Intro to Data Analytics Syllabus

Course Description

"Intro to Data Analytics with ChatGPT AI" is a comprehensive 12-week, part-time program designed to equip you with the knowledge and skills necessary to become a proficient junior data analyst. Throughout the course, you'll gain expertise in utilizing Excel, Power BI, SQL, Python, and ChatGPT AI for data analysis, ensuring data quality, and crafting engaging data visualizations. The curriculum also emphasizes professional development and job readiness, incorporating resume development and effective interview strategies. Upon completing the course, you'll possess a strong understanding of the data analytics process, along with the ability to address real-world data challenges using ChatGPT AI.

Course Goals

The "Intro to Data Analytics" course equips learners with data analysis expertise using prominent tools such as Excel, Power BI, SQL, Python, and ChatGPT AI. The curriculum encompasses data visualization, professional development strategies, SQL and Python programming for data analysis, utilizing ChatGPT AI for enhancing analytics, and practical guidance on crafting compelling resumes and succeeding in job interviews. Upon completion, participants will be prepared to tackle real-world data challenges, either by pursuing a data analytics career or by enhancing their current roles with data skills.

( 18 Hours )
Intro to Analytics and Excel
  • Focus on Excel as a crucial tool for data analysis
  • Learn the importance and practical uses of Excel
  • Explore workbook creation
  • Understand common Excel functions
  • Study conditional aggregation
  • Discover pivot tables and charts
  • Examine slicers and time slicers
  • Build a strong foundation for the remainder of the course
( 18 Hours )
Learn Microsoft Excel
  • Learn to apply conditional formatting in Excel
  • Discover how to import data effectively
  • Understand the process of adding to data models
  • Master pivoting using multiple tables
  • Gain expertise in Excel's PowerQuery and PowerPivot
  • Dive into Data Analysis Expressions (DAX)
  • Explore creating measures and KPIs
  • Perform what-if analysis in Excel
  • Get acquainted with the data analysis tool package
  • Participate in cohort quizzes, assignments, and exercises to reinforce learning
( 18 Hours )
SQL for Data Analysis (Part 1)
  • Understand the anatomy of an SQL query
  • Learn about Common Table Expressions in SQL
  • Explore generating data and creating a Date Dimension
  • Dive into random data generation and sampling techniques
  • Grasp train/test split implementation with SQL
  • Test your knowledge with a Basic SQL for Data Analysis quiz
  • Delve into describing a series in descriptive statistics
  • Discover how to describe a categorical series
  • Assess your understanding with a Descriptive Statistics quiz
( 18 Hours )
SQL for Data Analysis (Part 2)
  • Master the concept of grouping in SQL
  • Learn about conditional aggregates and subtotals
  • Evaluate your understanding with a Grouping and Subtotals quiz
  • Discover aggregate expressions in SQL
  • Understand window frames and their application
  • Explore accessing next and previous rows in SQL
  • Dive into SQL ranking functions
  • Test your knowledge with a Running and Cumulative Aggregation quiz
( 18 Hours )
SQL for Data Analysis (Part 3)
  • Learn to compare missing values in Interpolation
  • Explore back filling and forward filling techniques
  • Understand linear interpolation
  • Test your knowledge with an Interpolation quiz
  • Discover the concept of binning in data analysis
  • Learn about equal-height binning
  • Dive into equal-width binning
  • Assess your understanding with a Binning quiz
( 18 Hours )
Learn Power BI (Part 1)
  • Discover what PowerBI is and its purpose
  • Learn data visualization best practices
  • Understand the high-level overview and components of PowerBI
  • Gain experience in importing data and creating visuals
( 18 Hours )
Learn Power BI (Part 2)
  • Master data transformation techniques in PowerBI
  • Learn how to create relationships between data sets
  • Engage with cohort quizzes, assignments, and exercises to reinforce learning
( 18 Hours )
Learn Power BI (Part 3)
  • Learn how to publish reports in PowerBI
  • Discover how to create engaging dashboards
  • Dive into real-time visuals for dynamic data representation
  • Explore custom visuals for tailored visualizations
  • Understand security and sharing practices in PowerBI
  • Maximize usage and efficiency of PowerBI tools
  • Enhance learning through cohort quizzes, assignments, and exercises
( 18 Hours )
Python for Analysis (Part 1)
  • Get started with Python for analytics
  • Understand essential data structures in Python
  • Learn about control flow and built-in functions
  • Explore Numpy, an external library for numerical computing
  • Discover Scipy, an external library for scientific computing
  • Practice using Numpy and Scipy through exercises
  • Review solutions for Numpy and Scipy exercises
  • Work with comma-separated files (CSV)
  • Learn how to handle JSON files
  • Manage raw files in Python
  • Exercise: Read and analyze the Auto MPG dataset
  • Review solutions for reading the Auto MPG dataset
( 18 Hours )
Python for Analysis (Part 2)

Describing Data:

  • Learn about statistics and counts in data analysis
  • Understand reshaping the data
  • Practice group-by aggregations through exercises
  • Review solutions for group-by aggregations exercises

Cleaning Data:

  • Explore how to handle missing data
  • Learn to identify and manage outliers
  • Understand the importance of scaling data
  • Dive into working with categorical data
  • Exercise: Clean the Auto MPG dataset
  • Review solutions for cleaning the Auto MPG dataset

( 18 Hours )
Python for Analysis (Part 3)

Visualizing Data:

  • Get introduced to data visualization techniques
  • Learn to create scatter plots for data representation
  • Understand and create bar plots for data visualization
  • Explore visualizing data distributions
  • Learn to represent data using line graphs
  • Discover the use of heat maps in data visualization
  • Master the art of multi-plot grids for data representation
  • Exercise: Visualize the Auto MPG dataset
  • Review solutions for visualizing the Auto MPG dataset
  • Test your understanding with self-assessment questions
( 18 Hours )
Final Project Review & Career Prep

Final Project Options:

  • Create a Sales Analysis Dashboard using Excel or Power BI
  • Perform Social Media Analytics with Power BI

Career Preparation:

  • Get assistance with resume review and creation
  • Participate in a mockup interview to prepare for real interviews
  • Learn to create an effective LinkedIn profile for job search