Data Science

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Craft Your Big Data Future: Explore Our Data Science Programs Now.

Big Data is essential to thriving in today’s data-driven world. From optimizing operations to uncovering hidden trends, Big Data analytics unlock unparalleled insights, driving smarter decision-making and propelling growth. But navigating the vast ocean of Big Data requires the right tools and expertise. Enter Hadoop, the industry-leading open-source framework that empowers you to conquer Big Data’s challenges.

Hadoop: Your Big Data Compass

Hadoop tames the Big Data beast, efficiently storing and processing massive datasets on commodity hardware. Its distributed and fault-tolerant architecture ensures data availability and processing even in hardware failures. Imagine a fleet of ships, each carrying a piece of the Big Data puzzle, working in unison to analyze it at lightning speed. That’s Hadoop’s parallel processing power.

Mastering the Hadoop Ecosystem: Your Passport to Big Data Success

Hadoop goes beyond storage and processing. It’s a suite of powerful tools like HDFS, MapReduce, Spark, HBase, Hive, Pig, Oozie, Sqoop, and Flume. Each tool offers unique functionalities for wrangling, analyzing, and extracting valuable insights from your Big Data treasure trove.

GGT Hadoop Training: Your Stepping Stone to Big Data Mastery

Our comprehensive online course is your passport to becoming a sought-after Big Data professional. Learn from seasoned experts, immerse yourself in hands-on projects, and gain the skills and knowledge needed to excel in this booming field. Get certified, work on real-world projects, and unlock your potential to transform businesses with data-driven insights.

Don’t wait to embark on your Big Data Odyssey! Enroll in GGT Hadoop Training today and become a data-driven leader in your field.

Become a Digital Guardian

Start Your Data Science Today.

Ready to join the hottest industry with skyrocketing demand and high salaries?

This Data Science  Certification Program is your launchpad to a rewarding career protecting organizations from cyber threats.

Master your craft with industry veterans

Our online training boasts expert instructors with real-world experience, empowering you with cutting-edge skills.

Work-life balance, redefined

Enjoy the flexibility and comfort of working from home while tackling exciting cybersecurity challenges.

Get real-world Expert-led Online Training

Put your skills to the test, build your portfolio, and impress future employers.

Land your dream job with confidence

We don't just train you, we guide you. Get personalized career support.

Earn Above the Average in a Booming Field.

Leave the average behind and blast into a booming field with a salary to match!

Average Salary for Junior Position
64K £
Average Salary You can Earn
100K £
Average Salary For Higher Position
200K £

What will you learn from this course?

Learn data types, variables, functions, loops, and more advanced subjects like object-oriented programming, databases, and algorithms. Also learn how to test, debug, and write your own programs.

  • Data science Introduction
  • Data Science
  • Project Life cycle
  • CRISP - DM Model
  • Introduction to Statistics Concepts
  • Mean, Mode, Median
  • Probability
  • Probability Distribution
  • Binomial Distribution
  • Poisson Distribution

Gain fluency in both R and Python, for proficient use in later modules. Topics include random number generation, using vectors and matrices, working with APIs, and reading from/writing to different file formats.

The module also covers practices for ensuring code correctness, such as profiling, debugging and unit testing, and how to package code for distribution.

Gain familiarity with statistical and mathematical tools that will be used in later modules.

You’ll review the fundamentals of calculus, linear algebra, and probability theory, as well as other topics including matrix decomposition techniques, convergence of random variables, sample-based statistical inference, and numerical optimisation methods.

Produce convincing narrative summaries and informative visualisations for a variety of complex datasets.

You’ll learn how to evaluate the quality of a given dataset, diagnose and remedy missing and anomalous data, and consider the suitability of different exploratory analyses for various data types including spatial and temporal data.

Become familiar with data analysis and modelling, classification and resampling methods, and advanced topics like Random Forest and Support Vector Machines.

Gain the skills and knowledge to choose the appropriate supervised learning technique to effectively analyse and interpret data.

Build on your existing knowledge of ethics in data science and artificial intelligence and explore real-world issues.

Learn statistical concepts such as parameter estimation with large scale data and explore data sampling strategies in a Big Data world.

Design and develop data analysis procedures using Big Data technology (Hadoop and Spark), learn to utilise Big Data technology to perform a rigorous statistical analysis, and describe and apply mathematical techniques for fitting statistical models at scale and dealing with streaming data.

Examine subjective probabilities and the Bayesian paradigm for making coherent individual decisions in the presence of uncertainty.

Select and explore an appropriate deep learning model architecture for a given supervised and unsupervised learning application.

You’ll be able to implement data and training pipelines for different types of neural networks, as well as implement appropriate evaluation measures and model selection strategies for supervised and unsupervised applications.

Assess the tools and techniques for solving unsupervised learning challenges, exploring topics including clustering, dimension reduction and density estimation.

Complete your studies examining the ethical implications of data science and artificial intelligence.

Learn the mathematics of techniques dealing with three unstructured data types: images, networks, and text.

Master deep learning and well-established statistical methods to tackle unstructured data and implement statistical and machine learning tasks.

Investigate key decision-making frameworks and develop expertise for taking machine learning beyond the prediction process to formal decision-making processes.