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

The following items describe the outline of the course:

Day 1:

Introduction to Data Science

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

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 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

Day 2:

Data Science Prerequisites

• Probability and Statistics
• Linear Algebra
• Calculus
• Combinatorics

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
• Python
• Knowledge check
• Lab Activity
• Lab: Exploring data using Python

Day 3:

Data Preparation and Cleansing

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

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

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

Day 4:

Machine Learning (Predictive)

• Bayes Theorem
• Information Theory
• NLP
• Statistical Algorithms
• Stochastic Algorithms

Introduction to 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, Unsupervised, and Semi-supervised Learning

• What is prediction?
• Sampling, training set, testing set
• Constructing a decision tree
• Knowledge check Lab Activity

Day 5:

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

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 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