Global Nexus Institute Ltd

Professional Data Analytics

The Data Analytics Program equips learners with vital skills in Excel, Python, SQL, PowerBI, Generative AI, and Version Control. Participants ... Show more
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Professional Data Analytics Course

Data Analytics & Career Path

Embark on a journey into the world of data analytics, a rapidly growing field essential for data-driven decision-making across industries. This section introduces the fundamentals of data analytics, including key concepts, industry applications, and the diverse career opportunities available. Learners will explore roles such as Data Analyst, Business Intelligence Analyst, Data Scientist, and Data Engineer. Understanding the career landscape helps students identify their interests and set achievable goals. Additionally, this module emphasizes the importance of continuous learning and staying updated with the latest tools and techniques to thrive in the evolving data domain.

Excel for Data Analysis

Excel remains a foundational tool in data analysis. This module covers essential features such as formulas, functions, and data manipulation techniques. Participants will learn how to create and customize pivot tables, perform data cleaning, and develop interactive dashboards. Practical exercises include analyzing sales data, generating reports. Mastery of Excel enables learners to efficiently handle small to medium datasets and prepares them for more advanced analytical tools.

SQL for Data Management

SQL (Structured Query Language) is crucial for managing and querying large datasets stored in databases. This section introduces SQL syntax, including SELECT statements, joins, filters, and aggregations. Learners will practice writing queries to extract insights, update records, and optimize database performance. The ability to work with SQL empowers analysts to handle structured data efficiently and lays the groundwork for integrating data from multiple sources for comprehensive analysis.

Python For Data Analysis

Python offers powerful libraries for data manipulation and visualization. This module focuses on using Pandas for data cleaning, transformation, and analysis, alongside Matplotlib for creating insightful visualizations. Participants will learn how to import datasets, perform statistical analysis, and generate charts that communicate findings effectively. Additionally, this section covers scripting techniques to automate repetitive tasks and build reusable code, enhancing productivity and scalability in data projects.

Power BI for Dynamic Reporting

Power BI is a leading business intelligence tool for creating interactive and shareable reports. This module guides learners through data import, report design, and dashboard development. Participants will learn how to connect various data sources, apply filters, and visualize data through charts, maps, and slicers. The practical exercises include building executive dashboards that enable stakeholders to make informed decisions swiftly. Power BI skills are essential for presenting complex data insights in an accessible format.

Generative AI in Data Analytics

Generative AI introduces innovative approaches to automate analysis and generate insights. This section covers AI-powered tools and techniques, including natural language processing (NLP) and automated report generation. Learners will explore how AI can assist in data summarization, pattern recognition, and predictive modeling. Understanding AI’s applications in analytics enhances the ability to leverage emerging technologies for smarter decision-making and operational efficiency.

Version Control for Collaboration

Effective collaboration in data projects requires version control systems like Git and platforms like GitHub. This module teaches learners how to track changes, manage code repositories, and collaborate seamlessly with team members. Participants will practice branching, committing, and merging code, ensuring a structured workflow. Mastery of version control promotes teamwork, reduces errors, and maintains project integrity in complex data analyses.

Career Preparation & Portfolio Building

Preparing for a successful career in data analytics involves building a compelling portfolio, honing interview skills, and understanding industry expectations. This section provides guidance on showcasing projects, certifications, and skills through online platforms like GitHub and LinkedIn. Learners will receive tips on resume writing, interview preparation, and networking strategies. By the end of this module, participants will be confident in presenting their expertise to potential employers and demonstrating their readiness for data-driven roles.

Basics of MySQL Database
Intermediate MySQL
What skills will I gain from the Professional Data Analytics program?
You will learn data entry, analysis, visualization, automation, and collaboration skills using tools like Excel, Python, SQL, PowerBI, Generative AI, and Git/GitHub.
Who is the ideal participant for this program?
The program is suitable for aspiring data analysts, business professionals, and students seeking to develop comprehensive data skills for various industries.
Will I be able to work with large datasets after completing the program?
Yes, you will learn techniques to handle, analyze, and visualize large datasets efficiently, preparing you for real-world data challenges.
Does the program include career support and portfolio development?
Yes, it offers career preparation guidance and opportunities to build a strong portfolio showcasing your data analytics projects.
Pay in 4 installments (80000, 40000, 40000, 40000)
Course details
Duration 5 Months
Lectures 36
Video 24 Hours
Assignments 14
Level Intermediate
Basic info
  • Course Title: Professional Data Analytics
  • Duration: Self-paced with flexible learning schedule
  • Delivery Mode: Online, instructor-led modules with hands-on projects
  • Language: en_US
  • Certification: Certificate of Completion awarded upon finishing the program
Course requirements
  • Basic computer literacy and internet proficiency
  • Familiarity with basic mathematics and statistics concepts
  • Access to a computer with internet connectivity
  • No prior programming experience required, though beneficial
  • Motivation to learn data analysis and visualization skills
Intended audience
  • Aspiring data analysts and data scientists
  • Business professionals seeking to harness data insights
  • Students interested in data analytics careers
  • IT professionals looking to expand into data roles
  • Anyone interested in developing skills in data analysis tools and AI

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