Glomacs Institute

Course Introduction and Setup

• Learning Objectives
• What is Data Analysis and What is the Value to Business
• Basic domain knowledge and conceptual understanding e.g, what Data Analysis is, Business Intelligence, Data Mining, Machine Learning, Data Science, Deep Learning, and Artificial Intelligence
• Drivers of Emerging growing interest in Analytics in Organizations
• Types of Data Analytics
• From Data to Insights: Data Analytics Process Workflow
• Data Analytics Techniques and Concepts
• Analytics Use Cases and Applications (Horizontal and Vertical Industries)
• Stakeholders in an Analytics project and roles
• Planning an Analytics Projects – Key Steps, Tasks, Roles and Components
• Hard and Soft Skills for Successful Career in Data Analytics
• Key Takeaways

• Learning Objectives
• Big Data
• Structure, Semi-Structure, and Unstructured Data
• Platform for Data Storage
• Data Management
• Data Strategy
• Data Quality
• Data Governance
• Data Architecture
• Data Privacy
• Key Takeaways

• Learning Objectives
• What is Statistics and why is it important in Data Analysis
• Different Types of Statistics
• Different Types of Variables
• Statistical Measures (Central Tendency and Dispersion) and their significance
• Data Distributions
• Correlation and Regression
• Hypothesis Testing
• Type I and Type II error
• Forecasting and Time Series Analysis
• Key Takeaways

• Learning Objectives
• Why Excel for Data Analysis?
• Range Names and Tables
• Reading and manipulating data
• Data Cleaning, Sorting, and Filtering
• Basic excel data manipulation operations and functions, tips, and tricks.
• Spreadsheet functions to organize data such as IF, nested IF, VLOOKUP and HLOOKUP functions)
• Pivot Tables
• Data Visualizations and Charts
• Conditional Formatting
• Advanced graphing and charting tips
  • Histogram, Line, Scatterplots, Bars, Pie, etc
• Power Pivot and Power Query
• Time Series Analysis and Seasonal Adjustment in Excel
• Key Takeaways

• Learning Objectives
• What is SQL and RDBMS?
• SQL and modern languages such as HiveQL, SparkSQL
• RDBMS Concepts fundamentals
  • Tables and Data Types
  • Primary / Secondary Keys
• Basic SQL Commands to manipulate and retrieve data
  • SELECT Statement
  • WHERE clause
  • UPDATE Statement
  • JOINS (“left”, “right”, “outer”, “full”)
  • Groupings (GROUP BY)
  • Aggregation Functions (e.g. AVG, MAX, SUM, COUNT)
  • Subquery
  • Conditions with CASE (when/then/else/end) operator in SQL
  • Indexes
  • Wildcards

Creating Reusable Queries

• Learning Objectives
• Why Python for Data Analysis
• Introduction to Jupyter Notebook and Anaconda Environment
• Basics of Python Programming Concepts for a Data Analyst
  • The Python Interpreter
  • Modules
  • Functions
  • Data Structure:
    • Lists
    • Tuples
    • Dictionaries
    • Sets
  • Control Flow
  • Sorting
  • List Comprehensions
  • Iterators
  • Regular Expressions
  • Classes and OOP
  • Zip and Argument Unpacking
• Mathematical Operations on Array using NumPy
  • What is NumPy?
  • NumPy vs List
  • Array Creation
  • Basics Operations on Array
  • Universal Function (Ufuncs)
  • Aggregation Functions
  • Broadcasting
  • Fancy Indexing
• Data Manipulation with Pandas
  • Introduction and Motivation for Pandas
  • Pandas Data Structures
  • Essential Functionalities
  • Getting Data into and out of Pandas (OracleDB, SAS, SQLServer, Excel CSV, etc)
  • Pandas Object Creation (Series and DataFrame)
  • Viewing Data
  • Column and Row Selection (by label and position)
  • Exploratory Data Analysis
  • Dealing with Missing Data
  • Summarizing and Computing Descriptive Statistics
  • Data Wrangling
    • Merge/Join (Outer, Left, Right, Inner)
    • Object Concatenation
  • Aggregation Grouping
  • Reshaping and Pivoting
    • Stacking and unstacking
    • Pivot Tables
  • Working with Time Series
    • Dates and Times in Python
    • Pandas Time Series: indexing by Time
    • Pandas Time Series Data Structures
    • Frequencies and Offsets
    • Resampling, Shifting, and Windowing
  • Data Visualization with Matplotlib and Seaborn
    • Introduction to Matplotlib and Seaborn
    • Line Plots
    • Scatter Plots
    • Density and Contour plots
    • Customizing Plots
    • Visualization with Seaborn
  • Key Takeaways

• Learning Objectives
• The Art of Storytelling and Best Practices for Telling Great Data Stories
• Data Visualization Tools (Strength / Weaknesses of Each)
• Overview of Tableau / PowerBI Desktop Interface

• Connecting and Shaping Data in PowerBI

• Creating Data Models and Table Relationships

• Analyzing Data with DAX (Data Analysis Expressions)

• Creating Interactive Dashboards and Best Practices
• Key Takeaways