6198 Opera courses in Linlithgow delivered On Demand

Scaling with Simplicity - How to Eliminate Complexity in Large Agile Programs As agile thought leaders debate if agile at scale is really "agile" many of us who work in the trenches understand that scaling is sometimes necessary. We work in legacy environments and on complex codebases that require coordination. Our organizations may be working towards independent teams and a microservices architecture, but even the migrations required to get there demand scaling. This talk will help participants reduce complexity in scaled organizations and simplify their program framework. Attendees will receive actionable recommendations to help them execute more efficiently starting today. With a heavy focus on eliminating dependencies and effective planning, the presentation will help attendees bring simplicity to even the most complex scaled programs. Interspersed through the presentation will be real-life examples of successes and failures so attendees can understand how to put ideas into action. Attendees will also receive tips on influencing leadership to adopt these recommendations, empowering them to drive sustainable change. Key takeaways: How to simplify a program's operating model Tips for eliminating dependencies and managing through dependencies that can't be cut How to overcome two problems no amount of planning can solve for: bad requirements and unstable priorities Strategies to reduce the impact of prioritization changes How to influence the change necessary to build a better managed program

Nine Guidelines to Successfully Executing Portfolio Management in Your Organization Nine guidelines for successful implementation of portfolio management, or enhancement of your organization´s existing portfolio management process, are presented in this video Implementing portfolio management is a difficult culture change. Everyone enjoys pursuing their own 'pet' projects that they believe will make a difference, but what about other peoples' projects? The importance of portfolio management must be communicated in order for it to be embraced and implemented, and change needs to be managed strategically as we optimize our organization's portfolio: programs, projects, and operational activities. This video presents nine guidelines for the successful implementation of portfolio management or enhancement of your organization's existing portfolio management process. This and other IIL Learning in Minutes presentations qualify for PDUs. Some titles, such as Agile-related topics may qualify for other continuing education credits such as SEUs, or CEUs. Each professional development activity yields one PDU for one hour spent engaged in the activity. Some limitations apply and can be found in the Ways to Earn PDUs section that discusses PDU activities and associated policies. Fractions of PDUs may also be reported. The smallest increment of a PDU that can be reported is 0.25. This means that if you spent 15 minutes participating in a qualifying PDU activity, you may report 0.25 PDU. If you spend 30 minutes in a qualifying PDU activity, you may report 0.50 PDU.

Embark on a journey into the world of Android app development with our comprehensive Kotlin Programming: Android Coding Bible course. This in-depth training will equip you with the knowledge and skills to master Kotlin, a modern and powerful programming language specifically designed for Android development. Whether you're a complete beginner or an experienced programmer seeking to expand your skillset, this course will guide you through the fundamentals of Kotlin, covering topics such as syntax, data types, variables, operators, control flow statements, functions, error handling, and null safety. You'll gain a solid understanding of object-oriented programming principles and learn how to effectively utilize collections to manage and process data. Learning Outcomes Master the fundamentals of Kotlin programming language for Android development Gain a thorough understanding of syntax, data types, variables, operators, and control flow statements Learn how to create and utilize functions to structure and modularize code Effectively handle errors and exceptions to ensure robust and reliable applications Implement null safety mechanisms to prevent NullPointerExceptions and enhance code stability. Why choose this Kotlin Programming: Android Coding Bible course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments 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. Unlock career resources for CV improvement, interview readiness, and job success. Who is this Kotlin Programming: Android Coding Bible course for? Aspiring Android developers seeking to master Kotlin programming language Individuals with prior programming experience looking to transition to Android development Software engineers aiming to expand their skillset with Kotlin and Android development expertise Students of computer science or related fields interested in learning Android app development Enthusiasts passionate about creating mobile applications for the Android platform. Career path Android Developer (£35,000 - £55,000) Senior Android Developer (£45,000 - £65,000) Android App Architect (£55,000 - £75,000) Mobile Application Developer (£30,000 - £50,000) Software Engineer (£40,000 - £60,000) Full-Stack Developer (£45,000 - £65,000) Prerequisites This Kotlin Programming: Android Coding Bible does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Kotlin Programming: Android Coding Bible 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. Course Curriculum Section 01: Introduction & Setup Introduction To This Course 00:02:00 Windows Setup 00:13:00 Mac Setup 00:10:00 Linux Setup 00:12:00 Online Code Editor 00:02:00 Section 02: Basics Variables 00:06:00 Data Types 00:06:00 String 00:05:00 Array 00:07:00 Data Type Conversion 00:05:00 Comments 00:04:00 Arithmetic Operators 00:07:00 Relational Operators 00:06:00 Assignment Operators 00:06:00 Unary Operators 00:05:00 Bitwise Operators 00:09:00 Logical Operators 00:04:00 Input & Output 00:03:00 Section 03: Control Statements Conditional if Statement 00:05:00 When Statement 00:07:00 For Loop 00:04:00 While Loop 00:04:00 do while Loop 00:04:00 Break Statement 00:04:00 Continue Statement 00:04:00 Section 04: Functions Basic Functions 00:03:00 Function Parameters 00:04:00 Return Values 00:04:00 Recursion 00:04:00 Default & Named Arguments 00:06:00 Lambda Function 00:04:00 Higher Order Function 00:05:00 Inline Function 00:02:00 Section 05: Error/Exception Handling Try Catch Block 00:04:00 Try Catch Expression 00:05:00 Multiple Catch Block 00:05:00 Nested Try Catch Block 00:05:00 Finally Block 00:02:00 Throw Keyword 00:02:00 Section 06: NULL Safety Nullable & Non Nullable Types 00:03:00 Smart Cast 00:02:00 Unsafe and Safe Cast 00:03:00 Elvis Operator 00:04:00 Section 07: Collections List: listOf Function 00:06:00 List: mutableListOf Function 00:05:00 List: arrayListOf Function 00:06:00 Map: mapOf Function 00:07:00 Map: HashMap 00:08:00 Map: hashMapOf Function 00:05:00 Map: mutableMapOf Function 00:04:00 Set: setOf Function 00:04:00 Set: mutableSetOf Function 00:04:00 Set: HashSet 00:04:00 Section 08: Classes & Objects Basic Example 00:07:00 Nested and Inner Class 00:06:00 Constructors 00:05:00 Visibility Modifiers 00:06:00 Inheritance 00:05:00 Method Overriding 00:04:00 Property Overriding 00:02:00 Abstract Class 00:03:00 Superclass 00:03:00 Data Class 00:05:00 Multiple Class Inheritance & Interfaces 00:03:00 Sealed Class 00:03:00 Extension Function 00:03:00 Generics 00:05:00 Section 09: Extras Integer Type Range 00:05:00 Regex 00:04:00 Call Java from Kotlin 00:03:00 Call Kotlin from Java 00:02:00 Section 10: Resource Resource 00:00:00 Assignment Assignment - Kotlin Masterclass Programming Course: Android Coding Bible 00:00:00

Innovative Sustainable Horticulture Management The founders of the High Line showed the world how an unwanted landscape relic could be turned into a beautiful, and usable, public space; thereby providing a renown model of adaptive reuse. Today, the continued operation of the park is equally as carefully designed and innovative.The landscaping on the High Line was designed to idealize nature in an urban setting, and our plant selection and horticulture management techniques are calibrated to maintain that nature.These innovative, sustainable methods ranging from composting, to integrated pest management, to the use of sustainable cleaning products, to water flow design lead to an environment where our plants can thrive and our surroundings can remain clean and beautiful for the visitors of today and for future generations. This and other IIL Learning in Minutes presentations qualify for PDUs. Some titles, such as Agile-related topics may qualify for other continuing education credits such as SEUs, or CEUs. Each professional development activity yields one PDU for one hour spent engaged in the activity. Some limitations apply and can be found in the Ways to Earn PDUs section that discusses PDU activities and associated policies. Fractions of PDUs may also be reported. The smallest increment of a PDU that can be reported is 0.25. This means that if you spent 15 minutes participating in a qualifying PDU activity, you may report 0.25 PDU. If you spend 30 minutes in a qualifying PDU activity, you may report 0.50 PDU.

Building and Scaling a Data Science Culture As your data and AI teams scale from one to thousands of employees, you will encounter roadblocks along the way. From handling messy data to productionization and customer adoption, these obstacles can delay or even derail otherwise strong teams. Drawing on experiences gleaned from hundreds of clients, Michael Li presents a framework that successful companies have embraced to build and scale their data teams. The talk goes over how organizations progress along three maturity curves: Analytical, Operational, and Organizational. As enterprises strive to move along each of these maturity curves, they must solve various organizational challenges and develop new capabilities and skills in order to become data-driven organizations. We will provide key takeaways for managers and executives for each step of the maturity curves. This and other IIL Learning in Minutes presentations qualify for PDUs. Some titles, such as Agile-related topics may qualify for other continuing education credits such as SEUs, or CEUs. Each professional development activity yields one PDU for one hour spent engaged in the activity. Some limitations apply and can be found in the Ways to Earn PDUs section that discusses PDU activities and associated policies. Fractions of PDUs may also be reported. The smallest increment of a PDU that can be reported is 0.25. This means that if you spent 15 minutes participating in a qualifying PDU activity, you may report 0.25 PDU. If you spend 30 minutes in a qualifying PDU activity, you may report 0.50 PDU.

Building and Scaling a Data Science Culture As your data and AI teams scale from one to thousands of employees, you will encounter roadblocks along the way. From handling messy data to productionization and customer adoption, these obstacles can delay or even derail otherwise strong teams. Drawing on experiences gleaned from hundreds of clients, Michael Li presents a framework that successful companies have embraced to build and scale their data teams. The talk goes over how organizations progress along three maturity curves: Analytical, Operational, and Organizational. As enterprises strive to move along each of these maturity curves, they must solve various organizational challenges and develop new capabilities and skills in order to become data-driven organizations. We will provide key takeaways for managers and executives for each step of the maturity curves. This and other IIL Learning in Minutes presentations qualify for PDUs. Some titles, such as Agile-related topics may qualify for other continuing education credits such as SEUs, or CEUs. Each professional development activity yields one PDU for one hour spent engaged in the activity. Some limitations apply and can be found in the Ways to Earn PDUs section that discusses PDU activities and associated policies. Fractions of PDUs may also be reported. The smallest increment of a PDU that can be reported is 0.25. This means that if you spent 15 minutes participating in a qualifying PDU activity, you may report 0.25 PDU. If you spend 30 minutes in a qualifying PDU activity, you may report 0.50 PDU.

Delve into the world of data analysis with 'R Programming for Data Science,' a course designed to guide learners through the intricacies of R, a premier programming language in the data science domain. The course opens with a broad perspective on data science, illuminating the pivotal role of R in this field. Learners are then introduced to R and RStudio, equipping them with the foundational tools and interfaces essential for R programming. The curriculum progresses with an introduction to the basics of R, ensuring learners grasp the core principles that underpin more complex operations. A highlight of this course is its in-depth exploration of R's versatile data structures, including vectors, matrices, factors, and data frames. Each unit is crafted to provide learners with a comprehensive understanding of these structures, pivotal for effective data handling and manipulation. The course also emphasizes the importance of relational and logical operators in R, key elements for executing data operations. As the course advances, learners will engage with the nuances of conditional statements and loops, essential for writing efficient and dynamic R scripts. Moving into more advanced territories, the course delves into the creation and usage of functions, an integral part of R programming, and the exploration of various R packages that extend the language's capabilities. Learners will also gain expertise in the 'apply' family of functions, crucial for streamlined data processing. Further units cover regular expressions and effective strategies for managing dates and times in data sets. The course concludes with practical applications in data acquisition, cleaning, visualization, and manipulation, ensuring learners are well-prepared to tackle real-world data science challenges using R. Learning Outcomes Develop a foundational understanding of R's role in data science and proficiency in RStudio. Gain fluency in R programming basics, enabling the handling of complex data tasks. Acquire skills in managing various R data structures for efficient data analysis. Master relational and logical operations for advanced data manipulation in R. Learn to create functions and utilize R packages for expanded analytical capabilities. Why choose this R Programming for Data Science course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments 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. Unlock career resources for CV improvement, interview readiness, and job success. Who is this R Programming for Data Science course for? Beginners in data science eager to learn R programming. Data analysts and scientists looking to enhance their skills in R. Researchers in various fields requiring advanced data analysis tools. Statisticians seeking to adopt R for more sophisticated data manipulations. Professionals in finance, healthcare, and other sectors needing data-driven insights. Career path Data Scientist (R Expertise): £30,000 - £70,000 Data Analyst (R Programming Skills): £27,000 - £55,000 Bioinformatics Scientist (R Proficiency): £35,000 - £60,000 Quantitative Analyst (R Knowledge): £40,000 - £80,000 Research Analyst (R Usage): £25,000 - £50,000 Business Intelligence Developer (R Familiarity): £32,000 - £65,000 Prerequisites This R Programming for Data Science does not require you to have any prior qualifications or experience. You can just enrol and start learning.This R Programming for Data Science 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. Course Curriculum Unit 01: Data Science Overview Introduction to Data Science 00:01:00 Data Science: Career of the Future 00:04:00 What is Data Science? 00:02:00 Data Science as a Process 00:02:00 Data Science Toolbox 00:03:00 Data Science Process Explained 00:05:00 What's next? 00:02:00 Unit 02: R and RStudio Engine and coding environment 00:03:00 Installing R and RStudio 00:04:00 RStudio: A quick tour 00:04:00 Unit 03: Introduction to Basics Arithmetic with R 00:03:00 Variable assignment 00:04:00 Basic data types in R 00:03:00 Unit 04: Vectors Creating a vector 00:05:00 Naming a vector 00:04:00 Arithmetic calculations on vectors 00:07:00 Vector selection 00:06:00 Selection by comparison 00:04:00 Unit 05: Matrices What's a Matrix? 00:02:00 Analyzing Matrices 00:03:00 Naming a Matrix 00:05:00 Adding columns and rows to a matrix 00:06:00 Selection of matrix elements 00:03:00 Arithmetic with matrices 00:07:00 Additional Materials 00:00:00 Unit 06: Factors What's a Factor? 00:02:00 Categorical Variables and Factor Levels 00:04:00 Summarizing a Factor 00:01:00 Ordered Factors 00:05:00 Unit 07: Data Frames What's a Data Frame? 00:03:00 Creating Data Frames 00:20:00 Selection of Data Frame elements 00:03:00 Conditional selection 00:03:00 Sorting a Data Frame 00:03:00 Additional Materials 00:00:00 Unit 08: Lists Why would you need lists? 00:01:00 Creating a List 00:06:00 Selecting elements from a list 00:03:00 Adding more data to the list 00:02:00 Additional Materials 00:00:00 Unit 09: Relational Operators Equality 00:03:00 Greater and Less Than 00:03:00 Compare Vectors 00:03:00 Compare Matrices 00:02:00 Additional Materials 00:00:00 Unit 10: Logical Operators AND, OR, NOT Operators 00:04:00 Logical operators with vectors and matrices 00:04:00 Reverse the result: (!) 00:01:00 Relational and Logical Operators together 00:06:00 Additional Materials 00:00:00 Unit 11: Conditional Statements The IF statement 00:04:00 IFELSE 00:03:00 The ELSEIF statement 00:05:00 Full Exercise 00:03:00 Additional Materials 00:00:00 Unit 12: Loops Write a While loop 00:04:00 Looping with more conditions 00:04:00 Break: stop the While Loop 00:04:00 What's a For loop? 00:02:00 Loop over a vector 00:02:00 Loop over a list 00:03:00 Loop over a matrix 00:04:00 For loop with conditionals 00:01:00 Using Next and Break with For loop 00:03:00 Additional Materials 00:00:00 Unit 13: Functions What is a Function? 00:02:00 Arguments matching 00:03:00 Required and Optional Arguments 00:03:00 Nested functions 00:02:00 Writing own functions 00:03:00 Functions with no arguments 00:02:00 Defining default arguments in functions 00:04:00 Function scoping 00:02:00 Control flow in functions 00:03:00 Additional Materials 00:00:00 Unit 14: R Packages Installing R Packages 00:01:00 Loading R Packages 00:04:00 Different ways to load a package 00:02:00 Additional Materials 00:00:00 Unit 15: The Apply Family - lapply What is lapply and when is used? 00:04:00 Use lapply with user-defined functions 00:03:00 lapply and anonymous functions 00:01:00 Use lapply with additional arguments 00:04:00 Additional Materials 00:00:00 Unit 16: The apply Family - sapply & vapply What is sapply? 00:02:00 How to use sapply 00:02:00 sapply with your own function 00:02:00 sapply with a function returning a vector 00:02:00 When can't sapply simplify? 00:02:00 What is vapply and why is it used? 00:04:00 Additional Materials 00:00:00 Unit 17: Useful Functions Mathematical functions 00:05:00 Data Utilities 00:08:00 Additional Materials 00:00:00 Unit 18: Regular Expressions grepl & grep 00:04:00 Metacharacters 00:05:00 sub & gsub 00:02:00 More metacharacters 00:04:00 Additional Materials 00:00:00 Unit 19: Dates and Times Today and Now 00:02:00 Create and format dates 00:06:00 Create and format times 00:03:00 Calculations with Dates 00:03:00 Calculations with Times 00:07:00 Additional Materials 00:00:00 Unit 20: Getting and Cleaning Data Get and set current directory 00:04:00 Get data from the web 00:04:00 Loading flat files 00:03:00 Loading Excel files 00:05:00 Additional Materials 00:00:00 Unit 21: Plotting Data in R Base plotting system 00:03:00 Base plots: Histograms 00:03:00 Base plots: Scatterplots 00:05:00 Base plots: Regression Line 00:03:00 Base plots: Boxplot 00:03:00 Unit 22: Data Manipulation with dplyr Introduction to dplyr package 00:04:00 Using the pipe operator (%>%) 00:02:00 Columns component: select() 00:05:00 Columns component: rename() and rename_with() 00:02:00 Columns component: mutate() 00:02:00 Columns component: relocate() 00:02:00 Rows component: filter() 00:01:00 Rows component: slice() 00:04:00 Rows component: arrange() 00:01:00 Rows component: rowwise() 00:02:00 Grouping of rows: summarise() 00:03:00 Grouping of rows: across() 00:02:00 COVID-19 Analysis Task 00:08:00 Additional Materials 00:00:00 Assignment Assignment - R Programming for Data Science 00:00:00

Blockchain for Project Management Blockchain is not just a buzzword; it is an exciting new technology that allows users to exchange sensitive data without the need for brokers and other third party mediators. Blockchain technology provides a disruptive new method of conducting transactions over the internet and will ultimately change how we do project management. Enabling encrypted, distributed, and secure logging of digital transactions, blockchain is the underlying technology of Bitcoin and other cryptocurrencies. But it is capable of much more and in this session, we will discuss where and how this technology is useful in project management.This session will explore the intersection of blockchain technology and project management. We will discuss the base concepts of blockchain, in particular, the notion of smart contracts and how to apply them to managing project-related activities. Smart contracts are the most transformative blockchain application. For example, a smart contract could be used to register project status reports or some project work performance data. A smart contract could also be used to automatically send a payment to a vendor as soon as a milestone has been met.Experts tell us that blockchain technology is an effective project management platform because it supports superior operational workflow. In this session, you will learn how implementing a blockchain-based project will lower risk, save time, and lower costs. After completing this session attendees will be able to: Understand how blockchain can be applied to project management Explain the basics of blockchain and the technology that make blockchains possible Distinguish between the components of blockchain

An Introduction to Professional Scrum Although Scrum has been around for more than 25 years, it is still a new concept to many. Moreover, there are myths that arise. In this session, Eric Naiburg, Chief Operating Officer for Scrum.org, provides an overview of the Scrum framework. He'll discuss how Scrum enables agility, and how an empirical process can empower teams that use it. You'll learn about the empirical process in Scrum where decisions are made based on observation and experimentation rather than on detailed upfront planning. We will apply this learning using facts and real-world examples. This and other IIL Learning in Minutes presentations qualify for PDUs. Some titles, such as Agile-related topics may qualify for other continuing education credits such as SEUs, or CEUs. Each professional development activity yields one PDU for one hour spent engaged in the activity. Some limitations apply and can be found in the Ways to Earn PDUs section that discusses PDU activities and associated policies. Fractions of PDUs may also be reported. The smallest increment of a PDU that can be reported is 0.25. This means that if you spent 15 minutes participating in a qualifying PDU activity, you may report 0.25 PDU. If you spend 30 minutes in a qualifying PDU activity, you may report 0.50 PDU. What You Will Learn: Common myths and misconceptions about Scrum The 2020 Scrum Guide, Scrum Events, and Scrum Artefacts The Project Manager and Scrum Accountabilities in Scrum How Scrum can be used with other practices like Kanban, DevOps, Continuous Delivery, and more

Dive deep into the vast realm of Python data science with our meticulously crafted course: 'Python Data Science with Numpy, Pandas and Matplotlib'. Explore the intricate details of Python, setting the stage with Pandas and Numpy, before delving into the power of Python data structures. With topics ranging from Python Strings to Matplotlib Histograms, you'll gain a holistic insight, ensuring that every dataset you touch unveils its story compellingly. So, if you're keen on transmuting raw data into visual masterpieces or insights, this journey is tailor-made for you. Learning Outcomes Grasp foundational knowledge of Python and its data structures like strings, lists, and dictionaries. Understand the potential of NumPy, from basic array operations to handling multi-dimensional arrays. Master the versatility of Pandas, encompassing everything from dataframe conversions to intricate operations like aggregation and binning. Efficiently manage, manipulate, and transform data using Pandas' diverse functionalities. Create visually striking and informative graphs using the power of Matplotlib. Why buy this Python Data Science with Numpy, Pandas and Matplotlib course? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Python Data Science with Numpy, Pandas and Matplotlib 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 £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this Python Data Science with Numpy, Pandas and Matplotlib course for? Beginners eager to jumpstart their journey in Python data science. Analysts looking to enhance their data manipulation skills using Python. Statisticians keen on expanding their toolset with Python-based libraries. Data enthusiasts desiring a deep dive into Python's data libraries and structures. Professionals aiming to upgrade their data visualisation techniques. Prerequisites This Python Data Science with Numpy, Pandas and Matplotlib does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Python Data Science with Numpy, Pandas and Matplotlib 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. Career path Data Scientist: £40,000 - £80,000 Python Developer: £35,000 - £70,000 Data Analyst: £30,000 - £55,000 Business Intelligence Analyst: £32,000 - £60,000 Research Analyst: £28,000 - £52,000 Data Visualization Engineer: £33,000 - £65,000 Course Curriculum Course Introduction and Table of Contents Course Introduction and Table of Contents 00:09:00 Introduction to Python, Pandas and Numpy Introduction to Python, Pandas and Numpy 00:07:00 System and Environment Setup System and Environment Setup 00:08:00 Python Strings Python Strings - Part 1 00:11:00 Python Strings - Part 2 00:09:00 Python Numbers and Operators Python Numbers and Operators - Part 1 00:06:00 Python Numbers and Operators - Part 2 00:07:00 Python Lists Python Lists - Part 1 00:05:00 Python Lists - Part 2 00:06:00 Python Lists - Part 3 00:05:00 Python Lists - Part 4 00:07:00 Python Lists - Part 5 00:07:00 Tuples in Python Tuples in Python 00:06:00 Sets in Python Sets in Python - Part 1 00:05:00 Sets in Python - Part 2 00:04:00 Python Dictionary Python Dictionary - Part 1 00:07:00 Python Dictionary - Part 2 00:07:00 NumPy Library - Introduction NumPy Library Intro - Part 1 00:05:00 NumPy Library Intro - Part 2 00:05:00 NumPy Library Intro - Part 3 00:06:00 NumPy Array Operations and Indexing NumPy Array Operations and Indexing - Part 1 00:04:00 NumPy Array Operations and Indexing - Part 2 00:06:00 NumPy Multi-Dimensional Arrays NumPy Multi-Dimensional Arrays - Part 1 00:07:00 NumPy Multi-Dimensional Arrays - Part 2 00:06:00 NumPy Multi-Dimensional Arrays - Part 3 00:05:00 Introduction to Pandas Series Introduction to Pandas Series 00:08:00 Introduction to Pandas Dataframes Introduction to Pandas Dataframes 00:07:00 Pandas Dataframe conversion and drop Pandas Dataframe conversion and drop - Part 1 00:06:00 Pandas Dataframe conversion and drop - Part 2 00:06:00 Pandas Dataframe conversion and drop - Part 3 00:07:00 Pandas Dataframe summary and selection Pandas Dataframe summary and selection - Part 1 00:06:00 Pandas Dataframe summary and selection - Part 2 00:06:00 Pandas Dataframe summary and selection - Part 3 00:07:00 Pandas Missing Data Management and Sorting Pandas Missing Data Management and Sorting - Part 1 00:07:00 Pandas Missing Data Management and Sorting - Part 2 00:07:00 Pandas Hierarchical-Multi Indexing Pandas Hierarchical-Multi Indexing 00:06:00 Pandas CSV File Read Write Pandas CSV File Read Write - Part 1 00:05:00 Pandas CSV File Read Write - Part 2 00:07:00 Pandas JSON File Read Write Pandas JSON File Read Write Operations 00:07:00 Pandas Concatenation Merging and Joining Pandas Concatenation Merging and Joining - Part 1 00:05:00 Pandas Concatenation Merging and Joining - Part 2 00:04:00 Pandas Concatenation Merging and Joining - Part 3 00:04:00 Pandas Stacking and Pivoting Pandas Stacking and Pivoting - Part 1 00:06:00 Pandas Stacking and Pivoting - Part 2 00:05:00 Pandas Duplicate Data Management Pandas Duplicate Data Management 00:07:00 Pandas Mapping Pandas Mapping 00:04:00 Pandas Grouping Pandas Groupby 00:06:00 Pandas Aggregation Pandas Aggregation 00:09:00 Pandas Binning or Bucketing Pandas Binning or Bucketing 00:08:00 Pandas Re-index and Rename Pandas Re-index and Rename - Part 1 00:04:00 Pandas Re-index and Rename - Part 2 00:05:00 Pandas Replace Values Pandas Replace Values 00:05:00 Pandas Dataframe Metrics Pandas Dataframe Metrics 00:07:00 Pandas Random Permutation Pandas Random Permutation 00:08:00 Pandas Excel sheet Import Pandas Excel sheet Import 00:07:00 Pandas Condition Selection and Lambda Function Pandas Condition Selection and Lambda Function - Part 1 00:05:00 Pandas Condition Selection and Lambda Function - Part 2 00:05:00 Pandas Ranks Min Max Pandas Ranks Min Max 00:06:00 Pandas Cross Tabulation Pandas Cross Tabulation 00:07:00 Matplotlib Graphs and plots Graphs and plots using Matplotlib - Part 1 00:06:00 Graphs and plots using Matplotlib - Part 2 00:02:00 Matplotlib Histograms Matplotlib Histograms 00:03:00 Resource File Resource File - Python Data Science with Numpy, Pandas and Matplotlib 00:00:00