Advanced Data Analysis with Python

Updated On: 22/07/2026

Created On: 06/02/2026

Course Overview

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.

Tools Covered

Some of the major industry-relevant tools you'll work with in this course include:

Google Colab
Jupyter Notebook
VS Code
PyCharm
NumPy
Pandas
Matplotlib
Seaborn
Plotly
SQL
DBeaver
Power BI
Tableau
Streamlit
GitHub Copilot
Codeium
Pandas Profiling
Graphy, Polymer
Flourish

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Frequently Asked Questions

Basic knowledge of Python and programming fundamentals is recommended. Familiarity with data analysis concepts can be helpful, but learners who want to advance their Python and data analysis skills can also join the training.

The training is best suited for learners with basic Python knowledge who want to develop more advanced data analysis skills.

You will learn advanced Python-based data analysis techniques, data cleaning and manipulation, data visualization, statistical analysis, and methods for extracting meaningful insights from datasets.

Yes. The training focuses on hands-on learning through practical exercises, data analysis tasks, visualization, and working with datasets.

The course helps you move beyond basic Python and develop practical skills in data cleaning, analysis, visualization, and interpretation, giving you a stronger foundation for working with real-world data.

The training can help you prepare for roles such as Data Analyst, Python Data Analyst, Business Data Analyst, Junior Data Scientist, and other data-related positions.

Dedicated labs, certified instructors, and placement support. You will receive guidance from trainers throughout the course, including support with practical exercises, coding tasks, datasets, projects, and technical concepts. You can also ask questions and get clarification during the training.

Yes. Depending on your requirements, you can discuss suitable schedules with the training team. One-to-one training and group-based learning options may also be available.

Our course is available in hybrid training mode. You can attend classes in person or join online, whichever works best for you. If you cannot attend in person on a particular day, you can simply join the class online instead.

The course pricing varies depending on the subject and level. For detailed information on the course price, please contact us directly: +977-9841002000 / +977-1-4111849. Our team will be happy to guide you or send us an email at [email protected].

Our syllabus outlines are only the headlines of the major modules. To ensure a complete understanding of the course, we offer free counseling. Also, if you have specific modules in mind, you can customize the course. Send your inquiry today!

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

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

Lesson 2: Foundations of Data Analysis

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

Lesson 3: Programming for Data Analysis

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

Lesson 4: Exploratory Data Analysis (EDA)

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

Lesson 5: SQL for Data Analysis

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

Lesson 6: Statistical Analysis for Data Analysts

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

Lesson 7: Data Cleaning, Transformation, and Feature Engineering

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

Lesson 8: Advanced Analytical Techniques

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

Lesson 9: Data Visualization and Dashboarding

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

Lesson 10: Business Analytics and Insight Communication

Metric design
Leading vs lagging indicators

Executive summaries
Insight-focused reporting

Narrative structure
Visual storytelling techniques

Graphy
Polymer
Flourish

Lesson 11: Real-World Applied Projects

End-to-end analytical project
Real business dataset analysis
Insight presentation
Documentation standards
Optional deployment (Streamlit dashboard)

Why Advanced Data Analysis with Python?

Advanced Data Skills: Learn Python-based techniques to work with, analyze, and interpret complex datasets.

Data Processing: Build skills in cleaning, transforming, and preparing data for accurate analysis.

Powerful Visualization: Create meaningful charts and visualizations to understand and present data clearly.

Practical Analysis: Work with real-world datasets to apply advanced data analysis techniques.

Industry-Relevant Skills: Develop Python and data analysis skills useful for modern data-driven roles.

Career Preparation: Build a strong foundation for opportunities in data analysis, business intelligence, and related fields.

Experienced Instructors: Learn from skilled instructors with practical experience in Python and data analysis.