Next Live Class Date - March 04 2023

Course Overview

Glomacs AI/DS/Analytics Track 1 is targeted at those interested in career as a Data Analyst / Business Intelligence Analyst or anyone who is interested in understanding how to gain insight from data for decision making. It comprises of course modules that provide you with comprehensive, in-depth overview of the fundamental concepts, techniques, and tools in Data Analysis and Business Intelligence. It also provides practical hands-on, use-cases of the concepts in solving real life business problems using some modern tools and technologies in the field. Among the tools are Python (and/or R), SQL/HiveQL, Tableau/PowerBI, Statistical Analysis with Microsoft Excel.

Learning Outcomes

Gain Domain Knowledge

Understand key concepts, techniques, tools and terminologies in Data Analysis, Business Intelligence, and other relevant areas such as Data Mining, Data Science, Machine Learning, and Artificial Intelligence

Develop Analytical Thinking and Storytelling Skills

Gain an understanding of how to answer business questions using analytical techniques and process. You will also learn how to communicate your findings to executives using the right storytelling techniques and tools.

Gain Practical, Hands-On Project Experience

Develop hands-on experience using some modern tools for analytics and insights and applying them to problems in various domains such as banking, retail, healthcare, telecom, and travel.

You get EVERYTHING you need to become an expert!

Resume and Coverletter Template

An industry proven template to help you with your foot in the door

One-on-One direct mentorship with Instructor

Don’t you love to have direct access with your instructor, guidance and mentor-ship when you need it

Projects to build your portfolio

Access to datasets, case studies to build your portfolio, and capstone projects to help you stay rooted.

Payment Options (Track 1)

Early-Bird Discounted Full Payment


Early-Bird Discounted Two Installments Payment


Early-Bird Discounted Three Installments Payment


Course Curriculum

Module 1: Introduction to the Course

Course Introduction and Setup

Module 2: Introduction to Data Analytics

  • 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

Module 3: Fundamental Statistics for Data Analysis

  • 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

Module 4: Introducing Data Analysis in Excel

  • Learning Objectives
  • Introduction to Excel Spreadsheet
  • Reading and manipulating data;
  • Basic excel data manipulation operations and functions, tips and tricks.
  • Spreadsheet functions to organize data such as IF, nested IF, VLOOKUP and HLOOKUP functions)
  • Data Filtering, Pivot Tables, and Charts
  • Advanced graphing and charting tips
    • Histogram, Line, etc
  • Key Takeaways

Module 5: Fundamentals of SQL for Data Analysis

  • 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

Module 6: Python for Data Analysis

  • 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

Module 7: Data Storytelling and Visualization

  • 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


Some Frequently Asked Question

When is the Data Science Track 1 starting

The next batch of Data Science Track 1 would start on SATURDAY MARCH 04, 2023

What is the deadline for registration for next batch

Registration will open online for the training on Monday, January 23 and it closes by Wednesday, March 1st (3 days to the training start date). You can NOW register online at

Where and when do the classes take place

The class is an interactive, online, instructor-led training for 12 weeks for those in Track 1. Classes take place on Saturdays

How much is the registration cost for the course?

The normal price for track 1 of the course is CAD $2,000. We are giving everyone that registers for batch 1 of the class a 10% discount off normal course price which make the payment for batch 1 $1800. However, we further give an early bird discounted price of $1600 for those who register for the class before February 20 2023.

Will I get a job as a Data Analyst after taking this class

This course would give you all the required knowledge and skills required for a Data Analyst or BI role. We also support you with resume and interview preparation and practical projects to make you ready for industry

Where do I get further information about the class

For further enquiries about the class, send an email to:


Take Glomacs Data Science Track 1 to get yourself for the high-demand roles of in field of Data Science and Analytics