Course Overview
Our Data Analytics course is tailored to equip students with essential skills in data collection, cleaning, and analysis. This 6-week program focuses on data-driven decision-making, using tools like Excel, SQL, and Python to perform statistical analysis, data visualization, and predictive modeling. By the end of the course, students will be able to manage large datasets and generate actionable insights for business improvement.
Program Modules
Introduction to Data Analytics
Overview of Data Analytics, Key Concepts, and Applications in Business and Industry.
Data Collection Methods
Understanding Various Data Sources, Survey Techniques, and Data Acquisition.
Data Cleaning and Preparation
Techniques for Cleaning Data, Handling Missing Values, and Data Transformation.
Exploratory Data Analysis
Data Visualization Techniques, Descriptive Statistics, and Insight Generation.
Predictive Analytics
Using Statistical Models and Machine Learning for Prediction and Decision Making.
Data Visualization Tools
Introduction to Tools such as Tableau, Power BI, and Data Storytelling Techniques.
Text and Sentiment Analysis
Techniques for Analyzing Text Data, Natural Language Processing Basics.
Big Data Analytics
Overview of Big Data Technologies, Hadoop, Spark, and Data Processing.
Capstone Project
Hands-on Project Applying Data Analytics Techniques to Real-World Problems.
Data Analytics in Business
Understanding the Role of Data Analytics in Business Strategies and Operations.
Data Ethics and Privacy
Best Practices for Data Usage, Ethical Considerations, and Regulatory Compliance.
Career Guidance
Resume Building, Interview Preparation, and Navigating the Job Market in Data Analytics.
Benefits of Data Analytics Course
- 📊 Master advanced analytics tools and techniques
- 🔍 Gain expertise in data visualization and storytelling
- 💡 Learn real-world business analytics applications
- 🌐 Hands-on experience with top data analytics software
- 👥 Connect with professionals in the analytics field
- 📈 Build a strong portfolio with industry-level projects
Career Paths in Data Analytics
Our training prepares you for various high-demand roles, including:
- 📊 Data Analyst
- 📈 Business Intelligence Analyst
- 🔍 Market Research Analyst
- 💻 Data Visualization Specialist
- 📱 Big Data Analyst
- 🎯 Data Consultant
Industry Placement Opportunities
Top companies in India are actively hiring data analytics professionals:
- Junior Data Analyst ₹5,00,000 - ₹8,00,000 per annum
- Senior Data Analyst ₹8,00,000 - ₹15,00,000 per annum
- Data Analytics Manager ₹15,00,000 - ₹30,00,000 per annum
Learning Activities & Projects
Business Analytics Project
Apply analytics techniques to solve real-world business problems using data-driven insights.
- Data visualization and dashboard creation
- Exploratory data analysis (EDA)
- Predictive analytics for business growth
Market Research Analysis
Conduct in-depth market research and analyze consumer behavior using analytics tools.
- Customer segmentation analysis
- Data interpretation for strategic planning
- Marketing campaign effectiveness assessment
Assessment Methods
Weekly Quizzes
Regular quizzes to test your knowledge on various data analytics concepts.
Hands-on Projects
Complete real-world projects and apply analytics techniques to solve business problems.
Final Capstone Project
Demonstrate your skills by working on a capstone project involving end-to-end data analysis.
Peer Reviews
Engage in peer reviews to provide and receive feedback on projects and assignments.
Sample Projects
Discover some of the key projects you will work on during the Data Science and Analytics course. These projects demonstrate various data analysis and visualization techniques, showcasing your ability to handle and interpret complex data sets.
A project focusing on segmenting customers based on purchasing behavior and demographic data using clustering algorithms and data visualization techniques.
Develop a predictive model to forecast future sales based on historical sales data and other relevant factors using time series analysis.
Conduct an in-depth exploratory data analysis on a real-world dataset, including data cleaning, visualization, and insights generation.
Build a model to detect fraudulent transactions and anomalies in financial data using machine learning algorithms.
Analyze social media data to determine the sentiment of public opinions and trends using natural language processing techniques.
Develop a system to analyze and visualize real-time data streams, providing actionable insights for decision-making.
Perform market basket analysis to understand customer purchasing patterns and identify frequently co-occurring items using association rule mining.
Analyze healthcare data to identify trends, predict patient outcomes, and improve healthcare services using statistical and machine learning methods.
Predict which customers are likely to stop using a service using classification techniques to improve retention strategies.