Duration 4 days -28 hrs.
Overview
In today’s business landscape, data stands as a cornerstone for informed decision-making. Yet, the processes of consuming, generating, and distilling insights from data are often overlooked. Our Data Analytics and Visualization Mastery program address this critical gap, aiming to equip participants with essential skills to transform raw data into comprehensible insights.
By the end of the program, participants will be adept at navigating the data landscape, molding and modeling data effectively, and creating impactful visualizations. This mastery will empower them to contribute significantly to their organizations’ data-driven decision-making processes.
Don’t miss this opportunity to unlock the true potential of your data. Join us on a journey to master the art and science of Data Analytics and Visualization.
Objectives
- Understand How we Acquire the Data?
- Know, how to determine the usability of the data.
- Learn Industry Best-Practices and Standard when dealing with data.
- Learn how to tell the Story
Audience
- Data Analysts: Professionals responsible for collecting, processing, and analyzing data to help organizations make informed decisions.
- Business Analysts: Individuals who analyze business processes and data to provide insights into improving business performance.
- Data Scientists: Professionals who use statistical methods and machine learning techniques to extract valuable insights from data.
- Data Engineers: Individuals involved in designing and maintaining the infrastructure for collecting and storing data.
- IT Professionals: Those responsible for managing the technical aspects of data analytics, including data storage, processing, and security.
- Business Intelligence (BI) Professionals: Individuals working with BI tools to create reports and dashboards for data-driven decision-making.
- Managers and Executives: Decision-makers who want to understand how to leverage data analytics and visualization to drive business strategies.
- Researchers and Academics: Scholars and researchers interested in incorporating data analytics and visualization techniques into their research.
- Marketing and Sales Professionals: Individuals seeking to use data analytics to optimize marketing campaigns, understand customer behavior, and improve sales strategies.
- Finance Professionals: Those in finance roles interested in using data analytics to analyze financial data and support financial decision-making.
- Healthcare Professionals: Individuals in healthcare looking to use data analytics for patient care, operational efficiency, and health outcomes analysis.
- Consultants: Professionals offering consulting services in the areas of data analytics and visualization.
- Entrepreneurs and Startups: Individuals looking to apply data analytics and visualization techniques to gain insights for business development and growth.
- Students: Those studying data science, analytics, or related fields who want to build practical skills in data analytics and visualization.
- Anyone Interested in Data Analysis: Individuals with a general interest in data analysis and visualization, even if they are not currently working in a data-related role.
Pre- requisites
- Proficiency in using a computer, including file management and basic software navigation.
- Familiarity with common operating systems (e.g., Windows, macOS, Linux).
- Basic understanding of arithmetic operations.
- Familiarity with concepts like percentages, ratios, and averages.
- Basic knowledge of statistical concepts such as mean, median, and standard deviation is recommended but not mandatory.
- Familiarity with spreadsheet software, particularly Microsoft Excel or Google Sheets.
- Ability to perform basic tasks such as data entry, formula creation, and simple data analysis.
- An eagerness to explore and analyze data with a curiosity-driven mindset.
- Willingness to think critically and draw insights from data.
Course Content
Day 1
- Introduction to Data Analysis (Data Preparation)
- Data Source Checklist
- Acquiring the Data
- Data Cleansing
- Common Cleansing Actions
Day 2
- Exploring the data
- Picking the right presentation format
- Data Type Presentation (Discrete vs. Continuous)
Day 3
- Story Telling
- Applying it to a project
Day 4
- Presenting the Result
