Data Science Specialist

Data is everywhere and it is transforming our world. Almost all industries are bracing big data and using different data analysis techniques to dig out valuable insights and create data-driven solutions for their challenges. R is rapidly becoming the leading programming language for effective data analysis and statistics. It is the tool of choice for many data science professionals in every industry. More and more companies are hiring professionals who can analyse data and uncover insights to make better decisions.

Star Data Science (SDS) is a certification program that introduces you to the world of data, its science and analytics. It helps you get started on your data science journey and build the skill-set required to tackle the real-world data analysis challenges as a data engineer. The program focuses on working with and exploring data using a variety of visualization, analytical, and statistical techniques. The SDS introduces the learners to R programming and how to use R for effective data analysis, detailing all aspects of R from data exploration and data wrangling, further to data analytics and visualization and to text mining and mobile analytics.

The program helps learners master the machine learning concepts and its capabilities in data visualization, and further discusses key concepts such as regression techniques, decision tree, recommendation engines, big data frameworks such as Hadoop, HIVE, MapReduce and Azure.


Beginner, Intermediate

Course Objectives

In this course, you will learn about:

Course Outcome

After completing this course, you will be able to:

Table of Contents Outline

  1. Introduction to Data Science and Analytics
  2. Exploring Big Data and Types of Data
  3. The Lifecycle of Data Science
  4. Getting Started with R
  5. Introduction to Statistics and Probability with R
  6. Data Exploration and Data Wrangling
  7. Data Visualization and Tools
  8. Handling Real World Data
  9. Ethics and Law in Data and Analytic
  10. Introduction to Machine Learning
  11. Linear Regression Techniques
  12. Logistic Regression Techniques
  13. Decision Trees
  14. Time Series Analysis
  15. Unsupervised Learning
  16. Text Mining and Analytics
  17. Exploring Mobile Analytics
  18. Using No-SQL and Transact-SQL in Data Science
  19. Exploring Data Science with Excel and Knime
  20. Recommendation Engines
  21. Big Data Frameworks (Hadoop/HIVE/MapReduce/Azure/ Machine Learning)
  22. Machine Learning and Hadoop
  23. Documentation and Deployment
  24. Data Science Tools and Applications

Exam Details

Exam Codes SDSS S08-520 (Academy customers use the same codes)
Number of Questions 60
Type of questions Multiple Choice
Length of Test 120 mins
Passing Score 70%
Recommended Experience SDS certification assumes the learner is new to data science and wants to
learn how to leverage big data and perform data analysis. No
knowledge of programming is required to take this course. Basic
knowledge of mathematics concepts is preferred.
Languages English
Registration link Closed