Data Analytics using Python Course with Certificate [Hindi]
This transformation course teaches you everything necessary to analyze data and extract insight using NumPy, Pandas, Matplotlib, SQL, Excel, and Power BI. From creating beautiful dashboards to predicting trends with machine learning, you’ll learn the skills the best companies seek. Prepare for a career that shapes the future; get started today!
Course Highlights – Everything You Need to Succeed
- 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 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 — Enroll Now
Master Data Analytics with Python from Scratch
Join our hands-on Data Analytics with Python course crafted by industry veterans and build real-world skills. It’s not just a course, it’s a job-ready bootcamp.
Start Anytime (self-paced) |
Duration 90+ Hrs |
Access Duration 2 Years |
Price |
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Online Data Analytics with Python Training Course Curriculum
- What is NumPy and its significance in numerical computing
- Installing NumPy: Step-by-step guide for Python
- How to install and configure NumPy in PyCharm
- Creating NumPy arrays
- Basics of NumPy arrays
- ndarray attributes
- Real-world applications of NumPy array attributes
- Exploring the 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 for computations.
- Statistical functions in NumPy
- Differences between Reference, View, and Copy in NumPy arrays
- Array concatenation
- Joining arrays with methods like stack(), vstack(), hstack(), and depth()
- Splitting arrays into smaller parts using split(), array_split(), vsplit(), and hsplit()
- Introduction to Pandas
- Installing Pandas on Windows
- Setting up Pandas in PyCharm
- How to download and prepare datasets for analysis
- Understanding Series in Pandas
- Key properties of Series and their applications
- Mathematical Operations on Pandas Series
- 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
- 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 for summarizing data
- 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
- Overview of Matplotlib for data visualization.
- Step-by-step installation guide for Matplotlib in PyCharm.
- Installing the Matplotlib library in Python
- Basics of chart design
- 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 for professional-looking visuals
- Understanding the Legend function
- Grid lines
- 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
- Overview of Scikit-learn
- Key features of Scikit-learn
- Installation and setup
- Exploring Scikit-learn’s ecosystem
- Data preparation techniques for machine learning
- Handling missing values
- Encoding categorical variables
- Feature scaling: Normalization and standardization using MinMaxScaler and StandardScaler
- Splitting datasets
- Linear Regression
- Logistic Regression
- Decision Trees
- Support Vector Machines (SVM)
- K-Nearest Neighbors (KNN)
- Clustering
- Dimensionality Reduction
- Anomaly Detection
- Performance metrics for classification
- Performance metrics for regression
- Confusion matrix
- Cross-validation
- Grid Search
- Randomized Search
- Early stopping
- Using pipelines
- Getting started with Microsoft Excel
- Understanding tabs, commands, and ribbons for effective navigation
- Introduction to cell references
- Entering and saving data
- Correct and incorrect methods of formula application in Excel
- Shortcut keys
- Alignment tab: Centering, indenting, and text wrapping.
- Number formatting: Adjusting formats for currencies, dates, and percentages.
- Advanced font tab usage: Styling, coloring, and highlighting text.
- Formatting numbers and dates for clear data representation.
- Basics of printing
- The art of writing formulas
- Sum Functions: Introduction to SUM, SUMIF, and SUMIFS with examples.
- Count Functions: Learning COUNT, COUNTIF, and COUNTIFS for data analysis.
- Average Functions: Understanding AVERAGE, AVERAGEIF, and AVERAGEIFS for calculating data trends.
- Text manipulation: Applying LOWER(), UPPER(), and PROPER() functions
- Handling symmetrical and non-symmetrical data with practical examples
- Using delimited and fixed-width tools to format complex datasets
- Introduction to the IF function
- Using IF with AND/OR
- Advanced lookup techniques: Introduction to VLOOKUP and HLOOKUP
- Conditional VLOOKUP for more refined searches
- Using MATCH and INDEX for dynamic referencing
- Combining VLOOKUP with MATCH for complex data retrieval
- Highlighting cells based on rules and conditions
- Using top/bottom rules to identify key data points
- Function-based formatting for advanced customization.
- Working with date and time formats: NOW(), TODAY(), and auto-fill features
- Calculating working days and durations using DATEDIF()
- Protecting worksheets: Locking cells and hiding formulas
- Securing workbook structures and enabling data protection
- Data validation: Creating dependent dropdown lists for error-free inputs
- Introduction to charts: Bar, column, line, area, and pie charts
- Advanced visualizations: Bubble charts, 3D charts, and map charts
- Modifying chart layouts: Titles, axes, and data labels
- Representing data tables within charts for enhanced clarity
- Grouping, sorting, and filtering data in Pivot Table
- Using slicers and pivot charts for interactive dashboards
- Advanced pivot techniques: Grouping, summarizing, and applying calculations
- Recording macros to automate repetitive tasks
- Editing, deleting, and securing macros for effective use
- Assigning styles and functionality to recorded macros
- Understanding Business Intelligence (BI) and its stages
- Overview of Power BI and its architecture
- Power BI use cases, benefits, and real-world applications
- Installing Power BI Desktop and initial setup
- Key components: Power BI Desktop, Service, and Mobile
- Introduction to Power BI’s interface and tools
- Using the Power Query Editor
- Cleaning data: Removing duplicates and transforming column types.
- Structuring data
- Fact vs. Dimension tables
- Creating data models using star schemas for efficiency
- Understanding relationships and cardinality in Power BI
- Introduction to DAX: Syntax, functions, and context
- Creating calculated columns, measures, and tables
- Advanced DAX: Time intelligence, logical, and iterator functions
- Designing reports with raw data: Adding charts, graphs, and visuals
- Formatting charts and applying conditional formatting
- Interactive dashboards: Slicers, filters, and drill-downs
- Understanding SQL and its significance in data manipulation
- Purpose of SQL in real-world applications
- Why SQL is an essential skill for data analysts
- Introduction to Database Management Systems (DBMS)
- What is Relational Database Management System (RDBMS)?
- Key differences between DBMS and RDBMS
- Database vs. Database Server: Definitions and differences
- Introduction to Single Databases and Database Servers
- Overview of Client-Server Architecture
- Hands-on: Installing and setting up MySQL
- What are SQL commands? Types of SQL commands
- MySQL data types: String, numeric, and date/time
- Introduction to DDL (Data Definition Language) commands
- DML commands: INSERT, SELECT, DELETE, and UPDATE
- Using SQL tools: MySQL Workbench and SQLyog
- Operators in SQL: Relational, BETWEEN, IN, and LIKE
- Aggregate functions: COUNT, AVG, SUM, etc
- Writing and using nested queries for complex data retrieval
- Benefits of aggregate and nested functions
- ORDER BY and GROUP BY clauses
- SQL aliase
- Using constraints in MySQL: Primary Key, Foreign Key, CHECK, DEFAULT
- Understanding NULL and IS NULL values
- Auto-increment fields and their use cases
- Introduction to SQL Joins: Inner, Left, Right, and Full joins
- Why and when to use joins
- Combining data from multiple tables with practical examples
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Our learners are working in leading organizations

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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

Why should you enroll in this Data Analytics with Python Course?
- Master the most in-demand Python libraries like NumPy, Pandas, Matplotlib, Scikit-learn, Excel, SQL, and Power BI
- Whether you’re a beginner, a working professional, or looking to pivot into data analytics, this course adapts to your learning needs
- Companies like Google, Amazon, and Netflix heavily rely on Python and Power BI for their analytics
- This course isn’t just about theory, it’s about applying knowledge
- With this course, you’ll be ready to explore roles in finance, marketing, technology, education, and more
- Stand out as companies across industries, from healthcare to e-commerce, seek skilled data professionals
- The global analytics market is projected to reach over $100 billion by 2025, creating countless job opportunities
- Learn how to transform raw data into meaningful narratives
Features of Data Analytics with Python Course


Data Analytics with Python Online Training FAQs
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.
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.
Basic computer skills and a willingness to learn. Familiarity with high school-level math (e.g., averages, percentages) is helpful but optional.
Yes! The course starts with foundational concepts and progresses to advanced topics, making it suitable for learners at any level.
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.
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.
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.
You’ll receive a Certificate of Completion, which you can showcase on your resume and LinkedIn profile to enhance your career prospects.
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.
Enrollment can be done through our website. We offer flexible payment plans to make the course accessible to everyone.