314 Algorithms courses delivered Online

Duration 5 Days 30 CPD hours This course is intended for This intermediate and beyond level course is geared for experienced technical professionals in various roles, such as developers, data analysts, data engineers, software engineers, and machine learning engineers who want to leverage Scala and Spark to tackle complex data challenges and develop scalable, high-performance applications across diverse domains. Practical programming experience is required to participate in the hands-on labs. Overview Working in a hands-on learning environment led by our expert instructor you'll: Develop a basic understanding of Scala and Apache Spark fundamentals, enabling you to confidently create scalable and high-performance applications. Learn how to process large datasets efficiently, helping you handle complex data challenges and make data-driven decisions. Gain hands-on experience with real-time data streaming, allowing you to manage and analyze data as it flows into your applications. Acquire practical knowledge of machine learning algorithms using Spark MLlib, empowering you to create intelligent applications and uncover hidden insights. Master graph processing with GraphX, enabling you to analyze and visualize complex relationships in your data. Discover generative AI technologies using GPT with Spark and Scala, opening up new possibilities for automating content generation and enhancing data analysis. Embark on a journey to master the world of big data with our immersive course on Scala and Spark! Mastering Scala with Apache Spark for the Modern Data Enterprise is a five day hands on course designed to provide you with the essential skills and tools to tackle complex data projects using Scala programming language and Apache Spark, a high-performance data processing engine. Mastering these technologies will enable you to perform a wide range of tasks, from data wrangling and analytics to machine learning and artificial intelligence, across various industries and applications.Guided by our expert instructor, you?ll explore the fundamentals of Scala programming and Apache Spark while gaining valuable hands-on experience with Spark programming, RDDs, DataFrames, Spark SQL, and data sources. You?ll also explore Spark Streaming, performance optimization techniques, and the integration of popular external libraries, tools, and cloud platforms like AWS, Azure, and GCP. Machine learning enthusiasts will delve into Spark MLlib, covering basics of machine learning algorithms, data preparation, feature extraction, and various techniques such as regression, classification, clustering, and recommendation systems. Introduction to Scala Brief history and motivation Differences between Scala and Java Basic Scala syntax and constructs Scala's functional programming features Introduction to Apache Spark Overview and history Spark components and architecture Spark ecosystem Comparing Spark with other big data frameworks Basics of Spark Programming SparkContext and SparkSession Resilient Distributed Datasets (RDDs) Transformations and Actions Working with DataFrames Spark SQL and Data Sources Spark SQL library and its advantages Structured and semi-structured data sources Reading and writing data in various formats (CSV, JSON, Parquet, Avro, etc.) Data manipulation using SQL queries Basic RDD Operations Creating and manipulating RDDs Common transformations and actions on RDDs Working with key-value data Basic DataFrame and Dataset Operations Creating and manipulating DataFrames and Datasets Column operations and functions Filtering, sorting, and aggregating data Introduction to Spark Streaming Overview of Spark Streaming Discretized Stream (DStream) operations Windowed operations and stateful processing Performance Optimization Basics Best practices for efficient Spark code Broadcast variables and accumulators Monitoring Spark applications Integrating External Libraries and Tools, Spark Streaming Using popular external libraries, such as Hadoop and HBase Integrating with cloud platforms: AWS, Azure, GCP Connecting to data storage systems: HDFS, S3, Cassandra, etc. Introduction to Machine Learning Basics Overview of machine learning Supervised and unsupervised learning Common algorithms and use cases Introduction to Spark MLlib Overview of Spark MLlib MLlib's algorithms and utilities Data preparation and feature extraction Linear Regression and Classification Linear regression algorithm Logistic regression for classification Model evaluation and performance metrics Clustering Algorithms Overview of clustering algorithms K-means clustering Model evaluation and performance metrics Collaborative Filtering and Recommendation Systems Overview of recommendation systems Collaborative filtering techniques Implementing recommendations with Spark MLlib Introduction to Graph Processing Overview of graph processing Use cases and applications of graph processing Graph representations and operations Introduction to Spark GraphX Overview of GraphX Creating and transforming graphs Graph algorithms in GraphX Big Data Innovation! Using GPT and Generative AI Technologies with Spark and Scala Overview of generative AI technologies Integrating GPT with Spark and Scala Practical applications and use cases Bonus Topics / Time Permitting Introduction to Spark NLP Overview of Spark NLP Preprocessing text data Text classification and sentiment analysis Putting It All Together Work on a capstone project that integrates multiple aspects of the course, including data processing, machine learning, graph processing, and generative AI technologies.

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Parsing or syntactic analysis is one of the first stages in designing and implementing a compiler. Implementing a full manual parser from scratch allows understanding and seeing this process from the inside, demystifying internal structures, and turning building parsers into an interesting engineering task.

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If you’re looking to start a career in Python coding, but don’t know where to begin, this might be for you. This course is aimed at absolute beginners that have never done any coding before. Early on in the course, you’ll learn what coding is, what certain types of languages are used for, specifically Python, and the types of careers available through learning Python.

In this course you will learn how to use the power of Python to train your machine such that your machine starts learning just like human and based on that learning, your machine starts making predictions as well!

Duration 1 Days 6 CPD hours This course is intended for Software Engineers Overview The objective of this course is to learn the key language concepts to machine learning, Spark MLlib, and Spark ML. This course will teach you the key language concepts to machine learning, Spark MLlib, and Spark ML. The course includes coverage of collaborative filtering, clustering, classification, algorithms, and data volume. This course will teach you the key language concepts to machine learning, Spark MLlib, and Spark ML. The course includes coverage of collaborative filtering, clustering, classification, algorithms, and data volume.

Duration 2 Days 12 CPD hours This course is intended for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful. Overview By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data. This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You?ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you?ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. Data Exploration and Cleaning Python and the Anaconda Package Management System Different Types of Data Science Problems Loading the Case Study Data with Jupyter and pandas Data Quality Assurance and Exploration Exploring the Financial History Features in the Dataset Activity 1: Exploring Remaining Financial Features in the Dataset Introduction to Scikit-Learn and Model Evaluation Introduction Model Performance Metrics for Binary Classification Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve Details of Logistic Regression and Feature Exploration Introduction Examining the Relationships between Features and the Response Univariate Feature Selection: What It Does and Doesn't Do Building Cloud-Native Applications Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients The Bias-Variance Trade-off Introduction Estimating the Coefficients and Intercepts of Logistic Regression Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters Activity 4: Cross-Validation and Feature Engineering with the Case Study Data Decision Trees and Random Forests Introduction Decision trees Random Forests: Ensembles of Decision Trees Activity 5: Cross-Validation Grid Search with Random Forest Imputation of Missing Data, Financial Analysis, and Delivery to Client Introduction Review of Modeling Results Dealing with Missing Data: Imputation Strategies Activity 6: Deriving Financial Insights Final Thoughts on Delivering the Predictive Model to the Client

Recent developments in data analytics and statistics underscore the critical importance of understanding both data structure and analytics methodologies in today's data-driven world. With the exponential growth of data, businesses are increasingly relying on skilled professionals who can harness the power of data to drive informed decision-making. Our comprehensive Data Structure, Data Analytics with Statistics & Data Science QLS Endorsed Diploma bundle, endorsed by the Quality Licence Scheme (QLS) and accredited by the CPD Quality Standards (QS), offers a holistic approach to mastering data structure, data analytics, and statistical techniques. In an era where data reigns supreme, organisations seek individuals who can navigate complex datasets with confidence and precision. This Data Structure, Data Analytics bundle equips learners with the essential skills and knowledge needed to excel in the field of data science and analytics. By delving into topics such as data manipulation, statistical analysis, and database management, participants gain a deep understanding of how to extract valuable insights from raw data. Moreover, our guided courses in career development and communication empower learners to effectively communicate their findings and advance their professional journey. Bundle Include includes: QLS Endorsed Courses: Course 01: Certificate in Data Analytics with Tableau at QLS Level 3 Course 02: Diploma in Data Structure at QLS Level 5 Course 03: Advanced Diploma in Statistics & Probability for Data Science & Machine Learning at QLS Level 7 CPD QS Accredited Courses: Course 04: Business and Data Analytics for Beginners Course 05: Learn Financial Analytics and Statistical Tools Course 06: SQL For Data Analytics & Database Development Course 07: Big Data Analytics with PySpark Power BI and MongoDB Course 08: Google Data Studio: Data Analytics Course 09: Business Intelligence Analyst Course 10: Spatial Data Visualization and Machine Learning in Python Course 11: Data Analysis In Excel Take your career to the next level with our Data Structure, Data Analytics bundle that includes technical courses and five guided courses focused on personal development and career growth. Course 12: Career Development Plan Fundamentals Course 13: CV Writing and Job Searching Course 14: Networking Skills for Personal Success Course 15: Ace Your Presentations: Public Speaking Masterclass Course 16: Decision Making and Critical Thinking Seize this opportunity to elevate your career with our comprehensive Data Structure, Data Analytics bundle, endorsed by the prestigious QLS and accredited by CPD.Data Structure, Data Analytics with Statistics & Data Science QLS Endorsed Diploma. Learning Outcomes: Upon completion of this Data Structure, Data Analytics bundle, participants will be able to: Demonstrate proficiency in data analytics tools such as Tableau, SQL, and Google Data Studio through Data Structure, Data Analytics courses. Apply statistical techniques to analyse and interpret data for informed decision-making. Design and implement data structures to efficiently store and retrieve information. Utilise machine learning algorithms for predictive analysis and pattern recognition. Develop effective communication and presentation skills to convey insights to stakeholders. Navigate career development pathways in the field of data science and analytics. This Data Structure, Data Analytics course bundle provides a deep dive into the foundational principles of data structure, data analytics, and statistical methodologies. Participants will explore the fundamental concepts of data manipulation, including sorting, searching, and storing data efficiently. Through hands-on exercises and theoretical discussions, learners will gain a solid understanding of various data structures such as arrays, linked lists, trees, and graphs, along with their applications in real-world scenarios. Moreover, the Data Structure, Data Analytics bundle encompasses a comprehensive exploration of data analytics techniques, equipping participants with the skills to extract actionable insights from complex datasets. From descriptive and inferential statistics to predictive modelling and machine learning algorithms, learners will discover how to uncover patterns, trends, and correlations within data, enabling informed decision-making and strategic planning. Throughout the Data Structure, Data Analytics course, emphasis is placed on practical applications and case studies, allowing participants to apply their knowledge to solve real-world problems in diverse domains. CPD 160 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This Data Structure, Data Analytics course is perfect for: Aspiring data analysts seeking to enhance their analytical skills acrod Data Structure, Data Analytics courses. Professionals transitioning into roles requiring proficiency in data analytics. Students pursuing careers in data science, statistics, or related fields. Business professionals looking to leverage data for strategic decision-making. Individuals interested in advancing their career prospects in the field of data analytics. Anyone seeking to gain a comprehensive understanding of data structure, analytics, and statistics. Requirements You are warmly invited to register for this bundle. Please be aware that there are no formal entry requirements or qualifications necessary. This curriculum has been crafted to be open to everyone, regardless of previous experience or educational attainment. Career path Upon completion of the Data Structure, Data Analytics courses, you will be able to: Data Analyst Business Intelligence Analyst Data Scientist Statistician Database Administrator Machine Learning Engineer Data Engineer Certificates 13 CPD Quality Standard Certificates Digital certificate - Included 3 QLS Endorsed Certificates Hard copy certificate - Included