280 Data Structure courses in Sheffield delivered Online

“Master SQL programming and database management with MySQL. Learn to create, manage, and analyze data with SQL’s powerful tools in this comprehensive online course.”

Embark on a journey to master Python with our Diploma in Python Fundamentals course. From basic data types to advanced OOP, learn to code efficiently, handle files, and manage errors in Python. Ideal for beginners and those looking to solidify their programming skills.

Course Overview This comprehensive course offers a deep dive into three essential technologies for data science: Python, JavaScript, and Microsoft SQL. Learners will gain foundational knowledge and practical skills in each of these key areas, which are crucial for handling data, creating interactive websites, and working with databases. By the end of the course, students will be proficient in writing Python code for data analysis, creating dynamic web content with JavaScript, and managing data with Microsoft SQL. The course is designed to equip learners with the technical skills needed to succeed in data science, making it a valuable investment for anyone looking to excel in this growing field. Course Description In this course, learners will explore the core principles of Python, JavaScript, and Microsoft SQL, all tailored to the needs of data science professionals. The curriculum covers Python’s data structures, functions, and libraries essential for data analysis, while JavaScript introduces students to web development skills, including client-side validation and data visualisation. The Microsoft SQL section focuses on data management, including filtering, joining, and structuring queries. Learners will develop a solid understanding of these technologies, which will enable them to manipulate data, automate processes, and design interactive applications. The course also includes real-world applications, ensuring learners are well-prepared for future opportunities in data science and web development. Course Modules: Module 01: JavaScript Getting Started Module 02: JavaScript Fundamentals Module 03: JavaScript Strings Module 04: JavaScript Operators Module 05: JavaScript Conditional Statements Module 06: JavaScript Control Flow Statements Module 07: JavaScript Functions Module 08: Data Visualization (Google Charts) Module 09: JavaScript Error Handling Module 10: JavaScript Client-Side Validations Module 11: Python Introduction Module 12: Python Basic Module 13: Python Strings Module 14: Python Operators Module 15: Python Data Structures Module 16: Python Conditional Statements Module 17: Python Control Flow Statements Module 18: Python Core Games Module 19: Python Functions Module 20: Python Args, KW Args for Data Science Module 21: Python Project Module 22: Publish Your Website for Live Module 23: MS SQL Statements Module 24: MS SQL Filtering Data Module 25: MS SQL Functions Module 26: MS SQL Joins Module 27: MS SQL Advanced Commands Module 28: MS SQL Structure and Keys Module 29: MS SQL Queries Module 30: MS SQL Structure Queries Module 31: MS SQL Constraints Module 32: MS SQL Backup and Restore (See full curriculum) Who is this course for? Individuals seeking to enhance their skills in data science. Professionals aiming to expand their knowledge in programming and database management. Beginners with an interest in Python, JavaScript, and SQL. Anyone looking to enter the field of data science or web development. Career Path Data Scientist Web Developer Database Administrator Data Analyst Front-End Developer Full Stack Developer Data Engineer

Diploma in Python Programming Course Overview The Diploma in Python Programming offers an in-depth exploration of Python, one of the most versatile and in-demand programming languages. This course is designed to provide learners with a strong foundation in Python, covering essential concepts such as data structures, functions, libraries, and file handling. Learners will gain the skills necessary to write Python code to solve real-world problems, enabling them to create applications, automate tasks, and perform data analysis. By the end of the course, learners will have the practical knowledge to use Python effectively for various programming tasks in both professional and personal settings. Course Description This comprehensive course begins with the basics of Python programming, guiding learners through essential concepts such as syntax, data types, and conditional statements. Learners will progress to more advanced topics, including file handling, data storage structures, and error handling. Key modules like the creation of user functions, working with external libraries, and implementing Python in database management provide valuable skills that can be directly applied in the workplace. This course also covers essential tools such as command prompt usage, Jupyter notebooks, and package management in Python. By the end of the course, learners will have developed the confidence and competence to apply Python across various domains, including software development, data analysis, and system automation. Diploma in Python Programming Curriculum Module 01: Introduction to Python Programming Module 02: Getting Started with Python Module 03: Conditional Branching with Python Module 04: Importing External/Internal Library in Python Module 05: Project Rock Paper and Scissors Module 06: Strings Operation in Python Module 07: Date and Time in Python Module 08: File Handling, Read and Write Using Python Module 09: Data Storage Structures: Tuple, List, and Dictionary Module 10: Writing User Functions in Python Module 11: Sending Mail Module 12: Import Tricks in Python Module 13: Import Operating System and Platform Module 14: Exceptions Handling in Python Module 15: Installing Packages and Scheduling in Python Module 16: Database in Python Using SQLite Module 17: Running Programs from Command Prompt and Jupyter Notebook Module 18: Conclusion (See full curriculum) Who is this course for? Individuals seeking to develop a foundational understanding of Python programming. Professionals aiming to enhance their programming skills for career advancement. Beginners with an interest in software development, data analysis, or automation. Anyone looking to pursue a career in programming or technology. Career Path Software Developer Data Analyst Automation Engineer Python Programmer Database Administrator IT Specialist

Learn Web Development from Scratch Course Overview This comprehensive course offers a step-by-step journey through web development, starting from the very basics to advanced concepts. Learners will explore core technologies including HTML, CSS, JavaScript, and Python, gaining the skills necessary to build responsive, interactive websites and dynamic web applications. The course emphasises real-world applications, enabling learners to develop their own web projects and publish them online. By the end of the course, participants will confidently navigate the web development process, from setting up their environment to mastering coding principles and deploying live websites. This course is designed to equip individuals with both foundational knowledge and practical abilities that align with current industry standards, preparing them for career advancement or entry into the tech sector. Course Description This detailed web development course covers a broad spectrum of topics essential for anyone looking to build a solid foundation in creating websites and applications. Starting with environment setup, learners will delve deeply into HTML, progressing through beginner to expert levels, before moving on to CSS for styling and layout control. JavaScript modules provide an introduction to programming logic, data handling, and user interaction techniques, including error handling and client-side validations. The course also introduces Python fundamentals, focusing on its applications in web development and data science. Throughout the modules, learners will engage with structured coding tasks and projects designed to reinforce understanding and boost confidence. The final stages focus on publishing and managing live websites, ensuring learners complete the course ready to contribute effectively in web development roles. Learn Web Development from Scratch Curriculum Module 01: Getting Started Module 02: Setting up Development Environment Module 03: HTML Fundamentals Module 04: HTML Intermediate Module 05: HTML Advanced Module 06: HTML Expert Module 07: HTML Website Project Module 08: CSS Fundamentals Module 09: CSS Intermediate Module 10: CSS Advanced Module 11: CSS Expert Module 12: CSS Website Project Module 13: JavaScript Getting Started Module 14: JavaScript Fundamentals Module 15: JavaScript Strings Module 16: JavaScript Operators Module 17: JavaScript Conditional Statements Module 18: JavaScript Control Flow Statements Module 19: JavaScript Functions Module 20: Data Visualisation (Google Charts) Module 21: JavaScript Error Handling Module 22: JavaScript Client-Side Validations Module 23: Python Introduction Module 24: Python Basic Module 25: Python Strings Module 26: Python Operators Module 27: Python Data Structures Module 28: Python Conditional Statements Module 29: Python Control Flow Statements Module 30: Python Core Games Module 31: Python Functions Module 32: Python Args, KW Args for Data Science Module 33: Python Project Module 34: Publish Your Website for Live (See full curriculum) Who is this course for? Individuals seeking to start a career in web development. Professionals aiming to expand their technical skillset for career growth. Beginners with an interest in coding and digital technologies. Those wanting to build and manage their own websites or web applications. Career Path Junior Web Developer Front-End Developer Web Designer Full-Stack Developer Trainee Software Developer Assistant Digital Content Manager Data Visualisation Specialist

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.

Duration 3 Days 18 CPD hours This course is intended for Java Fundamentals is designed for tech enthusiasts who are familiar with some programming languages and want a quick introduction to the most important principles of Java. Overview After completing this course, you will be able to: Create and run Java programs Use data types, data structures, and control flow in your code Implement best practices while creating objects Work with constructors and inheritance Understand advanced data structures to organize and store data Employ generics for stronger check-types during compilation Learn to handle exceptions in your code Since its inception, Java has stormed the programming world. Its features and functionalities provide developers with the tools needed to write robust cross-platform applications. Java Fundamentals introduces you to these tools and functionalities that will enable you to create Java programs. The course begins with an introduction to the language, its philosophy, and evolution over time, until the latest release. You'll learn how the javac/java tools work and what Java packages are - the way a Java program is usually organized. Once you are comfortable with this, you'll be introduced to advanced concepts of the language, such as control flow keywords. You'll explore object-oriented programming and the part it plays in making Java what it is. In the concluding lessons, you'll be familiarized with classes, typecasting, and interfaces, and understand the use of data structures, arrays, strings, handling exceptions, and creating generics. Introduction to Java The Java Ecosystem Our First Java Application Packages Variables, Data Types, and Operators Variables and Data Types Integral Data Types Type casting Control Flow Conditional Statements Looping Constructs Object-Oriented Programming Object-Oriented Principles Classes and Objects Constructors The this Keyword Inheritance Overloading Constructor Overloading Polymorphism and Overriding Annotations References OOP in Depth Interfaces Typecasting The Object Class Autoboxing and Unboxing Abstract Classes and Methods Data Structures, Arrays, and Strings Data Structures and Algorithms Strings The Java Collections Framework and Generics Reading Data from Files The Java Collections Framework Generics Collection Advanced Data Structures in Java Implementing a Custom Linked List Implementing Binary Search Tree Enumerations Set and Uniqueness in Set Exception Handling Motivation behind Exceptions Exception Sources Exception Mechanics Best Practices

Duration 2 Days 12 CPD hours This course is intended for This course is designed for people who want to learn the Python programming language in preparation for using Python to develop software for a wide range of applications, such as data science, machine learning, artificial intelligence, and web development. Overview In this course, you will develop simple command-line programs in Python. You will: Set up Python and develop a simple application. Declare and perform operations on simple data types, including strings, numbers, and dates. Declare and perform operations on data structures, including lists, ranges, tuples, dictionaries, and sets. Write conditional statements and loops. Define and use functions, classes, and modules. Manage files and directories through code. Deal with exceptions. Though Python has been in use for nearly thirty years, it has become one of the most popular languages for software development, particularly within the fields of data science, machine learning, artificial intelligence, and web development?all areas in which Python is widely used. Whether you're relatively new to programming, or have experience in other programming languages, this course will provide you with a comprehensive first exposure to the Python programming language that can provide you with a quick start in Python, or as the foundation for further learning. You will learn elements of the Python 3 language and development strategies by creating a complete program that performs a wide range of operations on a variety of data types, structures, and objects, implements program logic through conditional statements and loops, structures code for reusability through functions, classes, and modules, reads and writes files, and handles error conditions. Lesson 1: Setting Up Python and Developing a Simple Application Topic A: Set Up the Development Environment Topic B: Write Python Statements Topic C: Create a Python Application Topic D: Prevent Errors Lesson 2: Processing Simple Data Types Topic A: Process Strings and Integers Topic B: Process Decimals, Floats, and Mixed Number Types Lesson 3: Processing Data Structures Topic A: Process Ordered Data Structures Topic B: Process Unordered Data Structures Lesson 4: Writing Conditional Statements and Loops in Python Topic A: Write a Conditional Statement Topic B: Write a Loop Lesson 5: Structuring Code for Reuse Topic A: Define and Call a Function Topic B: Define and Instantiate a Class Topic C: Import and Use a Module Lesson 6: Writing Code to Process Files and Directories Topic A: Write to a Text File Topic B: Read from a Text File Topic C: Get the Contents of a Directory Topic D: Manage Files and Directories Lesson 7: Dealing with Exceptions Topic A: Handle Exceptions Topic B: Raise Exceptions

Duration 5 Days 30 CPD hours This course is intended for This course is designed for students who want to learn the R programming language, particularly students who want to leverage R for data analysis and data science tasks in their organization. The course is also designed for students with an interest in applying statistics to real-world problems. A typical student in this course should have several years of experience with computing technology, along with a proficiency in at least one other programming language. Overview In this course, you will use R to perform common data science tasks.You will: Set up an R development environment and execute simple code. Perform operations on atomic data types in R, including characters, numbers, and logicals. Perform operations on data structures in R, including vectors, lists, and data frames. Write conditional statements and loops. Structure code for reuse with functions and packages. Manage data by loading and saving datasets, manipulating data frames, and more. Analyze data through exploratory analysis, statistical analysis, and more. Create and format data visualizations using base R and ggplot2. Create simple statistical models from data. In our data-driven world, organizations need the right tools to extract valuable insights from that data. The R programming language is one of the tools at the forefront of data science. Its robust set of packages and statistical functions makes it a powerful choice for analyzing data, manipulating data, performing statistical tests on data, and creating predictive models from data. Likewise, R is notable for its strong data visualization tools, enabling you to create high-quality graphs and plots that are incredibly customizable. This course will teach you the fundamentals of programming in R to get you started. It will also teach you how to use R to perform common data science tasks and achieve data-driven results for the business. Lesson 1: Setting Up R and Executing Simple Code Topic A: Set Up the R Development Environment Topic B: Write R Statements Lesson 2: Processing Atomic Data Types Topic A: Process Characters Topic B: Process Numbers Topic C: Process Logicals Lesson 3: Processing Data Structures Topic A: Process Vectors Topic B: Process Factors Topic C: Process Data Frames Topic D: Subset Data Structures Lesson 4: Writing Conditional Statements and Loops Topic A: Write Conditional Statements Topic B: Write Loops Lesson 5: Structuring Code for Reuse Topic A: Define and Call Functions Topic B: Apply Loop Functions Topic C: Manage R Packages Lesson 6: Managing Data in R Topic A: Load Data Topic B: Save Data Topic C: Manipulate Data Frames Using Base R Topic D: Manipulate Data Frames Using dplyr Topic E: Handle Dates and Times Lesson 7: Analyzing Data in R Topic A: Examine Data Topic B: Explore the Underlying Distribution of Data Topic C: Identify Missing Values Lesson 8: Visualizing Data in R Topic A: Plot Data Using Base R Functions Topic B: Plot Data Using ggplot2 Topic C: Format Plots in ggplot2 Topic D: Create Combination Plots Lesson 9: Modeling Data in R Topic A: Create Statistical Models in R Topic B: Create Machine Learning Models in R

Duration 3 Days 18 CPD hours This course is intended for This course is aimed at anyone who wants to harness the power of data analytics in their organization including: Business Analysts, Data Analysts, Reporting and BI professionals Analytics professionals and Data Scientists who would like to learn Python Overview This course teaches delegates with no prior programming or data analytics experience how to perform data manipulation, data analysis and data visualization in Python. Mastery of these techniques and how to apply them to business problems will allow delegates to immediately add value in their workplace by extracting valuable insight from company data to allow better, data-driven decisions. Outcome: After attending this course, delegates will: Be able to write effective Python code Know how to access their data from a variety of sources using Python Know how to identify and fix data quality using Python Know how to manipulate data to create analysis ready data Know how to analyze and visualize data to drive data driven decisioning across your organization Becoming a world class data analytics practitioner requires mastery of the most sophisticated data analytics tools. These programming languages are some of the most powerful and flexible tools in the data analytics toolkit. From business questions to data analytics, and beyond For data analytics tasks to affect business decisions they must be driven by a business question. This section will formally outline how to move an analytics project through key phases of development from business question to business solution. Delegates will be able: to describe and understand the general analytics process. to describe and understand the different types of analytics can be used to derive data driven solutions to business to apply that knowledge to their business context Basic Python Programming Conventions This section will cover the basics of writing R programs. Topics covered will include: What is Python? Using Anaconda Writing Python programs Expressions and objects Functions and arguments Basic Python programming conventions Data Structures in Python This section will look at the basic data structures that Python uses and accessing data in Python. Topics covered will include: Vectors Arrays and matrices Factors Lists Data frames Loading .csv files into Python Connecting to External Data This section will look at loading data from other sources into Python. Topics covered will include: Loading .csv files into a pandas data frame Connecting to and loading data from a database into a panda data frame Data Manipulation in Python This section will look at how Python can be used to perform data manipulation operations to prepare datasets for analytics projects. Topics covered will include: Filtering data Deriving new fields Aggregating data Joining data sources Connecting to external data sources Descriptive Analytics and Basic Reporting in Python This section will explain how Python can be used to perform basic descriptive. Topics covered will include: Summary statistics Grouped summary statistics Using descriptive analytics to assess data quality Using descriptive analytics to created business report Using descriptive analytics to conduct exploratory analysis Statistical Analysis in Python This section will explain how Python can be used to created more interesting statistical analysis. Topics covered will include: Significance tests Correlation Linear regressions Using statistical output to create better business decisions. Data Visualisation in Python This section will explain how Python can be used to create effective charts and visualizations. Topics covered will include: Creating different chart types such as bar charts, box plots, histograms and line plots Formatting charts Best Practices Hints and Tips This section will go through some best practice considerations that should be adopted of you are applying Python in a business context.