Course Overview

A Data Analyst course typically provides individuals with the skills and knowledge required to analyze and interpret complex data sets. These courses are designed to equip participants with the tools and techniques necessary to extract meaningful insights from data, helping organizations make informed decisions. Here’s a general overview of what you might expect from a Data Analyst course:

1. Introduction to Data Analysis:
   • Overview of data analysis concepts and its importance in decision-making.
   • Introduction to key terms and foundational principles in data analysis.
2. Data Collection and Cleaning:
   • Techniques for collecting data from various sources.
   • Data cleaning and preprocessing to ensure data quality.
3. Exploratory Data Analysis (EDA):
   • Visualization techniques to understand data distribution and patterns.
   • Statistical methods to explore relationships between variables.
4. Statistical Analysis:
   • Introduction to basic statistical concepts.
   • Application of statistical tests for hypothesis testing and inference.
5. Data Visualization:
   • Tools and techniques for creating effective data visualizations.
   • Communicating insights through charts, graphs, and dashboards.
6. Database Management and SQL:
   • Basics of database management systems.
   • Structured Query Language (SQL) for querying and manipulating data.
7. Programming Languages:
   • Introduction to programming languages commonly used in data analysis (e.g., Python, R).
   • Coding skills for data manipulation and analysis.
8. Machine Learning Basics:
   • Overview of machine learning concepts relevant to data analysis.
   • Introduction to commonly used algorithms for predictive modeling.
9. Data Ethics and Privacy:
   • Understanding the ethical considerations in handling and analyzing data.
   • Ensuring privacy and compliance with data protection regulations.
10. Real-world Applications:
    • Hands-on projects and case studies to apply learned skills to real-world scenarios.
    • Collaboration with industry professionals or exposure to real business datasets.
11. Communication Skills:
    • Developing effective communication skills to convey findings to non-technical stakeholders.
    • Creating reports and presentations.
12. Job Preparation:
    • Resume building and interview preparation.
    • Guidance on job search and networking in the data analytics field.

It’s essential to choose a course that aligns with your career goals and interests in the field of data analysis.

You will learn:

1. Introduction to Data Analytics
2. Excel Basics for Data Analysis
3.Data Visualization and Dashboards with Excel and Cognos
4.Python for Data Science, AI & Development
5. Python Project for Data Science
6. Databases and SQL for Data Science with Python
7. Databases and SQL for Data Science with Python
8. Data Visualization with Python
9. IBM Data Analyst Capstone Project
10.10. Certification from IBM and Microsoft