AI INTEGRATED COURSE

Advanced Data Analysis with Python

Course Overview
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Broadway Infosys offers an Advanced Data Analysis with Python course that can upgrade your career and is aimed at helping students and working professionals develop their analytical skills through using Python, SQL, statistics, and business intelligence tools. The participants will demonstrate their ability to analyze complex datasets and develop data-driven solutions by creating data visualizations and extracting business insights through their work with structured workflows that meet industry standards.

Participants begin by learning basic data analysis skills, mastering analytical reasoning, problem definition, KPI formulation, and the comprehensive analysis workflow. The curriculum teaches fundamental Python programming skills together with NumPy and Pandas, which students use for data cleansing and transformation, aggregation, and time-series analysis, while they learn Exploratory Data Analysis (EDA) to discover patterns and trends.

The course enables students to use SQL databases, perform statistical analysis for business decision-making, and execute advanced analysis methods, including segmentation and cohort analysis, A/B testing, and anomaly detection. Participants in the course develop their data visualization skills by creating interactive dashboards with Power BI and Tableau while learning to present insights that drive impactful business results.

The training concludes with real-world projects that require participants to demonstrate their skills through actual work challenges, enabling them to develop the skills employers need for their jobs.

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Lesson 1: Python Programming Language

  • What exactly is Python?
  • Python's root and its ecosystem
  • Python Installation & IDEs setting up (Google Colab, Jupyter Notebook, VSCode, PyCharm)
  • Python framework & Python syntax
  • Hands-on writing code on Google Colab

  • Data Types & Variables (String, Integer, Float, Complex, Boolean, None)
  • Input and Output Functions
  • Working with the format() method, f-strings, & escape sequences
  • Basic Arithmetic & Operators
  • Type casting, type checking, & validation

  • Conditional Statements (if, else, elif)
  • Loops (for, while)
  • Looping over tuples, strings, & dictionaries
  • Special loops in Python (for/else)
  • Using nested loops and flow control through conditions
  • Resolving real-world problems to improve skills
  • Special Statements: pass, continue, break

AI Tool:

  • Google Colab - Gemini

Lists:

  • Overview & fundamental operations
  • Indexing, slicing, & negative indexing
  • Looping through lists & conditions
  • List methods like .insert(), .append(), .remove(), .sort(), etc.
  • List comprehension with conditions

Tuples:

  • Introduction & operations
  • Indexing, slicing, & looping
  • List versus Tuple
  • Switching between lists and tuples
  • Tuple unpacking

Sets:

  • Introduction & set operations
  • Adding, removing, & discarding items
  • Set operations: union, intersection, and difference
  • Frozenset versus set

Dictionaries:

  • Introduction to dictionaries & methods like .get(), .update(), .keys(), .pop(), etc.
  • Dictionary comprehension
  • Nested dictionaries

AI Tool:

  • Gemini or Codeium

  • Defining functions through def keyword
  • Parameters, Arguments, & Return Statements
  • Returning multiple values
  • Default & keyword arguments
  • Anonymous functions (lambda)
  • Nested functions & closures
  • Scopes in Python: Local and Global

Text File Operations:

  • Reading & writing text files
  • Modes of file (r, w, a, rb, wb)
  • File path handling with the os module

Working with CSV Files:

  • Basics of CSV format and operations
  • Reading & writing CSV files with csv.reader & csv.writer
  • Using dictionaries in CSV files

Working with JSON:

  • Introduction to JSON & its structure
  • Reading & writing JSON data with the json module
  • Parsing JSON strings

AI Tool:

  • Using ChatGPT for prompt engineering

  • Classes & Objects
  • Class versus Object attributes
  • Initializing object attributes with __init__()
  • self keyword
  • Inheritance: single, multiple, and multi-level
  • Polymorphism & operator overloading
  • Function overriding & encapsulation

AI Tools:

  • Pythontutor.com

  • Try-except blocks
  • Catching specific exceptions
  • Using else & finally
  • Generating and creating custom exceptions
  • Problem-solving strategies

  • Lambda Functions
  • Generators & Iterators
  • List Comprehensions
  • Working with *args & **kwargs

Standard Libraries: os, random, math, functools, etc.

Data Manipulation with Pandas

  • Working with DataFrames
  • Reading & writing CSV files
  • Data manipulation techniques

Data Visualization

  • Using Matplotlib, Seaborn, and Plotly

AI Tools

  • Pandas Profiling

  • Designing and changing databases and tables
  • CRUD operations (CREATE, SELECT, UPDATE, DELETE)
  • Filtering data with the WHERE clause

AI Tools for SQL

  • DBeaver for SQL queries
  • Optimizing & explaining SQL queries with ChatGPT

  • Installing & configuring Git
  • Setting up local & remote repositories
  • Making commits & branching
  • Integrating local repositories to GitHub
  • Pushing changes & cloning repositories

AI Tools

  • GitHub Copilot for Git commands

  1. Web Scraping + Database + File Operations: Scrape data, store it in SQL, & export to CSV/JSON
  2. Desktop Application (Data Entry System): Develop an application to manage data in JSON/CSV format
  3. CLI Application with CRUD Operations: Design a CLI app with basic CRUD operations & database integration

  • Understanding Data Analysis in Industry
    • Responsibilities of Data Analysts
    • Data Analysis vs Data Science vs Business Intelligence
    • Data-driven decision-making
  • Analytical Thinking
    • Critical thinking approaches
    • Problem-solving frameworks

  • Descriptive Analytics
  • Diagnostic Analytics
  • Predictive Analytics (Analytical Perspective)
  • Prescriptive Analytics

  • Business problem understanding
  • Data acquisition
  • Data cleaning and preparation
  • Exploratory analysis
  • Statistical analysis
  • Visualization and reporting
  • Communicating insights

  • Translating business questions into analytical problems
  • Hypothesis formulation
  • KPI and metric definition

  • Array Operations
    • Array creation
    • Indexing and slicing
    • Broadcasting
  • Numerical Operations
    • Vectorized computations
    • Statistical calculations
    • Performance optimization

  • DataFrame Fundamentals
    • Series vs DataFrame
    • Indexing (.loc, .iloc)
    • Data types and memory usage
  • Data Input and Output
    • Reading CSV, Excel, JSON
    • SQL connections
  • Data Cleaning
    • Missing value handling
    • Removing duplicates
    • Data type conversion
    • String operations
  • Data Transformation
    • Filtering and sorting
    • GroupBy and aggregation
    • Pivot tables
    • Applying custom functions
  • Time Series Basics
    • Datetime conversion
    • Resampling
    • Rolling statistics

  • Data profiling
  • Summary statistics
  • Detecting anomalies

  • Distribution analysis
  • Skewness and kurtosis
  • Outlier detection

  • Correlation analysis
  • Cross-tabulation
  • Feature relationships

  • Generating analytical questions
  • Identifying patterns and trends

  • Relational databases
  • Schema design
  • Primary and foreign keys

  • SELECT statements
  • Filtering (WHERE, BETWEEN, IN, LIKE)
  • Sorting and limiting

  • COUNT, SUM, AVG, MIN, MAX
  • GROUP BY and HAVING

  • Joins (Inner, Left, Right, Full)
  • Self joins
  • Subqueries
  • Common Table Expressions (CTEs)

  • Window functions
  • Ranking and cumulative calculations
  • Time-based analysis

  • Indexing concepts
  • Performance best practices

  • Central tendency
  • Variability measures
  • Distribution properties

  • Conditional probability
  • Independence
  • Bayes theorem

  • Random sampling
  • Bias and variance
  • Sample size considerations

  • Confidence intervals
  • Hypothesis testing
  • p-value interpretation
  • Statistical Tests
    • t-test
    • z-test
    • Chi-square test
    • ANOVA

  • Interpretation challenges
  • Practical implications

  • Imputation strategies
  • Data exclusion decisions

  • IQR method
  • Z-score method
  • Business context evaluation

  • One-hot encoding
  • Label encoding
  • Feature scaling

  • Calculated metrics
  • Ratio features
  • Time-based features

  • Customer segmentation
  • RFM analysis
  • Behavioral grouping

  • Retention analysis
  • Lifecycle tracking

  • Trend detection
  • Seasonality analysis
  • Moving averages

  • Experiment design basics
  • Statistical evaluation
  • Business interpretation

  • Statistical threshold methods
  • Monitoring use cases

  • Chart selection
  • Visual clarity
  • Avoiding misleading visuals

  • Matplotlib fundamentals
  • Seaborn statistical plots
  • Plotly interactive visualization

  • Data modeling
  • Power Query transformations
  • Measures and calculated columns
  • DAX basics
  • Dashboard design
  • Time intelligence
  • Geo visualization

  • Metric design
  • Leading vs lagging indicators

  • Executive summaries
  • Insight-focused reporting

  • Narrative structure
  • Visual storytelling techniques

  • Graphy
  • Polymer
  • Flourish

  • End-to-end analytical project
  • Real business dataset analysis
  • Insight presentation
  • Documentation standards
  • Optional deployment (Streamlit dashboard)
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Upcoming Classes (8)
01 Mar 2026
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23 Mar 2026