Data_Science_Course_Slide_2_Introduction_to_Data_Science


COURSE INSTRUCTOR

Data_Science_Instructor_Sandip_Gadekar

Sandip Gadekar

Data Science Instructor
★★★★★ (4.9)

Sandip Gadekar boasts over 5 years of experience in data science, specializing in Python, R, SQL, Machine Learning, Deep Learning, and Data Visualization. With a strong foundation in data analysis and modeling, he excels at deriving insights from complex datasets. His expertise includes building predictive models and utilizing statistical techniques to solve real-world problems. Passionate about teaching, he focuses on hands-on learning and real-world projects to equip students with practical skills.

BASIC INFORMATION


  • Length : 6 Months
  • Level : Basic to Advanced
  • Category : Technology
  • Class Mode : Online/Offline
  • Started : 1st / 15th Every Month
  • Daily Time : 10:00 AM to 4:00 PM
  • Days : Mon - Fri
  • Class Strength: 10 / Per Batch


Course Description

Data Science is a rapidly growing field that helps businesses transform data into valuable insights and smarter decisions. This course provides a strong foundation in analytics, statistics, and data-driven problem-solving.

You will gain hands-on experience with Python, SQL, Machine Learning, Deep Learning, Data Visualization, and Artificial Intelligence. The curriculum focuses on practical learning through real-world datasets, predictive analytics, data processing, and business intelligence projects that reflect current industry requirements.

Gain experience through projects, case studies, and internship opportunities to build a strong portfolio and become job-ready for a successful career in Data Science.

Course Syllabus

Module 1: Introduction to Data Science

  1. Introduction to Data Science
  2. Understanding the Data Science Lifecycle
  3. Role of Data Science in Modern Business
  4. Types of Data and Data Sources
  5. Data Collection and Data Preparation
  6. Statistics and Mathematics for Data Science
  7. Introduction to Python for Data Science
  8. Data Science Tools and Technologies Overview
  9. Business Intelligence and Data-Driven Decision Making
  10. Introduction to Artificial Intelligence and Machine Learning
  11. Applications of Data Science Across Industries
  12. Data Ethics and Governance
  13. Latest Trends in Data Science and AI
  14. Career Opportunities in Data Science
  15. Mini Project: Data Science Case Study
  1. Introduction to Python Programming
  2. Python Installation and Environment Setup
  3. Variables, Data Types, and Operators
  4. Control Statements and Looping Structures
  5. Functions and Modules
  6. Strings, Lists, Tuples, Sets, and Dictionaries
  7. Object-Oriented Programming (OOP) Concepts
  8. Exception Handling and File Operations
  9. Working with NumPy for Numerical Computing
  10. Data Manipulation using Pandas
  11. Data Cleaning and Data Transformation
  12. Exploratory Data Analysis (EDA)
  13. Data Visualization with Matplotlib and Seaborn
  14. Python for Data Science Applications
  15. Mini Project: Data Analysis using Python
  1. Introduction to Database Management Systems (DBMS)
  2. Understanding Relational Databases
  3. Database Design and Normalization
  4. Introduction to SQL
  5. Creating Databases and Tables
  6. SQL Queries (SELECT, INSERT, UPDATE, DELETE)
  7. Filtering, Sorting, and Grouping Data
  8. SQL Functions and Operators
  9. Joins (Inner, Left, Right, Full)
  10. Subqueries and Nested Queries
  11. Views, Indexes, and Stored Procedures
  12. Database Security and Backup Management
  13. SQL for Data Analysis and Reporting
  14. Connecting SQL with Python
  15. Mini Project: Database Design and Analytics
  1. Introduction to Data Analysis
  2. Data Collection and Data Preparation
  3. Data Cleaning and Data Transformation
  4. Exploratory Data Analysis (EDA)
  5. Data Wrangling Techniques
  6. Working with NumPy and Pandas
  7. Data Aggregation and Grouping
  8. Data Visualization with Matplotlib and Seaborn
  9. Identifying Trends and Patterns
  10. Business Data Analysis Techniques
  11. Data Interpretation and Reporting
  12. Introduction to Predictive Analytics
  13. Real-World Data Analysis Case Studies
  14. Data Analysis using Python Projects
  15. Mini Project: End-to-End Data Analysis Project
  1. Introduction to Data Visualization
  2. Importance of Data Visualization in Data Science
  3. Data Visualization Principles and Best Practices
  4. Visualizing Data with Matplotlib
  5. Advanced Visualization using Seaborn
  6. Creating Charts, Graphs, and Plots
  7. Bar Charts, Line Charts, and Pie Charts
  8. Histograms, Box Plots, and Scatter Plots
  9. Interactive Visualizations with Plotly
  10. Dashboard Development using Power BI
  11. Data Storytelling and Insight Presentation
  12. Business Intelligence and Reporting
  13. KPI and Performance Dashboard Creation
  14. Real-World Data Visualization Case Studies
  15. Mini Project: Interactive Data Visualization Dashboard
  1. Introduction to Machine Learning
  2. Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
  3. Machine Learning Workflow
  4. Data Preprocessing and Feature Engineering
  5. Training and Testing Datasets
  6. Regression Algorithms
  7. Classification Algorithms
  8. Clustering Techniques
  9. Model Evaluation and Performance Metrics
  10. Overfitting and Underfitting
  11. Feature Selection and Optimization
  12. Machine Learning with Scikit-Learn
  13. Predictive Analytics and Forecasting
  14. Real-World Machine Learning Applications
  15. Mini Project: Machine Learning Prediction Model
  1. Introduction to Artificial Intelligence (AI)
  2. Introduction to Generative AI
  3. AI vs Machine Learning vs Deep Learning
  4. Prompt Engineering Fundamentals
  5. Working with ChatGPT and AI Assistants
  6. Large Language Models (LLMs) Overview
  7. Generative AI for Data Science Applications
  8. AI-Powered Data Analysis and Automation
  9. Natural Language Processing (NLP) Basics
  10. AI-Based Image and Content Generation
  11. Building AI-Powered Chatbots
  12. Introduction to OpenAI APIs and AI Integration
  13. Ethical AI and Responsible AI Practices
  14. Real-World AI and Generative AI Use Cases
  15. Mini Project: AI-Powered Data Science Solution

Real-World Student Projects


Explore the outstanding student projects and practical work, showcasing their skills and creativity in real-world scenarios. Discover how our students apply their knowledge through innovative and hands-on experiences.

Personalized_Data_Analytics_Course_Guidance

One-To-One Data Analytics Course Guidance

Our 1-to-1 guidance for the Data Analytics course offers a highly personalized learning experience with a 100% placement guarantee. Tailored to your individual needs, this course provides expert instruction on data cleaning, visualization, and statistical analysis. Benefit from hands-on projects that apply real-world data to solve practical problems. Our mentors offer targeted feedback and support, while our dedicated placement team helps you navigate job applications, optimize your resume, and prepare for interviews. With this comprehensive approach, you’ll develop the skills and confidence needed to excel in data analytics and secure a position in the competitive job market.

Customized Curriculum: Learning plan designed to align with your experience level and career goals. Direct access to seasoned data analysts for personalized guidance.

  • Personalized Curriculum
  • Expert Mentorship
  • Practical Projects
  • Skill Development
  • Portfolio Creation
  • Placement Support
Sandip_Gadekar_Founder_and_Director

Sandip Gadekar

Founder and Director

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