Certified Data Science Specialist (CDSS)
Course overview
This course is a hands-on guided course for you to learn the concepts, tools, and techniques that you need to begin learning data science. We will cover the key topics from data science to big data, and the processes of gathering, cleaning and handling data. This course is well balanced between theory and practical, and key concepts are taught using case studies references. Upon completion, participants will be able to perform the basic data handling tasks, collect and analyze data, and present them using industry standard tools.
Course Duration
5 Days
Cost
Audience
This workshop is intended for individuals who are interested in learning data science, or who want to begin their career as a data scientist.
Prerequisites
All participants should have basic understanding of data, relations, and basic knowledge of mathematics.
Course Content
DAY 1
Introduction to Data Science
• What is Data?
• Types of Data
• What is Data Science?
• Statistical thinking
• Knowledge Check
• Lab Activity
Data Processes
• Extract, Transform and Load (ETL)
• Data Cleansing
• Aggregation, Filtering, Sorting, Joining
• Data Workflow
• Knowledge Check
• Lab Activity
Data Quality
• Raw vs Tidy Data
• Key features of data quality
• Maintenance of data quality
• Data profiling
• Data completeness and consistency
Life of a data scientist
• Identify problem
• Define question
• Define ideal dataset
• Obtain data
• Analyze data
• Interpret results
• Distribute results
· Knowledge Check
DAY 2
Beginning Databases
• Types of Databases
• Relational Databases
• NoSQL
• Hybrid database
• Knowledge check
• Lab activity
Structured Query Language (SQL)
• Performing CRUD (Create, Retrieve, Update, Delete)
• Designing a Real world database
• Normalizing a table
• Knowledge check
• Lab Activity
Introduction to Python
• Basics of Python language
• Functions and packages
• Python lists
• Functional programming in Python
• Numpy and Scipy
• iPython
• Knowledge check
• Lab Activity
Lab: Exploring data using Python
DAY 3
Data Gathering
• Obtain data from online repositories
• Import data from local file formats (json, xml)
• Import data using Web API
• Scrape website for data
• Knowledge check
• Lab Activity
Instructor-led case study
Exploratory Data Analysis
• What is EDA?
• Goals of EDA
• The role of graphics
• Handling outliers
• Dimension reduction
Introduction to R
• Features of R
• Vectors
• Matrices and Arrays
• Data Frame
• Input / Output
Lab: Exploring data using R
DAY 4
Introduction Text Mining
• What is Text Mining?
• Natural Language Processing
• Pre-processing text data
• Extracting features from documents
• Using BeautifulSoup
• Measuring document similarity
• Knowledge check
• Lab activity
Supervised Learning
• What is prediction?
• Sampling, training set, testing set.
• Constructing a decision tree.
• Knowledge check
• Lab Activity
DAY 5
Presenting Data
• Choosing the right visualization
• Plotting data using Python libraries
• Plotting data using R
• Using Jupyter Notebook to validate scripts
• Knowledge check
• Lab activity
Data Analysis Presentation
• Using Markdown language
• Convert your data into slides
• Data presentation techniques
• The pitfall of data analysis
• Knowledge check
• Lab activity
• Group presentation
Lab: Mini Project
Big Data Landscape
• What is small data?
• What is big data?
• Big data analytics vs Data Science
• Key elements in Big Data (3Vs)
• Extracting values from big data
• Challenges in Big data
Big data Tools and Applications
• Introducing Hadoop Ecosystem
• Cloudera vs Hortonworks
• Real world big data applications
• Knowledge check
• Group discussion
What’s next?
• Preview of Data Science Specialist
• Showing advanced data analysis techniques
• Demo: Interactive visualizations