AI Data Analyst Course

This presentation provides an overview of the syllabus for our upcoming AI Data Analyst course, detailing the modules and capstone projects.

Course Content

Module 1: Introduction to Data Analysis

  • What is Data Analysis
  • Why Data Analysis
  • Types of Data
  • Types of Data Analysis
  • Steps in Data Analysis
  • Data Analysis Tools and Technologies
  • Data Analysis vs Statistics

Numerical Data Analysis using Python NumPy

  • Introduction to NumPy
  • Creating NumPy arrays
  • NumPy Array Properties
  • 1d, 2d and 3d arrays
  • Accessing array elements
  • Indexing, Slicing, Reshaping
  • NumPy Array Operations
  • NumPy Array Manipulation
  • Broadcasting

Data Analysis using Python Pandas

  • Introduction to Data Analysis using Pandas
  • Series & DataFrames
  • DataFrame Row & Column Operations
  • Indexing, Slicing & Extracting data
  • Importing data (CSV, Excel) in Pandas
  • Data Wrangling in Pandas
  • Functions in Pandas
  • Data Analysis in Pandas
  • Filtering, Grouping and Joining
  • Data Visualization using Matplotlib

Module 3: Data Analysis using SQL

  • SQL Commands for Data Analysis (DDL, DML)
  • Aggregate Functions & Grouping data
  • SQL Joins & Set Operators
  • Advanced Data Analysis: Subqueries
  • Data Cleaning & Transformation
  • Window & Logical functions
  • String & Date Functions
  • Analytical Functions in SQL
  • Performance Tuning & Query Optimization
  • Reporting & Data Visualization

Module 4: Statistics & EDA

  • Descriptive & Inferential Statistics
  • Data & Variables in Statistics
  • Measures of Central Tendency (Mean, Median & Mode)
  • Variance & Standard Deviation
  • Univariate Analysis (Histograms, Box plots & Bar charts)
  • Bivariate/Multivariate Analysis (Line plots, Scatter plots, Heat maps)
  • Probability Distribution & Central Limit Theorem
  • Correlation & Hypothesis Testing
  • EDA using Python

Module 5: Machine Learning with Python

  • Introduction to Machine Learning
  • Types of Machine Learning
  • Machine Workflow
  • Steps to build an ML Model
  • Supervised ML vs Unsupervised ML
  • Supervised Learning: Regression & Classification
  • Unsupervised Learning: Clustering
  • Pre-processing data for ML
  • Linear Regression
  • Logistic Regression
  • Model Validation & Tuning

Module 6: Data Analysis using Power BI

  • Introduction to Datawarehouse
  • OLTP vs OLAP
  • Star Schema vs Snowflake Schema
  • Overview of Power BI
  • Power BI Desktop
  • Connecting Power BI with different data sources
  • Power Query for Data Transformation
  • Data Modeling
  • DAX Functions
  • Reports & Visualization types
  • Publishing Reports
  • Power BI Service

Module 7: Azure for the Data Analyst (Microsoft Fabric)

  • Introduction to Azure
  • ETL vs ELT
  • Azure Data Fundamentals
  • Azure Data Lake Storage (ADLS)
  • Azure SQL database
  • Azure Data Factory (ADF)
  • Building ETL Pipelines with ADF
  • Data Movement & Transformation with ADF
  • Fabric Analytics

Module 8: AI and Generative AI

  • Introduction to Artificial Intelligence: History of AI
  • Types of AI
  • Areas and related disciplines of AI
  • Understanding AI Subdomains
  • Applications of AI
  • Tasks AI can solve
  • Introduction to Generative AI
  • Evolution of Gen AI
  • Gen AI vs Traditional ML
  • How Gen AI works
  • Applications of Gen AI
  • Popular Gen AI Tools
  • Introduction to ChatGPT
  • Large Language models (LLM)
  • Prompt Engineering
  • Hugging Face & LangChain

Capstone Projects

  • SQL Data Analysis Project 1: Fortune Auto Wheels Data Analysis using SQL (Tools: Data extraction and aggregation, SQL Functions, Joins and Subqueries)
  • SQL Data Analysis Project 2: Social Media Analytics using SQL - Analyzing Social media trends, posts and tweets (Tools: SQL, Joins, Aggregations)
  • Python Data Analysis Project 1: Online Food Store Order Analysis - Data Analytics in the Food and Beverage Sector (Tools: Python Pandas, Matplotlib/Seaborn, Statistics and EDA)
  • Python Data Analysis Project 2: EA Games Data Analysis using Python Pandas - Maximizing Online Game Revenue (Tools: Python Pandas, EDA, Matplotlib)
  • Python Data Analysis Project 3: Marketing Campaign Analysis with Python Pandas (Tools: Python, Pandas, EDA)
  • Python Data Analysis Project 4: Movie Recommendations using Python Pandas (Tools: Python, Pandas)
  • Power BI Data Analysis Project 1: Sales Insights Data Analysis - Analyzing Trends and patterns in Sales data (Tools: Power BI, Data Cleaning, Data Modeling)
  • Power BI Data Analysis Project 2: Financial Data Analysis & Reporting - Enhancing Business Decisions (Tools: Power BI, Data analysis, DAX)
  • Power BI Data Analysis Project 3: Supply Chain Analytics to Boost Profitability (Tools: Power BI, Data Modeling, DAX)
  • Power BI Data Analysis Project 4: Customer Churn Analysis (Tools: Power BI, DAX)
  • Microsoft Fabric Data Analysis Project: Cloud Data Warehouse Analysis - Load and Query a DWH in Microsoft Fabric using data pipelines (Tools: Microsoft Fabric Analytics)

Bonuses & Post-Course Services

  • Data Analyst Interview Questions and Answers
  • Resume/CV Preparation
  • LinkedIn Profile Optimization
  • Interview preparation guidance
  • Mock Interviews
  • Placement Assistance/Support
  • Job Referrals
  • Remote Jobs & Freelance Jobs