Data Analytics using Python Certification Course with AI & ChatGPT [English]
You’re going to master performing exploratory data analysis, data visualisation, Tables and charts in Excel, Power BI and data interpretation and (as a bonus) you’re going to gain all those real-working skills that make you extremely attractive to every industry! By the end of this course, you’ll have the tools to transform raw data into actionable insights. So, are you all set to start your career in data? Let’s go!
Course Highlights – Everything You Need to Succeed
- 60+ hrs live expert-led course
- 90+ hrs self-paced expert-led course
- 240+ hrs of comprehensive study material
- 145+ hrs of real-world practicals
- 70+ Interactive quizzes & assessments
- 590+ Interview questions for top MNCs
- 135+ Real-time projects with implementation
- 290+ Practical Examples
- 97% Positive reviews from learners
- 45+ Comprehensive assignments
- 30+ Real-time industry case-studies
- 280+ Data analytics tutorials
- 1:1 Career counselling with expert
- Practical knowledge which industry needs
- Industry-renowned certification
Your Data Analytics with Python Journey Starts Here — Try before you Buy
Master Data Analytics from Scratch
Join our hands-on Data Analytics course crafted by industry veterans and build real-world skills. It’s not just a course, it’s a job-ready bootcamp.Start 📅 30-Aug-2025 |
Schedule 🕗 8.00 PM IST | 10.30 AM EDT (Sat-Sun) |
Access Duration 🕗 Lifetime Access |
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| 📅 27-Sept-2025 | 🕗 5.30 PM IST | 08.00 AM EDT (Sat-Sun) | 🕗 Lifetime Access |
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Self-Paced Mastery
Ideal for: Beginners looking to learn and grow with expert guidance.
₹ 19990 | $260 | €230₹ 8990 | $117 | €103
💳 EMI Starts at ₹158 per month
| Self-paced Expert-led Course |
| Industry-relevant Curriculum by Experts |
| Beginner to Advanced Topics Covered |
| Notes, Study material & Cheatsheets |
| Real-world Practicals |
| Assessments to Test Your Skills |
| Interview Questions of Top MNCs |
| Real-time Live Projects |
| 1:1 Career Counselling with Expert |
| Real-time Industry Case-studies |
| 3 years Access Duration |
| Industry-renowned Certification |
Career Launchpad
Ideal for: Dedicated & ambitious learners looking for jobs in top MNCs.
₹ 29990 | $390 | €345₹ 12990 | $169 | €149
💳 EMI Starts at ₹281 per month
| Everything in “Self-Paced Mastery” plus: |
| Job Assistance |
| Resume & Interview Prep |
| Mock Interview |
| Internship |
| Job/Placement Prep |
| LOR |
| Additional Real-time Projects |
| Lifetime Course Access |
| LinkedIn Profile Optimization |
| Learn with ChatGPT & AI tools |
| Quizzes for Each Module to Track Progress |
Pro Mentorship
Ideal for: Industry professionals looking for a job or switching careers.
₹ 39990 | $520 | €460₹ 15990 | $208 | €184
💳 EMI Starts at ₹457 per month
| Everything in “Career Launchpad” plus: |
| Live interaction with Instructor for 3 months |
| Live Mentoring over Weekends |
| Personal mentorship and 1:1 guidance from experts |
| 100% Placement Assistance |
| Lifetime Support |
| Lifetime Upgrades to latest version |
| Additional Real-time Projects |
| 24x7xLifetime Access |
| Dedicated Hiring Manager |
| Interview Questions of MAANG Companies |
| After Job Support |
Easy EMI Options Available: Invest in Your Future Without Worry!
- 💳 Flexible Payment Options – Get started with easy monthly installments.
- 💵 Affordable Learning – Pay as low as ₹386/month.
- 📢 No Hidden Charges – Simple, transparent, and secure.
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Success Stories – They Believed, Learned & Achieved!
Success Isn’t Luck – It’s Learning. They Chose to Upskill – Now They’re in the World’s Best Companies!
“I dream for Big Data and DataFlair TechVidvan convert it into reality. A big thanks to the team.”
“Online training” the most suitable option to learn big data along with your current job. After my practice in the training, I was confident enough to face any type of interview…Read more
Shreyas Kudale 
Data Engineer Team lead, Hashmap
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While searching for a full-fledged Big Data course, I surveyed for many institutions and finally landed up at DataFlair. The instructor is extremely helpful in taking students to the expert level. He kept motivating me to learn more and more…Read more
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Data Science Engineer, Cisco
“How TechVidvan Paved My Path to Nulinz Technology.”
TechVidvan was far beyond my expectations. I have a very structured way of learning. The course curriculum was complete in itself and they focussed more on practicals rather than teaching just theory. The course covered multiple projects…Read more
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Application Developer at Nulinz Technology
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Before joining DataFlair, I had inquired at many other Big Data Training institutes, but didn’t get the satisfaction. Somehow, I found reviews from previous students…Read more
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Senior AI Engineer, Prescient Healthcare Group
“Cracking the Pre-Placement Offer: My Machine Learning Journey with DataFlair TechVidvan.”
By the time I reached my fourth year, I decided to upskill in Machine Learning, but the real challenge was finding the right approach. I didn’t just want theoretical knowledge; I wanted to get hands on…Read more
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Got placed in Course5Intelligence
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Director at Lead Squared
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Online Data Analytics with Python Training Course Curriculum
💻 Python
💻 Python for Data Analytics
🔵 Data Analytics
🔵 Power BI
💻 Excel
💻 SQL
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1. Introduction to Python
- What is Python
- Why should you learn it
- Python Applications
- Platform-dependent vs. platform-independent
- Features of Python
- Limitations of Python
- History of Python
2. Writing Your First Python Program
- Installing Python and setting up your environment
- Setting up PyCharm
- Python code execution
- First “Hello World” program
3. Python Data Types and Variables
- Python statements, indentation, and comments
- Using print() and taking user input
- Python Methods, Variables, and Identifiers
- Python data types
- Rules for declaring identifiers
4. Input and Output in Python
- input and output functions in Python
- Read data in a single line
- print() function
- format() method
- Replacement operators in Python
5. Python Operators
- What are Python operators
- Bitwise operators
- Arithmetic operators
- Assignment operators
- Number system conversions
- Identity operators
6. Control Statements in Python
- Conditional statements: if, else, elif
- Palindrome and Armstrong number
- Pyramid patterns using control statements
- Transfer statements: break, continue, and pass
7. Loops in Python
- Loops and their types
- While loop
- for loops
- Nested for loops
8. Strings in Python
- Strings in Python
- Built-in string functions and methods
- String type casting
9. Collections in Python
- Collections in Python
- Lists
- Tuples
- Sets
- Frozen Sets
- Dictionaries
- Byte arrays
10. Functions and Recursion in Python
- Functions in Python
- Function arguments, call by value, and returning values
- Passing functions as arguments
- Global variables
- eval() method
- Iterator
- Generator
- Clouser
- Decorator
- Iterable vs Iterator vs Generator
- Recursion through programs like factorial and Fibonacci series
- Recursion in Python and how to reverse a number
11. Lambda Functions in Python
- Introduction to lambda functions
- Lambda filter function
- Lambda map function
- Lambda reduce function
12. Arrays in Python
- Arrays and their operations
- NumPy arrays
- NumPy Mathematical Functions
- Multidimensional arrays
- Matrix operations
- Difference between views and copies in NumPy
- Object Reference in Python
13. Searching and Sorting in Python
- Binary search
- Bubble sort algorithms
14. Object-Oriented Programming in Python
- Difference between procedural and object-oriented programming
- Classes and objects
- Self variables
- Constructor Overloading
- Polymorphism
- Operator overloading
- Getters and Setters in Python
- Inner Class in Python
- Inheritance
- Constructor overloading
- Abstract Class in Python
15. Exception Handling in Python
- Exception handling in Python
- Types of Exceptions
- Using try, except, finally blocks
- User-defined exceptions
16. File Handling in Python
- File modes and operations
- CSV files
- Pickling in Python
- tell() and seek() methods
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1. Introduction to NumPy
- What is NumPy?
- Installing NumPy
- How to install NumPy in PyCharm
2. NumPy Array Creation and Attributes
- Creating NumPy arrays
- Basics of NumPy arrays
- ndarray attributes
- Real-world applications of NumPy array attributes
3. Essential NumPy Functions
- arange(), linspace(), and logspace() functions
- Initializing arrays using zeros(), ones(), full(), and eye() methods
- Comparing arrays with functions like any(), all(), and where()
- Performing arithmetic operations on arrays
- Statistical functions in NumPy
4. NumPy Concatenation and Splitting
- Differences between Reference, View, and Copy in NumPy
- Array concatenation
- Joining arrays with methods like stack(), vstack(), hstack(), and depth()
- Splitting arrays into smaller parts using split(), array_split(), vsplit(), and hsplit()
5. Introduction to Pandas and Setup
- Introduction to Pandas
- Installing Pandas on Windows
- Setting up Pandas in PyCharm
- How to download and prepare datasets for analysis
6. Pandas Series
- Understanding Series in Pandas
- Key properties of Series and their applications
- Mathematical Operations on Pandas Series
7. Pandas DataFrames
- Introduction to DataFrames
- Creating DataFrames from Excel, CSV, and Clipboard inputs.
- Exploring multiple ways to create DataFrames in Pandas.
- Exporting DataFrames to CSV and Excel formats.
- DataFrame attributes
- DataFrame slicing
- Sorting DataFrames
- Removing duplicate values in DataFrames
- Handling missing data using fillna() and dropna() methods.
- loc and iloc methods
8. Advanced Data Analysis
- Introduction to advanced data analysis with Pandas
- Joining DataFrames: Inner, outer, and custom joins
- Joins in Pandas without common columns
- Concatenating DataFrames for efficient data merging
- Utilizing the where() function in Pandas
- Grouping data with the groupby() method
- Aggregate functions
9. SQL Integration in Pandas
- Writing SQL-equivalent queries using Pandas
- Performing SQL-like operations (SELECT, WHERE) on DataFrames
- Using isin() and not isin() for advanced filtering
- Extracting top records with the nlargest() method
- Inserting, updating, and deleting data in Pandas DataFrames
10. Introduction to Matplotlib
- What is Matplotlib for data visualization
- Step-by-step installation guide for Matplotlib in PyCharm
- Installing the Matplotlib library in Python
- Basics of chart design
11. Working with Matplotlib
- Introduction to Matplotlib markers
- Drawing lines in Matplotlib
- Modifying chart lines for better presentation
- Customizing chart titles and axis labels with fonts and colors
- Adjusting title and axis fonts
- Understanding the Legend function
- Grid lines
12. Creating Beautiful Graphs
- Subplots in Matplotlib
- Plotting subplots
- Using xticks(), yticks(), xlabel(), ylabel(), xlim(), and ylim() for axis customization.
- Scatter plots in Matplotlib
- Adding color maps and color bars to scatter plots
- Creating vertical and horizontal bar graphs
- Plotting multiple bars in a single graph for grouped data visualization.
- Designing pie charts
- Matplotlib histograms
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1. Data Analytics
- Data Analytics Overview
- Importance of Data Analytics
- Types of Data Analytics
- Benefits of Data Analytics
- Data Visualization for Decision Making
2. Types of Data Analytics
- Descriptive Analytics
- Diagnostic Analytics
- Predictive Analytics
- Prescriptive Analytics
3. Data Acquisition & Cleaning
- Strategies for imputing or removing missing values
- Techniques for identifying and treating outliers
- Understanding different data types
- Managing various file formats
4. Data Transformation & Integration
- Applying normalization procedures
- Scaling features for consistency
- Combining and harmonizing multiple data sources
5. Dimensionality Reduction
- Leveraging Principal Component Analysis (PCA)
- Utilizing Singular Value Decomposition (SVD)
- Selecting the most relevant features
6. Exploratory Statistics & Visualization:
- Computing measures of central tendency and spread
- Creating frequency distribution tables
- Drawing box plots
- Plotting histograms
- Generating scatter plots
7. Advanced Regression & Association Methods:
- Implementing Ridge and Lasso regression
- Exploring Elastic Net regularization
- Fitting polynomial regression models
- Mining association rules with the Apriori algorithm
- Building recommendation engines via collaborative filtering
8. Fundamentals of Business Analytics
- Understand the role of data analytics in supporting business decision-making.
- Differentiate between types of analytics (descriptive, diagnostic, predictive, prescriptive) and their uses.
- Practice framing business problems as data questions and identifying relevant metrics/KPIs.
- Learn how to consider stakeholder needs and context when planning analyses.
9. Data Ethics and Privacy in Analytics
- Learn core principles of data ethics (privacy, consent, fairness, transparency, accountability).
- Understand legal and compliance considerations (e.g. data protection regulations) in handling data.
- Recognize common ethical pitfalls (bias, misuse of data) and how to address them.
- Develop strategies to ensure ethical, responsible use of data in projects.
10. Exploratory Data Analysis
- Apply advanced EDA techniques to uncover patterns, trends, and anomalies in data.
- Utilize data profiling tools to quickly generate summary reports of datasets.
- Learn methods for handling large or complex datasets (sampling, using advanced libraries or workflows).
- Improve skills in visualizing and interpreting data distributions and relationships.
11. Machine Learning for Advanced Data Analytics
- Grasp fundamental machine learning concepts (supervised vs. unsupervised learning, overfitting, etc.).
- Learn to apply common ML algorithms for analysis: e.g. regression, classification, clustering.
- Gain practical experience using Python libraries (such as scikit-learn) to train and evaluate simple models.
- Understand how to interpret model results and incorporate predictive insights into business contexts.
12. Data Storytelling and Communication
- Develop skills to turn analytical findings into a compelling narrative.
- Learn best practices for data visualization in the context of storytelling (choosing the right chart, simplifying complexity).
- Understand how to tailor communication of insights to different audiences (technical vs. non-technical).
- Practice presenting data-driven recommendations clearly and persuasively.
13. Analytics Case Studies and Real-World Scenarios
- Apply the full analytics process to real-world data problems from various domains.
- Practice end-to-end project execution: from defining the problem and cleaning data to analysis, modeling, and presenting results.
- Develop problem-solving skills and adaptability when faced with open-ended data challenges.
- Build a portfolio of case study projects to demonstrate skills to employers.
14. Industry Tools and Best Practices
- Understand how to structure and document Jupyter Notebook analyses for reproducibility and sharing.
- Explore methods for deploying or sharing results (e.g. publishing notebooks, creating simple dashboards).
- Familiarise yourself with other workflow tools and best practices that increase productivity and teamwork in data projects.
15. Data Analytics with Scikit-learn
- NumPy, Pandas, Matplotlib and Scikit-learn Installation in PyCharm
- Price Prediction Application
- Salary Prediction of Employees
- Marks Prediction of Students
- How to Visualize Predicted Value
16. Linear Regression Model for Multiple Variable
- Insurance Price Prediction
- Salary Prediction
- Home Price Prediction
- Sales Forecasting for a Retail Store
17. Machine Learning Models for Data Analytics
- How to Save Trained Model in File using Pickle
- How to Save Trained Model in File using Joblib
- Dummy Varibales in Dataset
- Jupyter Notebook Installation
- Write ML Algorithm in Jupyter Notebook
- Split Data into Training and Test Data
18. Logistic Regression for Data Analytics
- What is Logistic Regression
- Insurance Prediction
- Employee Retention Prediction
- Digits Prediction
- Car Price Prediction
- Loan Prediction
- Loan Approval Classifier
- Suspicious Login Detection
19. Decision Tree for Data Analytics
- What is Decision Tree?
- Practical Implementation of Decision Tree
- Iris Flower
- Tennis Game
- Titanic Movie
- College Admission Eligibility Predictor
- Restaurant Preference Classifier
- Flight Booking Cancellation Prediction
20. Random Forest Algorithm for Data Analytics
- What is the Random Forest Algorithm?
- Loan Approval Classifier
- Tourist Destination Recommender
- Credit Card Fraud Detection
- Digits Image Classification
- Iris Flower Prediction
21. Unsupervised Learning
- Introduction to Clustering Algorithms
- What is Clustering?
- Differences Between Supervised and Unsupervised Learning
- k-Means Clustering
- Hierarchical Clustering
22. Artificial Intelligence
- Introduction to Artificial Intelligence
- Working with AI
- Using AI in Data Analytics
- Real-time application of AI
- Differences between traditional AI and Generative AI
23. Gen AI & Prompt Engineering
- Introduction to Gen AI
- Introduction to Prompt Engineering
- Working with Prompts
- Importance in working with AI models like GPT and DALL·E.
- Gen AI Applications across industries (e.g., BFSI, Retail, Telecom).
- Best Practices of Prompt Design
- Types of prompts
- Components of effective prompts: Clarity, specificity, and relevance.
- Challenges in Prompt Engineering
- Understanding limitations of AI models.
- Ethical considerations in designing and using pro
24. Data Analytics Projects
- Sales Forecasting with Time Series Analysis
- Customer Segmentation
- Customer Churn Prediction
- Market Basket Analysis
- Web Traffic Trend Analysis and Anomaly Detection
- Credit Card Fraud Detection
- Supply Chain Demand Forecasting
- IoT Sensor Anomaly Detection
- Predictive Maintenance for Manufacturing Equipment
- IoT Sensor Data Analytics for Smart Homes
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1. Getting Started with Microsoft Power BI
- What is Business Intelligence
- Stages of Business Intelligence
- Use Cases of BI
- What is Power BI?
- Benefits of using Power BI
- Real-world applications of Power BI
- Various BI Tools
2. Power BI Installation and Setup
- How to download Power BI Desktop
- Step-by-step installation guide
- Initial setup and configuration
3. Power BI Architecture and Components
Understanding Power BI components
- Desktop, Service, Mobile
- How these components work together
Power BI Desktop UI
- Introduction to Power BI Desktop
- Installation of Power BI Desktop
- Exploring the Power BI Desktop interface
- Overview of key features and tools
- Navigating through different panes and menus
4. Get Data from Different Data Sources
Identify and Connect to a Data Source
- Types of data sources supported by Power BI
- Choosing the right data source for your needs
Get Data from Flat Files and Relational Data Sources
- Importing data from Excel, CSV, and other flat files
- Connecting to databases like SQL Server, MySQL, and more
5. Power Query
Introducing the Power Query Editor and Power Query UI
- What is Power Query?
- Introduction to Power Query Editor
- Navigating the Power Query Editor interface
- Basic features and functionalities
Power Query Ribbon and Different Tabs Introduction
- Overview of the Power Query ribbon
- Understanding different tabs and their functions
6. Power Query – Clean, Transform, and Load the Data
Enhance the Structure of the Data and Table Structure
- Organizing and structuring your data tables
- Creating relationships between tables
Profile the Data
- Analyzing data quality
- Identifying and handling data issues
Evaluate and Transform Column Data Types
- Changing data types for accuracy
- Ensuring consistency across your data
Change Data Source Settings
- Updating and managing data source connections
- Handling data source changes efficiently
7. Design a Data Model
Identify Fact and Dimension Tables
- Understanding the difference between fact and dimension tables
- Organizing your data for better analysis
Define Relationships and Cardinality
- Creating relationships between tables
- Understanding one-to-many and many-to-many relationships
Design a Data Model Using a Star Schema
- Building a star schema for efficient data analysis
- Benefits of using a star schema in Power BI
8. Data Model and DAX
Data Modelling and DAX
- Introduction to Relationships
- Creating Relationships
- Cardinality
- Cross-filter direction
- Use of inactive relationships
Introduction of DAX
- Why is DAX used?
- DAX syntax
- DAX functions
- Context in DAX
- Measures using DAX
9. Data Model and Calculations (DAX)
Create Calculated Tables
- Adding new tables using DAX
- Practical examples of calculated tables
- Learning about table, information, logical, text, iterator,
- Time intelligence functions (YTD, QTD, MTD)
Create Calculated Columns
- Creating columns with custom calculations
- Using DAX for dynamic data manipulation
10. Basic Measures using DAX
Create Basic Measures Using DAX
- Understanding measures in Power BI
- Writing simple DAX formulas for calculations
- Date and time functions
Difference Between Calculated Columns and Measures
- When to use calculated columns vs. measures
- Best practices for using DAX in your data model
- DAX advanced features
11. Create Reports
Create Report from Raw Data
- Starting your first Power BI report
- Importing and organizing your data in reports
12. Visualization in Reports
Add Visualization Items to Reports
- Inserting charts, graphs, and other visuals
- Customizing visual elements for clarity
Choose an Appropriate Visualization Type
- Selecting the right visual for your data
- Understanding different visualization options
13. Power BI Charts and Custom Visuals in Reports
Formatting and Configuring Power BI Charts
- Enhancing the look and feel of your charts
- Using formatting tools effectively
Use a Custom Visual and Import Custom Visuals
- Exploring the Power BI marketplace for custom visuals
- How to import and use custom visuals in your reports
14. Customize Themes and Conditional Formatting in Reports
Apply and Customize Themes
- Changing the overall theme of your report
- Creating a consistent look and feel
Configure Conditional Formatting
- Highlighting important data points
- Using color rules to emphasize trends
15. Sorting and Slicing in Reports
Apply Sorting
- Sorting data to improve readability
- Techniques for effective data sorting
Apply Slicing and Filtering
- Adding slicers for interactive filtering
- Using filters to focus on specific data
16. Create Hierarchies and AI Visuals in Reports
Create Hierarchies
- Building data hierarchies for better navigation
- Using hierarchies to drill down into data
Drilldown/Drillup into Data Using Interactive Visuals
- Exploring data at different levels of detail
- Making your visuals interactive and dynamic
AI Visuals – Q & A, Key Influencers, Decomposition Tree, Smart Narratives
- Utilizing AI-powered visuals for deeper insights
- How to implement and use AI visuals in your reports
17. How to Publish Reports
Publish Reports
- Sharing your reports with others
- Publishing reports to the Power BI service
18. Manage Workspaces
Create and Configure a Workspace
- Setting up workspaces for collaboration
- Organizing your projects within workspaces
Assign Workspace Roles
- Managing user roles and permissions
- Ensuring secure access to your data
Configure and Update a Workspace App
- Creating apps for easy access to reports and dashboards
- Updating and maintaining workspace apps
19. Create Dashboards
Create Dashboard
- Building your first Power BI dashboard
- Adding and arranging visuals on the dashboard
Manage Tiles on a Dashboard
- Customizing tiles for better presentation
- Organizing tiles for optimal layout
Pin a Live Report Page to a Dashboard
- Linking live report data to your dashboard
- Ensuring real-time data updates on your dashboa
20. Generative AI in Power BI
- Introduction to GenAI and its integration with Power BI
- Using Power BI Copilot for report generation and data analysis
- Enhancing dashboards with AI-generated summaries and insights
- Automating data storytelling with natural language narratives
- Real-world use cases of GenAI in business intelligence
- Best practices for using GenAI features responsibly
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1. Getting Started with Excel
- Introduction to Interface
- Navigating Sheets and Workbooks
- Ribbon in Excel
- Entering and Editing Data
- Basic Formatting: Fonts, Alignment, Number Formats
- Working with Cell Ranges and Selections
- Saving, Sharing and Collaborating
2. Working with Formulas and Functions
- Understanding Formulas: Operators and Cell References
- Basic Functions: SUM, AVERAGE, COUNT
- Working with Dates and Times
- Introduction to Logical Functions: IF, AND, OR
- Common Text Functions: CONCATENATE, LEFT, RIGHT, MID
- More on Functions: MIN, MAX, MEDIAN
- Array Formulas Basics
- Error Handling in Formulas
- ISNA and IFERROR Function
3. Managing Sheets and Workbooks
- Advanced Sheet Operations
- Jumping Between Sheets
- Mastering Multiple Workbooks
- Hiding and Unhiding Rows and Columns
- Freezing Panes and Splitting Screens
4. Data Formatting and Presentation
- Conditional Formatting: Highlighting Data
- Creating and Applying Cell Styles
- Working with Tables
- Sorting and Filtering Data
- Charts and Graphs: Creating Visualizations
- Customizing Charts: Formatting and Design
- Sparklines: Visual Data in Cells
5. Data Analysis Tools
- Sorting and Filtering Data: Advanced Techniques
- Working with Pivot Tables: Creating Interactive Reports
- Pivot Charts: Visualizing Pivot Table Data
- What-If Analysis: Goal Seek
- Data Tools in Excel
- Data Validation: Ensuring Data Integrity
- Importing and Exporting Data (CSV, Excel, etc.)
6. Advanced Formulas and Functions Part – 1
- Lookup Functions: VLOOKUP, INDEX, MATCH, XLOOKUP
- Statistical Functions: COUNTIFS, SUMIFS, AVERAGEIFS, STDEVIFS
- Array Formulas: Working with Ranges of Data
7. Advanced Formulas and Functions Part – 2
- Financial Functions: PMT, NPV, IRR, FV, PV
- Advanced Text Functions: LEN, PROPER, TRIM, FIND, SEARCH
- Working with Dates and Times (Advanced)
- Advanced Logical Functions
8. Power Query for Data Transformation
- Introduction to Power Query and Data Import
- Data Cleaning and Transformation
- Adding Columns and Combining Data
- Power Query Automation and Web Scraping
- Loading Data and Best Practices
- Power Pivot in Excel
9. Excel Automation with Macros and VBA
- Introduction to Excel Automation and Macros
- Getting Started with the VBA Editor (Interface)
- VBA Fundamentals: Variables, Data Types
- Working with Objects: Ranges, Worksheets, and Workbooks
- Automating Tasks & User Interaction
- Debugging, Error Handling, & Macro Security
10. AI Features in Excel
- Analyze Data Feature: Generating Insights and Visualizations
- Ideas Feature: Formula Suggestions, Data Patterns
- Data from Picture: Extracting Data from Images
- Forecasting with Forecast Sheet: Predicting Future Trends
11. Tips and Tricks
- Keyboard Shortcuts and Productivity Tips
- Optimizing Performance for Large Datasets
12. Building Interactive Dashboards
- Planning and Designing Dashboards
- Using PivotTables and PivotCharts for Dynamic Data
- Slicers and Timelines for Interactive Filtering
- Creating Interactive Form Controls
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1. Understanding SQL
- Introduction to SQL
- SQL Prerequisites
- What is SQL
- Purpose of SQL
- MySQL Server Installation
- SQLYog Installation
- Difference Between DBMS and RDBMS
- SQL Database and Database Server
- Comments in SQL
2. Commands in SQL Part – 1
- What are SQL Commands
- SQL DDL Statements
- SQL DDL Statements – Alter and Drop Command
- Practical Implementation of SQL DDL Command
- SQL DML Statements
- SQL DML Statements – Delete and Update Command
- Practical Implementation of SQL DML Command
3. Commands in SQL Part – 2
- SQL TCL Command
- Practical Implementation of TCL Command
- SQL DCL Command
- Practical Implementation of DCL Command
4. Data Types in SQL
- What are Data Type in SQL
- String Data Type in SQL
- Numerical Data Type in SQL
- Date Data Type in SQL
- Practical Implementation of Date Data Type in SQL
5. Operators in SQL
- What are Operators in SQL
- Arithmetic Operator in SQL
- Relational Operator in SQL
- Logical Operator in SQL
- Practical Implementation of Arithmetic, Relational and Logical Operators
- SQL String Operators
- SQL Like and Not Like Command
6. Clauses in SQL Part – 1
- Constraints in SQL
- Practical Implementation of Constraints in SQL
- Order by Clause in SQL
- Practical Implementation of Order by Clause
- GROUP BY and HAVING in SQL
- Practical Implementation of GROUP BY and HAVING in SQL
7. Clauses in SQL Part – 2
- Like Command in SQL
- Practical Implementation of Like Command in SQL
- Between and In Command in SQL
- Practical Implementation of Between and In Command
- ANY and ALL Operators
- Alias in SQL
- Practical Implementation of Alias in SQL
8. SQL Joins
- What are Joins in SQL
- Practical Implementation of SQL Joins
- Practical Implementatin of Self Join
9. SQL Keys
- Primary Key and Foreign Key Constraint in SQL
- Practical Implementation of Primary Key and Foreign Key Constraint
10. Advanced SQL
- Injection in SQL
- Practical Implementation of Injection
- Null Functions
- Check Constraint in SQL
- Practical Implementation of Check Constraint
11. Deep Dive in SQL
- Default Constraint in SQL
- Practical Implementation of Default Constraint
- Null Value in SQL
- Practical Implementations of Null Value in SQL
- Auto Increment in SQL
- Practical Implementation of Auto Increment in SQL
12. Functions in SQL Part – 1
- Aggregate Functions in SQL
- Practical Implementation of Aggregate Functions
- String Functions
- Nested Query in SQL
- Practical Implementation of Nested Query
13. Functions in SQL Part – 2
- SQL Window Function
- Practical Implementation of Window Function
- Bulk Insert in SQL
- Backup and Restore in SQL
14. Projects in SQL
- SQL Library Management System Project
- SQL Sales Data Analysis Project
- SQL Restaurant Billing System Project
- SQL Patient Record System Project
- SQL Vehicle Service Booking System Project
- SQL Simple Inventory ans Sales System Project
- SQL Movie Rating System Project
- SQL E-commerce Product Refund and Return Analytics System Project
💻 Self-paced 🔵 Instructor-led Live
Tools & Technologies
Python
Jupyter
Anaconda
NumPy
Pandas
Matplotlib
Scikit-learn
SQL
MySQL
AI
Power BI
ChatGPT
Excel
GenAI
Data Analytics using Python Projects
Build Real-time Projects & Get Job-Ready!
-
Sales Forecasting with Time Series Analysis
Predict future sales trends to support business planning and inventory management.Customer Segmentation
Group customers based on their behavior to improve marketing strategies and product targeting.Customer Churn Prediction
Identify customers likely to stop using a service to implement effective retention strategies. -
Market Basket Analysis
Discover purchasing patterns to recommend relevant products and increase sales opportunities.Sentiment Analysis
Analyze customer feedback to understand public opinion and improve brand perception.Web Traffic Trend Analysis and Anomaly Detection
Analyze website logs to detect unusual traffic spikes or drops using seasonal decomposition -
Credit Card Fraud Detection
Detect suspicious transactions to minimize financial losses and enhance transaction security.Supply Chain Demand Forecasting
Predict product demand accurately to optimize inventory levels and reduce stockouts.E-commerce Recommender System
Suggest personalized products to customers to improve shopping experience and boost sales. -
IoT Sensor Anomaly Detection
Monitor sensor data to identify unusual patterns and prevent potential system failures.Predictive Maintenance for Manufacturing Equipment
Anticipate equipment failures in advance to reduce downtime and maintenance costs.IoT Sensor Data Analytics for Smart Homes
Analyze smart home device data to optimize energy usage and improve living comfort.
Learn From Industry’s Best Instructors
Industry-renowned Certification
Our learners are working in leading organizations
Numbers That Speak Our Success
70000+
Career Carved
100+
Comprehensive Courses
4.8
Average Rating
19 LPA
Average Package
90+
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8 Out of 10 Learners Land High-Paying Jobs – Read Testimonials
Trusted by Thousands, Proven by Success – Read All Testimonials
Course Objectives
Why
What
Prerequisites
Who
Course Benefits
Jobs
Companies
Data Analytics with Python Course Objectives
By the end of this Data Analytics with Python course, you will:
- Master the Basics of Python for Data Analysis: Learn Python from scratch, focusing on its powerful libraries like NumPy and Pandas, which are essential for data manipulation and analysis.
- Explore Data with Pandas: Discover how to clean, transform, and explore data sets using Pandas. You’ll be able to handle large datasets efficiently and extract meaningful insights.
- Visualize Data with Matplotlib and Power BI: Learn to create professional data visualizations with Matplotlib in Python and Power BI to present your data clearly, and visually appealingly.
- Introduction to Machine Learning with Scikit-learn: Get an introduction to machine learning, using Scikit-learn to apply algorithms and start making predictions with your data.
- Hands-On Projects: Work on real-world data analytics projects that will give you practical experience and prepare you for the challenges you’ll face as a Data Analyst.
- Boost Your Career: Gain the skills employers are looking for and prepare for a career in data analytics with Python, one of the most in-demand fields today.
Through a mix of theory, practice, and projects, this course will give you the tools to thrive in data analytics. Ready to get started? Let’s dive into the data!
Why should you learn Data Analytics with Python?
- Python is easy to learn and use, even for beginners.
- High demand for data analysts across various industries.
- Python has powerful libraries like NumPy, Pandas, and Matplotlib.
- Data Analytics helps make data-driven decisions.
- Opens career opportunities in finance, healthcare, marketing, and more.
- Python is versatile – you can analyse data, create models, and visualise results.
- Demand for Python in data science is skyrocketing.
- Offers a hands-on approach to understanding data.
- Python is widely used in automation and machine learning.
- Boosts your problem-solving and critical-thinking skills.
- It’s highly customisable to suit various industries and needs.
- It makes working with big data and complex datasets easier.
What is Data Analytics with Python?
The only way to unlock this data is through Data Analytics with Python. With its easy-to-use syntax and robust libraries, Python allows anyone — even non-programmers — to gain insights from data quickly. Whether you want to analyse business trends, make data-driven decisions, or find hidden patterns, you’ll need Python.
Today, data is everywhere. Over 2.5 quintillion bytes of data are created each day in the world. But here’s the challenge: how do you understand it all? That’s where Data Analytics comes in.
Using Python’s libraries like NumPy, Pandas, and Matplotlib, you can clean, manipulate, and visualise data in ways that reveal patterns and trends you never knew existed. The simplicity of Python’s data analysis approach gives a new user a good head-start into efficiently analyzing data. This opportunity can carve out a path in any number of careers in fields as diverse as healthcare, finance and e-commerce.
And when working with numbers with Python, you don’t just work with them – you animate them. You’ll learn how to create and run clickable charts and models speak louder than words so that you can make better decisions quicker. Ready to turn data into insights? Join the course and start your data journey today!
What to do before you begin?
This Data Analytics with Python course is designed for beginners, so no prior programming experience is required. However, having a basic understanding of the following will help you make the most of this course:
- Basic Computer Skills: Familiarity with computer use and navigating software like Excel or web browsers.
- Basic Mathematics: A basic understanding of concepts like averages, percentages, and basic statistics (mean, median, mode) will be helpful.
- Curiosity and Problem-Solving Skills: A keen interest in data and a logical mindset to solve real-world problems using data.
- Familiarity with Excel: While not mandatory, knowing how to work with Excel will give you a head start when learning data manipulation techniques.
The Data Analytics with Python course will teach you everything you need to know, from Python basics to advanced data analysis techniques, step by step.
Who should go for this Data Analytics with Python course?
This Data Analytics with Python course is open to anyone curious about data and eager to build a career in the booming field of data analysis! Whether you’re a student, working professional, or complete beginner, this course is designed to meet your needs.
Here’s Who Will Benefit the Most:
- Students: Engineering or non-engineering or fresh graduate students looking to learn in-demand skills and aiming for high-paying roles in data analysis.
- Working Professionals: Professionals in non-technical roles (like marketing, finance, or HR) who want to add data skills to their portfolio. Those looking to shift into data analytics or related fields without programming experience.
- Complete Beginners: No technical or coding background? No problem! This course teaches everything from scratch.
- Entrepreneurs & Business Owners: Want to make data-driven decisions for your business? Learn how to analyze and visualize data to uncover growth opportunities.
- Career Switchers: If you’re considering a career shift, data analysis is a future-proof choice with versatile applications across industries.
By enrolling in our Data Analytics with Python course, you can expect the following benefits:
- High-Demand Skills, Zero Coding Background Required: Gain hands-on expertise in tools like Python, SQL, Excel, and Power BI, used by top companies worldwide.
- Practical, Job-Ready Training: Work on real-world datasets and solve practical problems faced by finance, healthcare, and e-commerce industries.
- Career-Boosting Opportunities: Data analysts are among the most in-demand professionals, with salaries starting at ₹6-8 LPA in India and $70,000+ globally.
- Learn Industry-Standard Tools and Techniques: Master tools like NumPy, Pandas, Matplotlib, and Scikit-learn, and create interactive dashboards with Power BI.
- Versatility Across Industries: Data analysis skills are needed in almost every field, from entertainment (Netflix recommendations) to finance (fraud detection) and healthcare (predictive patient care).
- Personalized for Beginners: Our step-by-step approach ensures you build confidence and expertise.
- Build a Solid Foundation for the Future: Data analysis is the gateway to fields like Data Science, Artificial Intelligence, and Machine Learning. Lay the groundwork to transition into these advanced domains if you expand your expertise.
- Empower Decision-Making Skills: Whether you’re a student, professional, or entrepreneur, these skills will amplify your decision-making capabilities.
- Flexible Learning at Your Own Pace: The self-paced nature of this course ensures that you can learn at a speed comfortable for you without overwhelming schedules.
Jobs after Learning this Data Analytics with Python Course
Mastering data analysis opens the door to many high-demand, well-paying job roles across industries. Here are the top career paths you can pursue after completing this course:
1. Data Analyst
- Role: Transform raw data into actionable insights for businesses.
- Average Salary: ₹6-8 LPA (India), $70,000+ (International).
- Industries: E-commerce, finance, healthcare, and more.
2. Business Analyst
- Role: Bridge the gap between business goals and technical solutions by analyzing data and improving processes.
- Average Salary: ₹7-10 LPA (India), $75,000+ (International).
- Industries: Banking, IT, and consulting.
3. Reporting Analyst
- Role: Create dynamic dashboards and visualizations to help businesses track KPIs and performance.
- Tools Used: Power BI, Excel, SQL.
- Average Salary: ₹5-7 LPA (India), $65,000+ (International).
4. SQL Developer
- Role: Design, manage, and analyze databases using SQL.
- Average Salary: ₹6-9 LPA (India), $72,000+ (International).
- Industries: Tech, telecom, and retail.
5. Data Visualization Specialist
- Role: Create impactful visual dashboards and presentations to communicate complex data insights.
- Tools Used: Power BI, Matplotlib, Excel.
- Average Salary: ₹5-8 LPA (India), $70,000+ (International).
6. Market Research Analyst
- Role: Analyze market trends, consumer behavior, and competition to guide business strategies.
- Average Salary: ₹4-6 LPA (India), $60,000+ (International).
7. Operations Analyst
- Role: Use data to optimize processes and improve operational efficiency.
- Industries: Logistics, supply chain, and manufacturing.
8. Financial Analyst
- Role: Analyze financial data, forecast trends, and assist in decision-making for investments and budgeting.
- Average Salary: ₹7-10 LPA (India), $75,000+ (International).
9. Product Analyst
- Role: Evaluate product performance and user behavior to improve customer experience and guide product development.
- Industries: Technology, SaaS, and startups.
10. Entry Point to Advanced Roles
This course lays the foundation for transitioning into high-level roles such as:
- Data Scientist
- Machine Learning Engineer
- AI Specialist
Our students are working in leading organizations
Features of Data Analytics with Python Course
Need Guidance? Ask Our Experts Now!
Data Analytics with Python Online Training FAQs
What is Data Analytics?
Data Analytics involves inspecting, cleaning, and modeling data to discover useful information, support decision-making, and uncover trends.
Why should I choose Data Analytics as a career?
Data Analytics is a highly demanded career with opportunities in multiple industries, such as finance, healthcare, marketing, and technology.
What skills do I need to become a Data Analyst?
Key skills include data cleaning, data visualization, statistical analysis, and proficiency in tools like Excel, SQL, and Python.
Do I need to know programming to start learning Data Analytics?
No! With Python, learning Data Analytics becomes accessible even for non-programmers, using simple syntax and powerful libraries.
How long does it take to become proficient in Data Analytics?
Depending on your pace, it can take a few months to a year to become proficient in the basics of Data Analytics.
Is Data Analytics the same as Data Science?
Data Analytics focuses on analyzing and interpreting data, while Data Science includes advanced modeling, machine learning, and deeper statistical techniques.
Is Data Analytics in high demand?
Yes, with the increasing importance of data in decision-making, the demand for skilled Data Analysts is growing rapidly across industries.
What are the career growth opportunities in Data Analytics?
Career growth can lead to roles like Data Scientist, Business Analyst, Data Engineer, and Chief Data Officer (CDO).
What are the challenges in Data Analytics?
Common challenges include cleaning messy data, handling large datasets, and understandably presenting complex findings.
How much does a Data Analyst earn?
Salaries vary by location and experience, but a Data Analyst’s salary can range from $50,000 to $100,000 annually in many countries.
