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Advanced Data Science & Analytics Program with Applied AI
👉 Enroll Today & Become a Job-Ready Data Scientist!
📞 Contact: 8485898120
Starting Soon
Starting Soon
Starting Soon
RS. 20,000 Only
This course includes
- 5 Months Training Program
- Live Online Sessions
- Basic to advance Training
- 3+ Projects Implementations
- Resume & Interview Preparation
- Hindi Language
- Lifetime Validity
- Chance to win Gifts from us
About Course
The Advanced Data Science & Analytics Program with Applied AI helps students build strong skills in data analysis, machine learning, and modern AI technologies used in today’s industries. Learn how to work with real-world data, create insights, and develop intelligent solutions through practical projects and hands-on training.
This program prepares you for high-demand careers like Data Scientist, Data Analyst, and AI Specialist, helping you stay ready for the future of data-driven technology. 🚀
Course Content
Introduction to Data Science
- Introduction to Data Science : Role of Data Scientist and Data Analyst, Data Science Lifecycle,
- Types of Data – Structured, Semi-Structured and Unstructured Data, Data Collection Methods, Data Cleaning and Data Preparation, Applications of Data Science in Industry, Data Driven Decision Making
Python Programming for Data Science
- Introduction to Python : Introduction to Programming Languages, Python Installation and IDE Setup, Python Syntax and Indentation Rules, Writing First Python Program
- Variables and Data Types : Python Variables, Primitive Data Types – int, float, string, Boolean, Type Conversion and Type Casting
- Operators and Expressions : Arithmetic Operators, Relational Operators, Logical Operators, Assignment Operators
- Control Statements : Conditional Statements – If, If Else, Nested If, Looping Statements – For Loop, While Loop, Break and Continue Statements
- Functions and Modules : Defining Functions, Function Parameters and Return Values, Lambda Functions, Python Modules and Packages
- Exception Handling : Errors vs Exceptions, Try, Except, Finally Blocks, Custom Exceptions
Python Libraries for Data Analysis
- NumPy : NumPy Arrays, Array Creation Methods, Array Indexing and Slicing, Mathematical Operations on Arrays, Broadcasting Concepts
- Pandas : Pandas Series and DataFrames, Importing Data from CSV and Excel Files, Data Cleaning Techniques, Handling Missing Values, Data Aggregation and GroupBy Operations, Data Filtering and Sorting
Data Visualization
- Introduction to Data Visualization : Importance of Data Visualization, Data Visualization Best Practices, Types of Charts and Graphs
- Matplotlib : Line Charts, Bar Charts, Scatter Plots, Histograms, Plot Customization Techniques
- Seaborn : Statistical Data Visualization, Heatmaps, Distribution Plots, Pair Plots, Correlation Visualization
Statistics for Data Science
- Descriptive Statistics : Mean, Median, Mode, Variance, Standard Deviation
- Probability : Probability Basics, Probability Distributions, Conditional Probability, Bayes Theorem
- Statistical Analysis : Hypothesis Testing, Confidence Intervals, Correlation and Covariance
SQL for Data Analytics
- Database Fundamentals : Introduction to Databases, Relational Database Concepts, Tables, Rows and Columns, Primary Keys and Foreign Keys
- SQL Queries : SELECT Queries, Filtering Data using WHERE Clause, Sorting using ORDER BY, Aggregate Functions – COUNT, SUM, AVG
- Advanced SQL : Joins – Inner Join, Left Join, Right Join, Group By Clause, Subqueries, Data Aggregation for Analysis
Machine Learning Fundamentals
- Introduction to Machine Learning : Machine Learning Workflow, Data Preprocessing Techniques, Feature Selection, Training and Testing Models
- Supervised Learning : Linear Regression, Logistic Regression, Decision Trees, Random Forest Algorithms :
- Unsupervised Learning : Clustering Concepts, K-Means Clustering, Hierarchical Clustering
- Model Evaluation : Accuracy, Precision, Recall, F1 Score, Cross Validation Techniques
Business Intelligence & Dashboard
- Business Analytics : Business Intelligence Concepts, Data Interpretation Techniques, KPI Analysis, Data Driven Decision Making
- Dashboard Tools : Introduction to Power BI, Data Import and Transformation, Creating Interactive Dashboards, Data Visualization for Business Insights
AI Tools for Data Analytics
- AI Assisted Data Analysis : AI Tools for Data Cleaning, AI Assisted Data Visualization, Automated Data Analysis
- Prompt Engineering : Writing Effective Prompts, Using AI Tools for Data Insights, AI Automation for Data Reporting
Version Control & Collaboration
- Git & GitHub : Introduction to Git, Version Control Concepts, GitHub Repository Management, Collaboration Workflow for Data Science Projects.
Industry Projects
Data Analysis Project using Real Datasets, Machine Learning Model Development Project, Business Data Analytics Project, AI Assisted Data Analysis Project, Final Capstone Project.
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