Data Science Certification Course Using R
Access Duration - 365 Days
4.5( 2 REVIEWS )
129 STUDENTS
What Will I Learn?
Gain an introduction to the Data Science Lifecycle
Gain a complete understanding of the tools and techniques in Data Transformation
Learn to perform Text Mining and Analyses
Gain insight into data visualisation
Learn about SMEs in data science training
Overview
If you wish to become an expert in data science, the Data Science Certification Using R is designed to provide individuals in-detail information on the data science lifecycle and machine learning algorithms. You will learn about different techniques and tools, gain insight into optimisation techniques and learn industry-standard best practices.
The Data Science Certification Using R will cover key topics that will get learners up to date on how to analyse and visualise data sets, learn about Decision Trees, Clustering, Naïve Bayes and Random Forest. Data science refers to a concept that will unify data analysis, statistics and related methods to analyse and understand actual data. You will learn to apply your skills in real-life projects by using different tools to gather to derive insight, and gather data sets and interpret it to make effective decisions.
The Data Science Certification Using R will help you master the skills required to tackle real-life data challenges. Qualifying in this course will set you in the right direction, giving you the opportunity to open the door to new and exciting job roles in data science and analytics.
Why You Should Consider Taking this Course at Global Edulink?
Global Edulink is a leading online provider for several accrediting bodies, and provides learners the opportunity to take this exclusive course awarded by CPD. At Global Edulink, we give our fullest attention to our learners’ needs and ensure they have the necessary information required to proceed with the Course. Learners who register will be given excellent support, discounts for future purchases and be eligible for a TOTUM Discount card and Student ID card with amazing offers and access to retail stores, the library, cinemas, gym memberships and their favourite restaurants.
- Access Duration
- Who is this Course for?
- Entry Requirement
- Method of Assessment
- Certification
- Awarding Body
- Career Path & Progression
The course will be directly delivered to you, and you have 12 months access to the online learning platform from the date you joined the course. The course is self-paced and you can complete it in stages, revisiting the lessons at any time.
This course is recommended for,
- Data Scientists
- Business Analysts
- Analytics Managers
- Information Architects
- R Professionals who want to work with Big Data
- Learners should be over the age of 16, and have a basic understanding of English, ICT and numeracy.
- A sound educational background is recommended
- Basic understanding of R is beneficial
In order to qualify in the course ‘Data Science Certification Using R’ successfully, learners will take an online test with each module being rounded off with multiple-choice questions. This online test is marked automatically, so you will receive an instant grade and know whether you have passed the course.
Upon the successful completion of the course, you will be awarded the Data Science Certification Using R by CPD.
CPD is a leading awarding body in the United Kingdom that meets an excellent standard of high quality education. CPD is committed towards the enhancement of proficiency and personal skills in order to develop learners’ skills and abilities. CPD ensures that both practical and academic qualifications assist individuals to re-skill or up-skill and maintain a competitive advantage in their chosen industry.
The Data Science Certification Using R will improve your candidature for a number of jobs in data analytics. You can study further courses in the same field and enhance your academic expertise in data science, and add this as a valuable skillset on your resume. Your skills will be recognised by leading organisations that give you the opportunity to land a well-paying job. Given below are job titles you can compete for, along with the average UK salary per annum according to https://www.glassdoor.com.
- Data Analyst - £55,000 (Approximately)
- Business Analyst - £67,000 (Approximately)
- Analytics Manager - £79,000 (Approximately)
- Information Architects - £90,000 (Approximately)
Key Features
Gain an Accredited UK Qualification
Access to Excellent Quality Study Materials
Personalised Learning Experience
Support by Phone, Live Chat, and Email
Eligible for TOTUM Discount Card
UK Register of Learning Providers Reg No : 10053842Course Curriculum
| 1: Introduction to Data Science | |||
| What is Data Science? | |||
| What does Data Science involve? | |||
| Era of Data Science | |||
| Business Intelligence vs Data Science | |||
| Life cycle of Data Science | |||
| Tools of Data Science | |||
| Introduction to Big Data and Hadoop | |||
| Introduction to R | |||
| Introduction to Spark | |||
| Introduction to Machine Learning | |||
| 2: Statistical Inference | |||
| What is Statistical Inference? | |||
| Terminologies of Statistics | |||
| Measures of Centers | |||
| Measures of Spread | |||
| Probability | |||
| Normal Distribution | |||
| Binary Distribution | |||
| 3: Data Extraction, Wrangling and Exploration | |||
| Data Analysis Pipeline | |||
| What is Data Extraction | |||
| Types of Data | |||
| Raw and Processed Data | |||
| Data Wrangling | |||
| Exploratory Data Analysis | |||
| Visualization of Data | |||
| 4: Introduction to Machine Learning | |||
| What is Machine Learning? | |||
| Machine Learning Use-Cases | |||
| Machine Learning Process Flow | |||
| Machine Learning Categories | |||
| Supervised Learning algorithm: Linear Regression and Logistic Regression | |||
| 5: Classification Techniques | |||
| What are classification and its use cases? | |||
| What is Decision Tree? | |||
| Algorithm for Decision Tree Induction | |||
| Creating a Perfect Decision Tree | |||
| Confusion Matrix | |||
| What is Random Forest? | |||
| What is Naive Bayes? | |||
| Support Vector Machine: Classification | |||
| 6: Unsupervised Learning | |||
| What is Clustering & its Use Cases? | |||
| What is K-means Clustering? | |||
| What is C-means Clustering? | |||
| What is Canopy Clustering? | |||
| What is Hierarchical Clustering? | |||
| 7: Recommender Engines | |||
| What is Association Rules & its use cases? | |||
| What is Recommendation Engine & it’s working? | |||
| Types of Recommendations | |||
| User-Based Recommendation | |||
| Item-Based Recommendation | |||
| Difference: User-Based and Item-Based Recommendation | |||
| Recommendation use cases | |||
| 8: Text Mining | |||
| The concepts of text-mining | |||
| Use cases | |||
| Text Mining Algorithms | |||
| Quantifying text | |||
| TF-IDF | |||
| Beyond TF-IDF | |||
| 9: Time Series | |||
| What is Time Series data? | |||
| Time Series variables | |||
| Different components of Time Series data | |||
| Visualize the data to identify Time Series Components | |||
| Implement ARIMA model for forecasting | |||
| Exponential smoothing models | |||
| Identifying different time series scenario based on which different Exponential Smoothing model can be applied | |||
| Implement respective ETS model for forecasting | |||
| 10: Deep Learning | |||
| Reinforced Learning | |||
| Reinforcement learning Process Flow | |||
| Reinforced Learning Use cases | |||
| Deep Learning | |||
| Biological Neural Networks | |||
| Understand Artificial Neural Networks | |||
| Building an Artificial Neural Network | |||
| How ANN works | |||
| Important Terminologies of ANN’s | |||
Ash Watts
The material provided a wealth of information on machine learning algorithms. I gained a conceptual understanding of Statistics, Text Mining and Time Series.
Blair Hopkins
It was a successful course exploring key topics on data science and what it involves and business intelligence vs. data science.