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Certified Data Science Specialist (CDSS)

Duration : 5 Days

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.


All participants should have basic understanding of data, relations, and basic knowledge ofmathematics.

Who Should Attend

This workshop is intended for individuals who are interested in learning data science, or who want to begin their career as a data scientist.

Learning Outcomes

Upon completion of this course, you will be able to:

  • Identify the appropriate model for different data types
  • Create your own data process and analysis workflow
  • Define and explain the key concepts and models relevant to data science.
  • Differentiate key data ETL process, from cleaning, processing to visualization.
  • Implement algorithms to extract information from dataset.
  • Apply best practices in data science, and become familiar with standard tools

Exam Format

The CDSS Certification Exam duration is 2 hours, consisting of 50 Multiple Choice Questions, with a Passing Score of 70%. You will receive a professional CDSS Certification upon passing the exam.

Course Outline

Introduction to Data Science
• What is Data?
• Types of Data
• What is Data Science?
• Knowledge Check
• Lab Activity

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

Data Science Workflow
• Data Gathering
• Data Preparation & Cleansing
• Data Analysis – Descriptive, Predictive, and Prescriptive
• Data Visualization and Model Deployment
• Knowledge Check

Life of a data scientist
• What is a Data Scientist?
• Data Scientist Roles
• What does a Data Scientist Look Like?
• T-Shaped Skillset
• Data Scientist Roadmap
• Data Scientist Education Framework
• Thinking like a Data Scientist
• Knowns and Unknowns
• Demand and Opportunity
• Labor Market
• Applications of Data Science
• Data Science Principles
• Data-Driven Organization
• Developing Data Products
• Knowledge Check

Data Science Prerequisites
• Probability and Statistics
• Linear Algebra
• Calculus
• Combinatorics

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

Data Preparation and Cleansing
• Extract, Transform and Load (ETL) – Pentaho, Talend, etc
• Data Cleansing with OpenRefine
• Aggregation, Filtering, Sorting, Joining
• Knowledge Check Lab Activity

Exploratory Data Analysis (Descriptive)
•What is EDA?
• Goals of EDA
• The role of graphics
• Handling outliers
• Dimension reduction

Data Quality
• Raw vs Tidy Data
• Key Features of Data Quality
• Maintenance of Data Quality
• Data Profiling
• Data Completeness and Consistency

Introduction to R
• Packages for data import, wrangling, and visualization
• Conditionals and Control Flow
• Loops and Functions
• Knowledge check
• Lab activity
• Lab: Exploring data using R Machine Learning (Predictive)
• Bayes Theorem
• Information Theory
• Statistical Algorithms
• Stochastic Algorithms

Supervised, Unsupervised, and Semi-supervised Learning
• What is prediction?
• Sampling, training set, testing set.
• Constructing a decision tree
• Knowledge check Lab Activity

Data Visualization
• Choosing the right visualization
• Plotting data using Python libraries
• Plotting data using R
• Using Jupyter Notebook to validate scripts
• Knowledge check
• Lab activity

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

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

What’s Next?
• Preview of Data Science Specialist
• Showing advanced data analysis techniques
• Demo: Interactive visualizations


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