cufflinks Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1) 00:19:00 Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2) 00:14:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview) 00:11:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions) 00:37:00 Capstone Project - Python for Data Analysis & Visualization Project 1 - Oil vs Banks Stock Price during recession (Overview) 00:15:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3) 00:17:00 Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview) 00:03:00 Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Introduction to ML - What, Why and Types.. 00:15:00 Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff 00:15:00 scikit-learn - Linear Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Linear Regression Model Hands-on (Part 2) 00:19:00 Good to know! How to save and load your trained Machine Learning Model! 00:01:00 scikit-learn - Linear Regression Model (Insurance Data Project Overview) 00:08:00 scikit-learn - Linear Regression Model (Insurance Data Project Solutions) 00:30:00 Python for Machine Learning - scikit-learn - Logistic Regression Model Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc. 00:10:00 scikit-learn - Logistic Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Logistic Regression Model - Hands-on (Part 2) 00:20:00 scikit-learn - Logistic Regression Model - Hands-on (Part 3) 00:11:00 scikit-learn - Logistic Regression Model - Hands-on (Project Overview) 00:05:00 scikit-learn - Logistic Regression Model - Hands-on (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn - K Nearest Neighbors Theory: K Nearest Neighbors, Curse of dimensionality . 00:08:00 scikit-learn - K Nearest Neighbors - Hands-on 00:25:00 scikt-learn - K Nearest Neighbors (Project Overview) 00:04:00 scikit-learn - K Nearest Neighbors (Project Solutions) 00:14:00 Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging. 00:18:00 scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1) 00:19:00 scikit-learn - Decision Tree and Random Forests (Project Overview) 00:05:00 scikit-learn - Decision Tree and Random Forests (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) Support Vector Machines (SVMs) - (Theory Lecture) 00:07:00 scikit-learn - Support Vector Machines - Hands-on (SVMs) 00:30:00 scikit-learn - Support Vector Machines (Project 1 Overview) 00:07:00 scikit-learn - Support Vector Machines (Project 1 Solutions) 00:20:00 scikit-learn - Support Vector Machines (Optional Project 2 - Overview) 00:02:00 Python for Machine Learning - scikit-learn - K Means Clustering Theory: K Means Clustering, Elbow method .. 00:11:00 scikit-learn - K Means Clustering - Hands-on 00:23:00 scikit-learn - K Means Clustering (Project Overview) 00:07:00 scikit-learn - K Means Clustering (Project Solutions) 00:22:00 Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Theory: Principal Component Analysis (PCA) 00:09:00 scikit-learn - Principal Component Analysis (PCA) - Hands-on 00:22:00 scikit-learn - Principal Component Analysis (PCA) - (Project Overview) 00:02:00 scikit-learn - Principal Component Analysis (PCA) - (Project Solutions) 00:17:00 Recommender Systems with Python - (Additional Topic) Theory: Recommender Systems their Types and Importance 00:06:00 Python for Recommender Systems - Hands-on (Part 1) 00:18:00 Python for Recommender Systems - - Hands-on (Part 2) 00:19:00 Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) Natural Language Processing (NLP) - (Theory Lecture) 00:13:00 NLTK - NLP-Challenges, Data Sources, Data Processing .. 00:13:00 NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing 00:19:00 NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW. 00:19:00 NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes 00:13:00 NLTK - NLP - Pipeline feature to assemble several steps for cross-validation 00:09:00 Resources Resources - Data Science and Machine Learning using Python : A Bootcamp 00:00:00 Order your Certificates & Transcripts Order your Certificates & Transcripts 00:00:00 Frequently Asked Questions Are there any prerequisites for taking the course? There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course. Can I access the course at any time, or is there a set schedule? You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience. How long will I have access to the course? For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime. Is there a certificate of completion provided after completing the course? Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks. Can I switch courses or get a refund if I'm not satisfied with the course? We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase. How do I track my progress in the course? Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course. What if I have technical issues or difficulties with the course? If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.

QLS Endorsed + CPD QS Accredited - Dual Certification | Instant Access | 24/7 Tutor Support

Overview Uplift Your Career & Skill Up to Your Dream Job - Learning Simplified From Home! Kickstart your career & boost your employability by helping you discover your skills, talents and interests with our special Certificate in Python at QLS Level 3 Course. You'll create a pathway to your ideal job as this course is designed to uplift your career in the relevant industry. It provides professional training that employers are looking for in today's workplaces. The Certificate in Python at QLS Level 3 Course is one of the most prestigious training offered at StudyHub and is highly valued by employers for good reason. This Certificate in Python at QLS Level 3 Course has been designed by industry experts to provide our learners with the best learning experience possible to increase their understanding of their chosen field. This Certificate in Python at QLS Level 3 Course, like every one of Study Hub's courses, is meticulously developed and well researched. Every one of the topics is divided into elementary modules, allowing our students to grasp each lesson quickly. At StudyHub, we don't just offer courses; we also provide a valuable teaching process. When you buy a course from StudyHub, you get unlimited Lifetime access with 24/7 dedicated tutor support. Why buy this Certificate in Python at QLS Level 3? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments are designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience Who is this course for? This Certificate in Python at QLS Level 3 course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill. Career path As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Certificate in Python at QLS Level 3 is a great way for you to gain multiple skills from the comfort of your home. Prerequisites This Certificate in Python at QLS Level 3 does not require you to have any prior qualifications or experience. You can just enrol and start learning. This course was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Endorsed Certificate of Achievement from the Quality Licence Scheme Learners will be able to achieve an endorsed certificate after completing the course as proof of their achievement. You can order the endorsed certificate for only £85 to be delivered to your home by post. For international students, there is an additional postage charge of £10. Endorsement The Quality Licence Scheme (QLS) has endorsed this course for its high-quality, non-regulated provision and training programmes. The QLS is a UK-based organisation that sets standards for non-regulated training and learning. This endorsement means that the course has been reviewed and approved by the QLS and meets the highest quality standards. Please Note: Studyhub is a Compliance Central approved resale partner for Quality Licence Scheme Endorsed courses. Course Curriculum Section 1: Introduction & Getting Started Unit 1: Introduction 00:03:00 Unit 2: Instructor's Introduction 00:03:00 Unit 3: Why Python 00:03:00 Section 2: Downloading and Installing Python Editor Unit 1: Downloading and Installing Python 00:12:00 Section 3: Getting Started Unit 1: Hello World and Help Function 00:09:00 Section 4: Variables and Basic Data Types in Python Unit 1: Variables and Basic Data Types in Python 00:12:00 Section 5: Comments Unit 1: Commenting Your Code 00:07:00 Section 6: Input Unit 1: Reading Keyboard Input 00:11:00 Section 7: Exercise - Build a Program to Say Hello Unit 1: Exercise - Build a Program to Say Hello 00:05:00 Section 8: Exercise - Build a Simple Calculator Unit 1: Exercise - Build a Simple Calculator App 00:08:00 Section 9: Conditional Statements Unit 1: Conditional Statements 00:15:00 Section 10: Loops - For Loop Unit 1: Loops - For Loop 00:14:00 Section 11: Loops - While Loop Unit 1: Loops - While Loop 00:08:00 Section 12: Exercise - Building a Username Password App Unit 1: Exercise - Building a Username and Password App 00:05:00 Assignment Assignment - Certificate in Python at QLS Level 3 04:00:00 Order your QLS Endorsed Certificate Order your QLS Endorsed Certificate 00:00:00

The course helps you learn Snowflake from scratch and explore a few of its important features. You will build automated pipelines with Snowflake and use the AWS cloud with Snowflake as a data warehouse. You will also explore Snowpark to be worked on the data pipelines.

Get ready for an exceptional online learning experience with the Big Data Analysis, Data Science, Fintech & Python for Data Analyst bundle! This carefully curated collection of 20 premium courses is designed to cater to a variety of interests and disciplines. Dive into a sea of knowledge and skills, tailoring your learning journey to suit your unique aspirations. The Big Data Analysis, Data Science, Fintech & Python for Data Analyst is a dynamic package, that blends the expertise of industry professionals with the flexibility of digital learning. It offers the perfect balance of foundational understanding and advanced insights. Whether you're looking to break into a new field or deepen your existing knowledge, the Data Analysis package has something for everyone. As part of the Big Data Analysis, Data Science, Fintech & Python for Data Analyst package, you will receive complimentary PDF certificates for all courses in this bundle at no extra cost. Equip yourself with the Data Analysis bundle to confidently navigate your career path or personal development journey. Enrol today and start your career growth! This bundle comprises the following courses: CPD Quality Standards Courses: Big Data Analytics with PySpark Power BI and MongoDB Big Data Analytics with PySpark Tableau Desktop and MongoDB Building Big Data Pipelines with PySpark MongoDB and Bokeh Develop Big Data Pipelines with R & Sparklyr & Tableau Develop Big Data Pipelines with R, Sparklyr & Power BI Learn Python, JavaScript, and Microsoft SQL for Data Science SQL for Data Science, Data Analytics, and Data Visualization Excel Data Analysis Introduction to Data Analytics with Tableau Business and Data Analytics for Beginners Google Data Studio: Data Analytics Basic Data Analysis FinTech Learning Outcome: Gain comprehensive insights into multiple fields. Foster critical thinking and problem-solving skills across various disciplines. Understand industry trends and best practices through the Data Analysis Bundle. Develop practical skills applicable to real-world situations. Enhance personal and professional growth with Data Analysis. Build a strong knowledge base in your chosen course via Data Analysis. Benefit from the flexibility and convenience of online learning. With the Data Analysis package, validate your learning with a CPD certificate. Each course in this bundle holds a prestigious CPD accreditation, symbolising exceptional quality. The materials, brimming with knowledge, are regularly updated, ensuring their relevance. This bundle promises not just education but an evolving learning experience. Engage with this extraordinary collection, and prepare to enrich your personal and professional development. Embrace the future of learning with the Big Data Analysis, Data Science, Fintech & Python for Data Analyst, a rich anthology of 15 diverse courses. Each course in the Data Analysis bundle is handpicked by our experts to ensure a wide spectrum of learning opportunities. ThisBig Data Analysis, Data Science, Fintech & Python for Data Analyst bundle will take you on a unique and enriching educational journey. The bundle encapsulates our mission to provide quality, accessible education for all. Whether you are just starting your career, looking to switch industries, or hoping to enhance your professional skill set, the Big Data Analysis, Data Science, Fintech & Python for Data Analyst bundle offers you the flexibility and convenience to learn at your own pace. Make the Data Analysis package your trusted companion in your lifelong learning journey. CPD 35 CPD hours / points Accredited by CPD Quality Standards Who is this course for? The Big Data Analysis, Data Science, Fintech & Python for Data Analyst bundle is perfect for: Lifelong learners looking to expand their knowledge and skills. Professionals seeking to enhance their career with CPD certification. Individuals wanting to explore new fields and disciplines. Anyone who values flexible, self-paced learning from the comfort of home. Career path Unleash your potential with the Big Data Analysis, Data Science, Fintech & Python for Data Analyst bundle. Acquire versatile skills across multiple fields, foster problem-solving abilities, and stay ahead of industry trends. Ideal for those seeking career advancement, a new professional path, or personal growth. Embrace the journey with the Data Analysis bundle package. Certificates Certificate Of Completion Digital certificate - Included Certificate Of Completion Hard copy certificate - Included You will get a complimentary Hard Copy Certificate.

Introducing the 'Python Programming Bible | Networking, GUI, Email, XML, CGI' - your comprehensive, all-in-one resource for mastering Python! Are you an aspiring developer looking to dive into the ocean of Python programming or a seasoned coder seeking to level up your Python game? Look no further! Our course is expertly designed to take you from the basics to the complexities of Python, including Networking, GUI, Email, XML, and CGI. If you've ever dreamt of not just learning Python but truly mastering it, this is the course for you. This program is designed to provide a solid foundation and sharpen your skills in one of the most in-demand programming languages, while also introducing you to its many applications. This course starts with the basics of Python, providing a gentle yet thorough introduction and setup that caters to beginners as well as those looking to refresh their Python knowledge. As we study deeper into the heart of Python, we dive into objects, classes, and the power of regular expressions. But it doesn't stop there! You'll also become comfortable with concepts like CGI programming, which is an important building block for creating dynamic web pages. Navigating from core programming, we transition into the intricacies of managing databases and executing multithreading in Python. You'll gain the confidence to handle complex data management tasks, understand how Python interacts with databases, and efficiently manages multiple tasks simultaneously. The XML section allows you to get hands-on with parsing, data extraction, and manipulation, while the GUI section unveils the art of creating beautiful, user-friendly interfaces using Python. The course is enriched with a diverse set of resources, including real-world projects, quizzes, and interactive coding exercises. This is more than just a course, it's your passport to a new realm of opportunities, unlocking a world where Python programming is your strength, not just a skill.  So whether you're a student aiming to get a head start on your peers, a professional looking to diversify your skills, or an enthusiast wanting to dive deeper into the Python universe, the Python Programming Bible is the starting point for your journey to becoming a Python expert. Enrol today and step into a future of endless opportunities with Python! Learning Outcomes: Upon completion of the Python Programming Bible course, you should be able to: Understand and implement Python basics and advanced concepts. Build object-oriented programs with Python. Utilise regular expressions for pattern-matching tasks. Develop dynamic web pages using CGI programming. Interact with databases efficiently using Python. Apply multithreading for better utilisation of resources. Process and manipulate data using XML in Python. Design and create user-friendly GUIs with Python. Who is this course for? This Python Programming Bible course is ideal for the following: Beginners aiming to learn Python from scratch. Professionals looking to broaden their programming skills. Students pursuing a degree in Computer Science. Web developers looking to integrate Python into their toolkit. Data enthusiasts aiming to handle data with Python. Career Path: This Python Programming Bible course will help you to develop your knowledge and skills to pursue different careers, such as: Python Developer: (£35,000 - £70,000). Data Analyst: (£27,000 - £55,000). Web Developer: (£24,000 - £60,000). Data Scientist: (£45,000 - £90,000). Machine Learning Engineer: (£50,000 - £90,000). Software Developer: (£30,000 - £70,000). Certification After studying the course materials of the Python Programming Bible | Networking, GUI, Email, XML, CGI there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for free. Original Hard Copy certificates need to be ordered at an additional cost of £8. Prerequisites This Python Programming Bible | Networking, GUI, Email, XML, CGI does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Python Programming Bible | Networking, GUI, Email, XML, CGI was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Course Curriculum Section 01: Introduction & Setup Introduction 00:02:00 Setup on Mac OS X 00:03:00 Setup On Linux/Ubuntu 00:03:00 Setup On Windows 00:03:00 Run Code Online 00:03:00 Section 02: Basics Comments 00:02:00 Variables & Variable Types 00:05:00 Lists 00:04:00 Tuples 00:03:00 Dictionary 00:06:00 Data Type Conversion 00:02:00 Arithmetic Operators 00:05:00 Comparison Operators 00:03:00 Assignment Operators 00:03:00 Bitwise Operators 00:10:00 Logical Operators 00:07:00 Membership Operators 00:02:00 Identity Operators 00:02:00 Operator Precedence 00:03:00 Decision Making 00:09:00 Loops 00:06:00 Loop Control Statements 00:05:00 Numbers 00:05:00 Strings 00:12:00 Lists In Depth 00:05:00 Tuples In Depth 00:06:00 Dictionary In Depth 00:08:00 Date & Time 00:07:00 Functions 00:11:00 Modules 00:05:00 File Inputs & Outputs 00:13:00 Handling Exceptions 00:07:00 Section 03: Classes/Objects Simple Example 00:04:00 Creating Instance Objects 00:01:00 Accessing Attributes 00:04:00 Constructor New & Init Method 00:06:00 Destroying Objects 00:02:00 Class Inheritance 00:04:00 Overriding Methods 00:03:00 Overloading Methods 00:01:00 Overloading Operators 00:04:00 Data Hiding 00:03:00 Section 04: Regular Expressions Match Function 00:05:00 Search Function 00:02:00 Advanced Expressions 00:05:00 Search & Replace 00:03:00 Section 05: CGI Programming Basic CGI Programming 00:08:00 Get Method 00:06:00 Post Method 00:05:00 Cookies 00:05:00 Section 06: Database Setup Database 00:02:00 Connect To Database 00:05:00 Create Table 00:03:00 INSERT Operation 00:04:00 READ Operation 00:06:00 UPDATE Operation 00:02:00 DELETE Operation 00:02:00 Simple Network Example 00:04:00 Simple Client 00:04:00 Section 07: Multithreading Initiate a New Thread 00:07:00 Create Thread 00:06:00 Synchronise Threads 00:03:00 Multithreaded Priority Queue 00:09:00 Section 08: XML Parse an XML File 00:10:00 Section 09: GUI Introduction 00:02:00 Button Preview 00:03:00 Canvas 00:04:00 Checkbutton 00:02:00 Entry 00:02:00 Frame 00:04:00 Label 00:02:00 List Box 00:02:00 Menu button 00:03:00 Menu 00:08:00 Message 00:02:00 Radio button 00:05:00 Scale 00:03:00 Scrollbar 00:04:00 Text 00:03:00 Top-level 00:02:00 Spinbox 00:02:00 Paned Window 00:03:00 Message Box 00:02:00 Label Frame 00:02:00 Section 10: Resource Resource 00:00:00 Assignment Assignment - Python Programming Bible | Networking, GUI, Email, XML, CGI 00:00:00

Be efficient in Python programming & boost your skills with the U&P AI - Natural Language Processing with

Overview This comprehensive course on Python for Data Analysis will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Python for Data Analysis comes with accredited certification, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is this course for? There is no experience or previous qualifications required for enrolment on this Python for Data Analysis. It is available to all students, of all academic backgrounds. Requirements Our Python for Data Analysis is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career path Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. Course Curriculum 19 sections • 99 lectures • 00:08:00 total length •Welcome & Course Overview: 00:07:00 •Set-up the Environment for the Course (lecture 1): 00:09:00 •Set-up the Environment for the Course (lecture 2): 00:25:00 •Two other options to setup environment: 00:04:00 •Python data types Part 1: 00:21:00 •Python Data Types Part 2: 00:15:00 •Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1): 00:16:00 •Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2): 00:20:00 •Python Essentials Exercises Overview: 00:02:00 •Python Essentials Exercises Solutions: 00:22:00 •What is Numpy? A brief introduction and installation instructions.: 00:03:00 •NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes.: 00:28:00 •NumPy Essentials - Indexing, slicing, broadcasting & boolean masking: 00:26:00 •NumPy Essentials - Arithmetic Operations & Universal Functions: 00:07:00 •NumPy Essentials Exercises Overview: 00:02:00 •NumPy Essentials Exercises Solutions: 00:25:00 •What is pandas? A brief introduction and installation instructions.: 00:02:00 •Pandas Introduction: 00:02:00 •Pandas Essentials - Pandas Data Structures - Series: 00:20:00 •Pandas Essentials - Pandas Data Structures - DataFrame: 00:30:00 •Pandas Essentials - Handling Missing Data: 00:12:00 •Pandas Essentials - Data Wrangling - Combining, merging, joining: 00:20:00 •Pandas Essentials - Groupby: 00:10:00 •Pandas Essentials - Useful Methods and Operations: 00:26:00 •Pandas Essentials - Project 1 (Overview) Customer Purchases Data: 00:08:00 •Pandas Essentials - Project 1 (Solutions) Customer Purchases Data: 00:31:00 •Pandas Essentials - Project 2 (Overview) Chicago Payroll Data: 00:04:00 •Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data: 00:18:00 •Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach: 00:13:00 •Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach: 00:22:00 •Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach: 00:22:00 •Matplotlib Essentials - Exercises Overview: 00:06:00 •Matplotlib Essentials - Exercises Solutions: 00:21:00 •Seaborn - Introduction & Installation: 00:04:00 •Seaborn - Distribution Plots: 00:25:00 •Seaborn - Categorical Plots (Part 1): 00:21:00 •Seaborn - Categorical Plots (Part 2): 00:16:00 •Seborn-Axis Grids: 00:25:00 •Seaborn - Matrix Plots: 00:13:00 •Seaborn - Regression Plots: 00:11:00 •Seaborn - Controlling Figure Aesthetics: 00:10:00 •Seaborn - Exercises Overview: 00:04:00 •Seaborn - Exercise Solutions: 00:19:00 •Pandas Built-in Data Visualization: 00:34:00 •Pandas Data Visualization Exercises Overview: 00:03:00 •Panda Data Visualization Exercises Solutions: 00:13:00 •Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1): 00:19:00 •Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2): 00:14:00 •Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview): 00:11:00 •Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions): 00:37:00 •Project 1 - Oil vs Banks Stock Price during recession (Overview): 00:15:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1): 00:18:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2): 00:18:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3): 00:17:00 •Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview): 00:03:00 •Introduction to ML - What, Why and Types..: 00:15:00 •Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff: 00:15:00 •scikit-learn - Linear Regression Model - Hands-on (Part 1): 00:17:00 •scikit-learn - Linear Regression Model Hands-on (Part 2): 00:19:00 •Good to know! How to save and load your trained Machine Learning Model!: 00:01:00 •scikit-learn - Linear Regression Model (Insurance Data Project Overview): 00:08:00 •scikit-learn - Linear Regression Model (Insurance Data Project Solutions): 00:30:00 •Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc.: 00:10:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 1): 00:17:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 2): 00:20:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 3): 00:11:00 •scikit-learn - Logistic Regression Model - Hands-on (Project Overview): 00:05:00 •scikit-learn - Logistic Regression Model - Hands-on (Project Solutions): 00:15:00 •Theory: K Nearest Neighbors, Curse of dimensionality .: 00:08:00 •scikit-learn - K Nearest Neighbors - Hands-on: 00:25:00 •scikt-learn - K Nearest Neighbors (Project Overview): 00:04:00 •scikit-learn - K Nearest Neighbors (Project Solutions): 00:14:00 •Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging.: 00:18:00 •scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1): 00:19:00 •scikit-learn - Decision Tree and Random Forests (Project Overview): 00:05:00 •scikit-learn - Decision Tree and Random Forests (Project Solutions): 00:15:00 •Support Vector Machines (SVMs) - (Theory Lecture): 00:07:00 •scikit-learn - Support Vector Machines - Hands-on (SVMs): 00:30:00 •scikit-learn - Support Vector Machines (Project 1 Overview): 00:07:00 •scikit-learn - Support Vector Machines (Project 1 Solutions): 00:20:00 •scikit-learn - Support Vector Machines (Optional Project 2 - Overview): 00:02:00 •Theory: K Means Clustering, Elbow method ..: 00:11:00 •scikit-learn - K Means Clustering - Hands-on: 00:23:00 •scikit-learn - K Means Clustering (Project Overview): 00:07:00 •scikit-learn - K Means Clustering (Project Solutions): 00:22:00 •Theory: Principal Component Analysis (PCA): 00:09:00 •scikit-learn - Principal Component Analysis (PCA) - Hands-on: 00:22:00 •scikit-learn - Principal Component Analysis (PCA) - (Project Overview): 00:02:00 •scikit-learn - Principal Component Analysis (PCA) - (Project Solutions): 00:17:00 •Theory: Recommender Systems their Types and Importance: 00:06:00 •Python for Recommender Systems - Hands-on (Part 1): 00:18:00 •Python for Recommender Systems - - Hands-on (Part 2): 00:19:00 •Natural Language Processing (NLP) - (Theory Lecture): 00:13:00 •NLTK - NLP-Challenges, Data Sources, Data Processing ..: 00:13:00 •NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing: 00:19:00 •NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW.: 00:19:00 •NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes : 00:13:00 •NLTK - NLP - Pipeline feature to assemble several steps for cross-validation: 00:09:00 •Resources- Python for Data Analysis: 00:00:00

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