1003 Data Science courses in London delivered Online

I Asked A Python Programmer For A Joke. He Said, 'Import Antigravity' | 10 QLS Endorsed Courses for Python Programmer | 10 QLS Endorsed Hard Copy Certificates Included | Lifetime Access | Installment Payment | Tutor Support

Data speaks volumes—if you know how to listen. This course offers a clever route into the world of MySQL, showing you how structured data can turn into smart decisions. Whether you're sifting through figures, building dashboards, or deciphering patterns, understanding SQL isn’t just helpful—it’s essential. You’ll be guided through the foundations of MySQL with clarity and precision, making sense of tables, queries, joins, and more—all without the fluff or confusion. Designed with the analytical mind in focus, this course shines a light on how MySQL powers insights behind the scenes in data science and analytics. It's tailored for learners who prefer clarity over jargon and structure over noise. If you're curious about how databases work and how data fuels smart outcomes, this course is your ideal stepping stone—organised, engaging and purposefully built for those who appreciate logic and clean learning paths. Expert Support Dedicated tutor support and 24/7 customer support are available to all students with this premium quality course. Key Benefits Learning materials of the Design course contain engaging voiceover and visual elements for your comfort. Get 24/7 access to all content for a full year. Each of our students gets full tutor support on weekdays (Monday to Friday) Course Curriculum: Section 01: Getting Started Introduction How to get course requirements Getting started on Windows, Linux or Mac How to ask great questions FAQ's What is Source Code? Section 02: SQL Server setting up Section Introduction MySQL Server Installation Connect MySQL Server Instance MySQL Workbench overview Download and Restore Sample Database Section 03: SQL Database basics Section Introduction Overview of Databases Creating Database SQL Data Types Column Data Types on Workbench Creating Table Overview of Primary and Foreign Key Primary Key Foreign Key Creating Temporary tables EER - Enhanced Entity Relationship Diagrams Section 04: SQL DML (Data Manipulation Language) Section Introduction Insert statement Update statement Delete statement Section 05: SQL DDL (Data Definition Language) Section Introduction CREATE table statement DROP statement ALTER statement TRUNCATE statement COMMENT in query RENAME table Section 06: SQL DCL (Data Control Language) Create Database user GRANT permissions REVOKE permissions Section 07: SQL Statement Basic Section Introduction SQL Statement basic SELECT Statement SELECT DISTINCT SELECT with column headings Column AS statement DASHBOARD Analytics Section 08: Filtering Data rows SELECT WHERE Clause - theory SELECT WHERE Clause - practical Section 09: Aggregate functions for Data Analysis Sum() Min()-Max() Section 10: SQL Data Analyticstatements Order By statement SELECT TOP 3 records BETWEEN command IN operator Search Data usingLIKE cards Section 11: SQL Group by statement Section Introduction Group by - theory Data Analytics with Group By HAVING statement Section 12: JOINS Overview of Joins What are Joins Inner join Left outer join Right outer join Union CERTESIAN Product or Cross Join Query Exercise Solution for Query Exercise Section 13: SQL Constraints Section introduction Check constraint NOT NULL constraint UNIQUE constraint Section 14: Views Creating Views Data Analytic Viewsfrom multiple tables Section 15: Advanced SQL Functions Section Introduction Timestamp Extract from timestamp Mathematical scalar functions String functions Advanced functions Sub Queries SELECT with calculations Section 16: SQL Stored procedures Create stored procedure Stored procedure with parameter Drop Procedure Section 17: Import & Export data Section Introduction Import .csv file Export Data to .csv file Section 18: Backup and Restore Database Section Introduction Creating Database backup Restoring Database backup Exam & Retakes: It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable. Certification Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery). Who is this course for? This Learn MySQL from Scratch for Data Science and Analytics course is designed to enhance your expertise and boost your CV. Learn key skills and gain a certificate of achievement to prove your newly-acquired knowledge. Requirements This Learn MySQL from Scratch for Data Science and Analytics course is open to all, with no formal entry requirements. Career path Upon successful completion of The Learn MySQL from Scratch for Data Science and Analytics Course, learners will be equipped with many indispensable skills and have the opportunity to grab.

Duration 4 Days 24 CPD hours This course is intended for The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview Overview of data science and machine learning at scale Overview of the Hadoop ecosystem Working with HDFS data and Hive tables using Hue Introduction to Cloudera Data Science Workbench Overview of Apache Spark 2 Reading and writing data Inspecting data quality Cleansing and transforming data Summarizing and grouping data Combining, splitting, and reshaping data Exploring data Configuring, monitoring, and troubleshooting Spark applications Overview of machine learning in Spark MLlib Extracting, transforming, and selecting features Building and evaluating regression models Building and evaluating classification models Building and evaluating clustering models Cross-validating models and tuning hyperparameters Building machine learning pipelines Deploying machine learning models Spark, Spark SQL, and Spark MLlib PySpark and sparklyr Cloudera Data Science Workbench (CDSW) Hue This workshop covers data science and machine learning workflows at scale using Apache Spark 2 and other key components of the Hadoop ecosystem. The workshop emphasizes the use of data science and machine learning methods to address real-world business challenges. Using scenarios and datasets from a fictional technology company, students discover insights to support critical business decisions and develop data products to transform the business. The material is presented through a sequence of brief lectures, interactive demonstrations, extensive hands-on exercises, and discussions. The Apache Spark demonstrations and exercises are conducted in Python (with PySpark) and R (with sparklyr) using the Cloudera Data Science Workbench (CDSW) environment. The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview of data science and machine learning at scaleOverview of the Hadoop ecosystemWorking with HDFS data and Hive tables using HueIntroduction to Cloudera Data Science WorkbenchOverview of Apache Spark 2Reading and writing dataInspecting data qualityCleansing and transforming dataSummarizing and grouping dataCombining, splitting, and reshaping dataExploring dataConfiguring, monitoring, and troubleshooting Spark applicationsOverview of machine learning in Spark MLlibExtracting, transforming, and selecting featuresBuilding and evauating regression modelsBuilding and evaluating classification modelsBuilding and evaluating clustering modelsCross-validating models and tuning hyperparametersBuilding machine learning pipelinesDeploying machine learning models Additional course details: Nexus Humans Cloudera Data Scientist Training training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Cloudera Data Scientist Training course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Begin your machine learning journey by learning all about linear regression, logistic regression, and cluster analysis

In this course, you will learn the fundamentals of data visualization in Python using the well-known Matplotlib and Seaborn data science libraries and perform exploratory data analysis (EDA) by visualizing a data set using a variety of charts.

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In this course, you will quickly learn how to build DNNs (Deep Neural Networks) and how to train them. This learning-by-doing course will also help you master the elementary concepts and methodology with Python. You need to have a basic knowledge of python to get the best out of this course.

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This course begins with establishing the motivation for reinforcement learning and then progresses on to equipping you with all the necessary theory. Each section of the course helps you not only understand the fundamentals of RL but also gain necessary coding skills by taking you through exercises. By the end of the course, you will be able to complete a project using the OpenAI Gym toolkit.