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Data Analytics

Course Overview:

The Data Analytics course is designed to equip participants with the skills and knowledge needed to analyze, visualize, and interpret complex datasets. By the end of the course, learners will be proficient in leveraging data to make informed business decisions.

Learning Objectives:

  • Understand the fundamentals of data analytics and its role in decision-making.
  • Learn how to collect, clean, and preprocess data for analysis.
  • Explore key tools and technologies used in data analytics, such as Python, R, Excel, SQL, and Tableau.
  • Develop data visualization skills to communicate insights effectively.
  • Gain hands-on experience with real-world datasets and case studies.

Key Topics Covered:


Introduction to Data Analytics:
  • Importance of data in the modern world.
  • Types of data analytics: Descriptive, Diagnostic, Predictive, and Prescriptive.
Data Preparation and Cleaning:
  • Data collection methods.
  • Handling missing and inconsistent data.
  • Data transformation techniques.
Statistical Analysis:
  • Basics of statistics for data analytics.
  • Hypothesis testing and inferential statistics.
Data Visualization:
  • Best practices for visual storytelling.
  • Tools: Tableau, Power BI, and Matplotlib/Seaborn.
Programming for Data Analytics:
  • Python: Pandas, NumPy, and Scikit-learn.
  • R Programming for statistical computing.
Database Management and SQL:
  • Writing queries to extract data.
  • Working with relational databases.
Machine Learning Basics:
  • Introduction to predictive modeling.
  • Supervised vs. unsupervised learning.
Big Data and Cloud Analytics (Optional/Advanced):
  • Overview of big data technologies like Hadoop and Spark.
  • Cloud platforms like AWS, Azure, and Google Cloud for analytics.
Target Audience:
  • Business professionals and managers looking to leverage data in decision-making.
  • Recent graduates aiming for a career in data analytics.
  • IT professionals who want to transition into data-driven roles.
  • Anyone interested in understanding and applying data analytics concepts.
Prerequisites:
  • Basic knowledge of Microsoft Excel.
  • Familiarity with programming concepts is helpful but not required.
  • A willingness to work with numbers and datasets.
Course Format:
  • Duration: 8-12 weeks (flexible based on pace and depth of topics).
  • Mode: Online or In-person.
  • Assessment: Quizzes, assignments, and a capstone project.
Career Opportunities After the Course:
  • Data Analyst
  • Business Analyst
  • Marketing Analyst
  • Data Scientist (with additional skills in machine learning)
  • BI (Business Intelligence) Developer