Course Description

Data Analyst/Analytics: A Complete Overview

This 150-hour Data Analyst Certification Training, delivered in collaboration with IBM, is an ideal program for freshers and working professionals to gain a thorough understanding of data analytics. The extensive curriculum covers MySQL, Excel (Basic & Advanced), Tableau, Power BI, Python (Pandas, Numpy, Seaborn)and more. By acquiring skills and working on assignments and projects based on industrial data, you will soon master data analytics best practices. Read through to catch a glimpse of what’s inside the course and why you should pursue it at Scope.

Data Analytics: Process That Boosts Businesses 

Today Data analytics is a set of practices that focuses on deriving actionable insights from the generated data and applying them to improve the productivity, efficiency, and revenue of a business.

Studies reveal that:

• Data-driven organizations are 19x more likely to be profitable.
• Analytics makes decision-making 5x faster for businesses.
• The average ROI for enterprises using business intelligence and analytics is 1300%

Now that’s worth billions of dollars to a company, and it clearly shows how crucial the role of a data analyst is for any organization! 

Our Data Analytics course teaches you how to analyze and interpret data to make informed decisions. The course typically covers the following topics:

Data Collection: Learning how to gather data from various sources, such as databases, online sources, or surveys.
Data Cleaning: Understanding how to clean and preprocess data, removing errors, duplicates, and inconsistencies to ensure accurate analysis.
Statistical Analysis: Gaining knowledge of statistical methods and techniques to identify trends, patterns, and relationships in the data.
Data Visualization: Learning how to present data in graphical formats like charts, graphs, and dashboards to make it easier to understand.
Tools and Software: Getting hands-on experience with tools and software used in data analytics, such as Excel, SQL, Python,(Pandas, Numpy, Seaborn, Matpoltlib & Plotly) and data visualization tools like Tableau or Power BI.
Machine Learning Basics: Some courses may include introductory topics in machine learning, where you learn to build predictive models using data.
Problem-Solving: Applying analytical techniques to solve real-world business problems and make data-driven decisions.
Communication: Developing skills to communicate your findings effectively to non-technical stakeholders.