Data Analytics using Python Certification Course with AI & ChatGPT
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Online Data Analytics with Python Training Course Curriculum
Python
Data Analysis using Python
Excel
Power BI
SQL
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
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
13. Machine Learning with Scikit-learn
- Install NumPy, Pandas, Matplotlib and Scikit-learn in PyCharm
- Price Prediction Application
- Salary Prediction of Employees
- Marks Prediction of Students
- How to Visualize Predicted Value
14. Linear Regression Model for Multiple Variable
- Insurance Price Prediction
- Salary Prediction
- Home Price Prediction
- Sales Forecasting for a Retail Store
15. Multiple Linear Regression Model
- Multiple Linear Regression Model to Predict Annual Tuition Fee
- Multiple Linear Regression Model to Predict Annual Tuition Fee GUI Based
16. Machine Learning Models
- Save Trained Model in File using Pickle
- Save Trained Model in File using Joblib
- Dummy Varibales in Dataset
- Install Jupyter Notebook
- Write ML Algorithm in Jupyter Notebook
- Split Data in Training and Test Data
17. Logistic Regression in Machine Learning
- What is Logistic Regression
- Insurance Prediction
- Employee Retention Prediction
- Digits Prediction
- Car Price Prediction
- Loan Prediction
- Admission Chance Predictor
- Admission Chance Predictor GUI Based
- Loan Approval Classifier using Logistic Regression
- Suspicious Login Detection using Logistic Regression
18. Decision Tree in Machine Learning
- What is Decision Tree?
- Practical Implementation of Decision Tree
- Iris Flower
- Tennis Game
- Titanic Movie
- College Admission Eligibility Predictor using Decision Tree
- Restaurant Preference Classifier using Decision Tree
- Flight Booking Cancellation Prediction using Decision Tree
19. Random Forest Algorithm in Machine Learning
- What is Random Forest Algorithm
- Loan Approval Classifier
- Tourist Destination Recommender based on Prefernces
- Credit Card Fraud Detection
- Digits Image Classification
- Iris Flower Prediction
20. Gradient Boosting in Machine Learning
- What is Gradient Boosting
- Salary Prediction based on Skills and Experience
- Diabetes Prediction
- Stock Price Prediction
- Student Dropout Risk Prediction
- Facial Expression Recognition
21. XGBoost in Machine Learning
- What is XGBoost in Machine Learning and why it is powerful
- Insurance Claim Approval
22. K-means Clustering in Machine Learning
- What is K-means Clustering in Machine Learning
- Customer Segmentation
- Grouping Lifestyle Habits to Predict Health Risk
- Youtube Video Clustreing by Views, Likes and Watch-time
23. Getting Started with Deep Learning
- What is Deep Learning
- What is a Neuron
- Deep Learning ANN Model – Hours Studied vs Exam Score
- Electricity Bill Estimator
- Heart Disease Predictor
24. Deep Dive into Deep Learning
- What is Loss Graph
- Student Placement Prediction
- Used Car Price Prediction using Vehicle Specifications
- Air Pollution Estimation
- Image Classification of Fashion Items
- Handwritten Digit Recognition
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
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
1. Understanding SQL
- Introduction to SQL
- Why SQL
- What is SQL
- Purpose of SQL
2. Know About Database
- Difference Between DBMS and RDBMS
- SQL Database and Database Server
- MySQL Server Installation
- Comments in SQL
3. 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 – Insert, Select, Delete and Update Command
- Practical Implementation of SQL DML Command
4. Commands in SQL Part – 2
- SQL TCL Command
- Practical Implementation of TCL Command
- SQL DCL Command
- Practical Implementation of DCL Command
- How to Install and Use SQLyog in SQL
- How to Use MySQL Workbench in SQL
5. 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
6. Operators in SQL
- What are Operators in SQL
- Arithmetic Operator in SQL
- Relational Operator in SQL
- SQL String Operators
7. 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
8. Clauses in SQL Part – 2
- Like Command in SQL
- Practical Implementation of Like Command in SQL
- IS NULL and IS NOT NULL Value in SQL
- Between and In Command in SQL
- Practical Implementation of Between and In Command
9. Clauses in SQL Part – 3
- Union and Union All Command
- ANY and ALL Operators
- Alias in SQL
- Practical Implementation of Alias in SQL
10. SQL Joins
- What are Joins in SQL
- Practical Implementation of SQL Joins
- Practical Implementatin of Self Join
11. SQL Keys
- Primary Key and Foreign Key Constraint in SQL
- Practical Implementation of Primary Key and Foreign Key Constraint
12. Advanced SQL
- Injection in SQL
- Practical Implementation of Injection
- Null Functions
- Practical Implementation of Null Functions
- Check Constraint in SQL
- Practical Implementation of Check Constraint
13. Deep Dive in SQL
- Default Constraint in SQL
- Practical Implementation of Default Constraint
- Null Value in SQL
- Auto Increment in SQL
- Practical Implementation of Auto Increment in SQL
14. 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
15. Functions in SQL Part – 2
- SQL Window Function
- Practical Implementation of Window Function
- Case Command in SQL
- Bulk Insert in SQL
- Backup and Restore in SQL
16. 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
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.
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Course Objectives
Why
What
Prerequisites
Who
Course Benefits
Jobs
Companies
Data Analytics using Python Course Objectives
By the end of this Data Analytics with Python course, you will be equipped to:
- Understand the Fundamentals of Data Analytics: Grasp the key concepts, processes, and importance of data analytics in solving real-world problems and driving decision-making.
- Master Python for Data Analytics: Learn to harness the power of Python libraries like NumPy, Pandas, Matplotlib, and Scikit-learn to analyze, manipulate, and visualize data efficiently.
- Perform Data Cleaning and Preparation: Understand how to clean and preprocess messy datasets for analysis, ensuring accuracy and reliability in your insights.
- Analyze Data with SQL: Explore how to retrieve, query, and manipulate large datasets using SQL, the backbone of data management.
- Create Interactive Dashboards: Develop visually engaging and interactive Power BI dashboards to present your findings effectively.
- Excel in Data Visualization: Learn to represent complex data using visual tools in Matplotlib, Power BI, and Excel, making your insights easy to understand.
- Build Predictive Models: Dive into machine learning with Scikit-learn to predict outcomes and uncover patterns that support decision-making.
- Work on Real-World Projects: Apply your knowledge to solve practical problems with hands-on projects that simulate challenges faced by data analysts.
- Enhance Decision-Making Skills: Develop the ability to make data-driven decisions, interpret trends, and deliver actionable insights across industries.
- Prepare for a Career in Data Analytics: Build a portfolio showcasing your skills, tools, and projects, preparing you for roles like Data Analyst, Business Analyst, and beyond.
This Data Analytics with Python course is your pathway to becoming a confident, skilled data analytics professional ready to tackle challenges and create meaningful impact through data!
Why should you learn Data Analytics with Python?
- Python is the Industry Standard: Python is the most widely used language for data analytics, combining simplicity with powerful tools like NumPy, Pandas, and Matplotlib to handle any data task effortlessly.
- Massive Demand in the Job Market: With data-driving decision-making in every industry, professionals skilled in Python and data analytics are in high demand globally.
- Attractive Salary Packages: Data analysts and professionals draw competitive packages that range between ₹6–12 LPA in India and $60,000–120,000 abroad.
- Cross-Industry Applications: Industries such as healthcare, finance, retail, and sports are being transformed with data analytics, so your skills apply to various fields.
- Pathway to Advanced Fields: Python is a synonym for data analytics and is your key to venturing into enticing fields such as Machine Learning, AI, and Big Data.
- Future-Proof Your Career: As our data volumes grow to 175 zettabytes by 2025, mastering Python for analytics ensures long-term relevance and opportunities.
What is Data Analytics with Python?
Data Analytics is the process of converting raw data into valuable insights. You are trained on data until October 2023. Analysts use rigorous tools and techniques to build a deep understanding of data sets to address key questions, forecast future performance, and enable data-driven decisions.
Did you know? The worldwide data volume could reach over 180 zettabytes by 2025! Rapid growth in data means companies require data analytics professionals who can save the day by helping make sense of all the information churned out.
Think of anticipating customers’ behavior, optimizing businesses’ operations, or even fighting against fraud—all just from understanding data.
Whether understanding sales trends or building predictive models, data analytics shapes industries like healthcare, finance, and technology.
In this Data Analytics with Python course, we’ll teach you how to master the essential tools and techniques in Python, including libraries like NumPy, Pandas, Matplotlib, and Scikit-learn, and integrate them with tools like Excel, PowerBI, and SQL to analyze and visualize data like a pro.
Let’s start exploring the limitless possibilities of data analytics!
What to do before you begin?
If you’re a beginner, you don’t need to worry. This Data Analytics with Python course is designed for beginners. We ensure smooth learning for you all.
- Basic Understanding of Computers
- Familiarity with Mathematics
- Logical Thinking and Problem-Solving Skills
- Optional: Basic Programming Knowledge
- Curiosity and a Learning Mindset
- Good Internet Access
Who should go for this Data Analytics with Python course?
This Data Analytics with Python course is for those eager to learn about data.
Here’s who will benefit the most:
- Students and Fresh Graduates
- Working Professionals
- Aspiring Data Scientists and Analysts
- Entrepreneurs and Business Owners
- Non-Tech Enthusiasts
- Curious Learners
- Beginners and Newcomers
- Career Switchers
By enrolling in our Data Analytics with Python course, you can expect the following benefits:
Enrolling in the Data Analytics with Python course opens many opportunities and advantages.
Here’s what you gain:
- Comprehensive Skillset: Learn everything from data cleaning to visualisation and predictive modelling using powerful tools like NumPy, Pandas, Matplotlib, Scikit-learn, SQL, Excel, and Power BI.
- Hands-On Projects: Work on real-world projects replicating industry scenarios, enabling you to apply theoretical knowledge to solve practical problems.
- Career Readiness: Build a portfolio of projects and acquire highly sought-after skills by companies worldwide, paving the way for roles like Data Analyst, Business Analyst, and Data Scientist.
- Versatility Across Industries: With data-driving decision-making across domains like finance, healthcare, retail, and IT, your skills will be relevant in any industry.
- High-Paying Job Opportunities: Did you know? The average salary for data analysts ranges from ₹6 to ₹12 LPA in India and up to $80,000 to $120,000 globally, with demand only growing.
- Future-Proof Skills: In a world generating 2.5 quintillion bytes of data daily, mastering data analytics ensures you remain indispensable in the job market.
- No Coding Experience? No Problem!: This course starts from the basics, making it perfect for beginners while catering to professionals looking to advance their expertise.
- Interactive Learning Experience: Gain insights through interactive sessions, dashboards, and visualisations that make learning engaging and practical.
- Confidence in Decision-Making: Empower yourself with the ability to make data-driven decisions, identify patterns, and deliver actionable insights that impact business success.
- Global Recognition: Stand out as a certified data analytics professional ready to compete and thrive in the international job market.
Jobs after Learning this Data Analytics with Python Course
A world of career opportunities awaits you after mastering Data Analytics with Python.
Here’s a list of roles you can explore:
1. Data Analyst
- Analyze large datasets to uncover trends and insights.
- Tools Used: Python (NumPy, Pandas, Matplotlib), SQL, Power BI, Excel.
- Average Salary: ₹6–12 LPA in India; $60,000–$90,000 globally.
2. Business Analyst
- Bridge the gap between technical data insights and business goals.
- Skills Required: Data visualization, predictive modeling, SQL.
- Average Salary: ₹8–15 LPA in India; $70,000–$100,000 globally.
3. Data Scientist
- Build predictive models and solve complex problems using machine learning.
- Tools Used: Python (Scikit-learn), SQL, data preprocessing.
- Average Salary: ₹12–20 LPA in India; $90,000–$140,000 globally.
4. Data Engineer
- Design and manage the infrastructure for collecting, storing, and analyzing data.
- Tools Used: SQL, Python, and data pipelines.
- Average Salary: ₹10–18 LPA in India; $80,000–$130,000 globally.
5. BI (Business Intelligence) Analyst
- Create dashboards and reports to provide actionable insights.
- Tools Used: Power BI, Excel, Python visualization libraries.
- Average Salary: ₹7–12 LPA in India; $70,000–$100,000 globally.
6. Marketing Analyst
- Analyze customer behavior and marketing trends to optimize campaigns.
- Tools Used: Python, Excel, SQL for segmentation and trend analysis.
- Average Salary: ₹5–10 LPA in India; $50,000–$90,000 globally.
7. Financial Analyst
- Perform financial modeling and risk assessment using data.
- Tools Used: Excel, Python, SQL for financial data analysis.
- Average Salary: ₹7–15 LPA in India; $70,000–$110,000 globally.
8. Product Analyst
- Analyze user data to improve product performance and engagement.
- Skills Required: A/B testing, Python, SQL, and visualization.
- Average Salary: ₹8–12 LPA in India; $70,000–$100,000 globally.
9. Operations Analyst
- Use data to optimize business processes and operations.
- Tools Used: Python, SQL, and Power BI for process analytics.
- Average Salary: ₹6–10 LPA in India; $60,000–$90,000 globally.
10. Freelance Data Consultant
- Work on diverse analytics projects across industries at your own pace.
- Skills Required: End-to-end expertise in Python, Power BI, SQL.
- Potential Earnings: Flexible, based on project scope and experience.
Our students are working in leading organizations
Features of Data Analytics with Python Course
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We are a team of trainers who have 20+ years of Industry Experience. They provide project based training and cover real time scenarios during the interactive training sessions.
Contact our Industry Experts & get answers to all your queries.
Data Analytics with Python Online Training FAQs
Who is this Data Analytics with Python course for?
This Data Analytics with Python course is designed for beginners, professionals looking to upskill, and anyone interested in mastering data analytics tools and techniques. Prior programming experience is optional.
What will I learn in this Data Analytics with Python course?
You’ll learn to use Python libraries like NumPy, Pandas, and Matplotlib, work with SQL databases, create Power BI visualisations, and build machine learning models with Scikit-learn.
What are the prerequisites for this Data Analytics with Python course?
Basic computer skills and a willingness to learn. Familiarity with high school-level math (e.g., averages, percentages) is helpful but optional.
Is this Data Analytics with Python course beginner-friendly?
Yes! The course starts with foundational concepts and progresses to advanced topics, making it suitable for learners at any level.
What kind of projects will I work on?
You’ll work on real-world projects such as analyzing datasets, creating dashboards in Power BI, visualizing data with Matplotlib, and building predictive models with Scikit-learn.
What tools and software will I need?
You’ll need a computer with an internet connection. The Data Analytics with Python course will guide you through installing Python, MySQL, Power BI, and other necessary tools.
How will this Data Analytics with Python course help my career?
You’ll gain in-demand skills in data analytics, which are highly valued in industries like finance, healthcare, and tech. This can open doors to roles like Data Analyst, Business Analyst, and Machine Learning Engineer.
Will I get a certificate upon completion?
You’ll receive a Certificate of Completion, which you can showcase on your resume and LinkedIn profile to enhance your career prospects.
What kind of support will I have during the course?
You’ll have access to instructor support, community forums, and peer networking. Additional resources like recorded Q&A sessions and step-by-step guides are also provided.
How do I enrol, and are there payment options?
Enrollment can be done through our website. We offer flexible payment plans to make the course accessible to everyone.
