Course Overview:
The Data Analytics course is crafted to provide learners with a deep understanding of how to collect, process, and interpret vast amounts of data to drive informed decision-making. The course covers the entire data lifecycle, from data collection and cleaning to advanced analytical techniques, visualizations, and reporting. Whether you’re new to analytics or looking to deepen your expertise, this course equips you with the skills needed to become a proficient Data Analyst and make data-driven recommendations to improve business outcomes.

Throughout the course, you’ll work on real-world datasets, apply statistical analysis techniques, and learn how to use industry-standard tools such as Python, SQL, Excel, Power BI, and Tableau to uncover meaningful insights.

Key Modules:

1. Introduction to Data Analytics
   • Overview of the Data Analyst role and its importance in modern business.
   • Key skills and competencies required in data analysis.
   • Introduction to different types of data (structured, unstructured, and semi-structured).
2. Data Collection and Cleaning
   • Data sourcing: Internal vs. external data sources.
   • Techniques for cleaning and preparing data for analysis.
   • Managing missing data, data validation, and data transformation.
3. Exploratory Data Analysis (EDA)
   • Understanding descriptive statistics and key metrics.
   • Analyzing trends, patterns, and correlations in data.
   • Hands-on with Python, R, and SQL for EDA.
4. Data Visualization Techniques
   • Fundamentals of data visualization and storytelling with data.
   • Creating insightful visualizations using tools like Power BI, Excel, and Tableau.
   • Best practices for communicating data insights to stakeholders.
5. Statistical Analysis and Hypothesis Testing
   • Introduction to inferential statistics, probability, and distributions.
   • Conducting hypothesis tests, A/B testing, and regression analysis.
   • Hands-on with statistical tools to solve real-world business problems.
6. Advanced Analytics & Predictive Modeling
   • Introduction to machine learning and predictive modeling techniques.
   • Using algorithms like regression, decision trees, and clustering for data-driven predictions.
   • Hands-on projects involving real-time prediction using Python, R, or other tools.
7. Big Data and Data Engineering Fundamentals
   • Understanding big data concepts and tools like Hadoop and Spark.
   • Introduction to cloud-based analytics platforms such as AWS and Azure.
   • Data storage, retrieval, and pipeline development techniques.

Learning Outcomes:

• Develop the skills to collect, clean, and analyze datasets from various sources.
• Gain proficiency in using tools such as Python, SQL, Excel, Power BI, and Tableau.
• Understand statistical methods and how to apply them in real-world decision-making.
• Learn how to build predictive models and present insights to key stakeholders.
• Become adept at visualizing and communicating complex data insights.
• Understand the basics of big data technologies and cloud analytics platforms.

Who Should Enroll:

• Aspiring Data Analysts.
• Professionals transitioning into data-driven roles.
• Business Analysts, Financial Analysts, and IT professionals looking to enhance their data skills.
• Graduates interested in pursuing a career in data analytics.

Career Opportunities:

• Data Analyst
• Business Intelligence Analyst
• Data Scientist (entry-level)
• Analytics Consultant
• Operations Analyst
• Financial Data Analyst

Duration:

• 8-12 weeks (Flexible schedules for working professionals)

Assessment & Certification:

• Practical assessments, hands-on projects, and quizzes.
• Certification upon course completion, recognized by top companies in data-driven industries