Data Science Specialist

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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.

Audience

Beginner, Intermediate

Course Objectives

In this course, you will learn about:

• Fundamentals of big data and data analytics
• Using R programming language for data analysis
• Data exploration and data wrangling
• Data visualization and tools
• Machine learning in data analysis
• Machine learning and Hadoop
• Text mining and mobile analytics
• Data science with Excel and Knime
• Recommendation engines
• Different big data frameworks

Course Outcome

After completing this course, you will be able to:

• Explain big data and data analytics essentials
• Use the R programming language for data analysis
• Understand data exploration and data wrangling
• Analyze and visualize data using different tools
• Describe machine learning in data analysis
• Use machine learning with Hadoop
• Perform text mining and mobile analytics
• Use Excel and Knime for data science
• Use recommendation engines
• Explore different big data frameworks

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