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Professional Data Analyst Certification (PDAC)

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  • Professional Data Analyst Certification (PDAC)
Professional Data Analyst Certification (PDAC)
  • Fee: 4500
  • Timing: 2.5 Hours Per Day
  • Duration: 6 Months

COURSE DETAILS

The PDAC (Professional Data Analyst Certification) offered at NIMACT is having the latest curriculum related to Data Analysis and Business Intelligence at the professional certification level, with the view to preparing data professionals for catering to the IT, Banking, Retail, Education and Corporate sector in India. The curriculum and the study materials are revised and updated every six months for the inclusion of the latest tools, techniques and AI trends in the Data Industry. Accordingly assistance and advice are sought on a continuing basis from the industry for knowing and assessing their requirements.

PDAC is the best career course for those who want to move from ordinary computer work to professional data work. Today every organization keeps data, but the real value is created by the person who can clean that data, analyse it, and present it in a clear report or dashboard. This course teaches that complete process from start to end, so it is a good option for students and working people who want to build a strong and future ready career.

There are many jobs after this course. Companies, banks, hospitals, schools, retail chains and small firms recruit candidates who hold a Data Analyst certification, and the job profile will be Data Analyst, MIS Executive, Reporting Executive, Excel and Power BI Specialist, Business Analyst Assistant, Data Entry and Data Management Executive, Dashboard Developer etc. Freelance data and dashboard projects are also a strong income option after this course.

Who Should Attend: People who are involved in Office Administration, MIS Work, Accounts, Sales Reporting, Record Keeping and Report Preparation such as Charts, Dashboards, Summaries etc. and are keen to advance their career. Students and computer literates who have decided to make a career in Data Analysis, Business Intelligence or Data Science, as well.

ELIGIBILITY CRITERIA: Students who have passed 10th standard with basic computer knowledge are eligible to pursue this course.

Mode: Hybrid (Offline + Online + Live Doubt Session)

SYLLABUS:
To get a better idea about the course structure, let us go through a list of important subjects present in this program. Note - Only the important points have been mentioned.

  • Data Concept, ICT & AI
  • Artificial Intelligence (AI) for Data Work - ChatGPT, Claude AI, Gemini, Copilot, Grok, DeepSeek, Perplexity
  • Introduction to Data Analytics
  • Data Types, Data Sources and Data Formats
  • Data Life Cycle - Collect, Clean, Analyse, Visualize, Report
  • Types of Analytics - Descriptive, Diagnostic, Predictive, Prescriptive
  • Role and Responsibility of a Data Analyst
  • Basics of Statistics for Data Analysis
  • Mean, Median, Mode, Range and Standard Deviation
  • Frequency Distribution and Percentage Analysis
  • Correlation, Trend and Forecasting Concept
  • Sampling and Data Accuracy
  • Microsoft Excel for Data Analysis
  • Data Entry Rules, Data Validation and Data Protection
  • Sorting, Filtering, Conditional Formatting
  • Text, Date, Logical and Statistical Functions
  • Lookup Functions - VLOOKUP, HLOOKUP, XLOOKUP, INDEX, MATCH
  • LAMBDA, LET and Dynamic Array Functions
  • Pivot Table, Pivot Chart and Slicer
  • What If Analysis, Goal Seek and Solver
  • Power Query - Data Import, Transform and Merge
  • Power Pivot and Data Modelling
  • DAX (Data Analysis Expression)
  • Excel Dashboard Design
  • Project
  • Database, RDBMS and SQL
  • Data Modelling Concept, Primary Key and Foreign Key
  • MySQL / PostgreSQL Environment
  • DDL, DML, DQL Commands
  • SELECT, WHERE, ORDER BY, GROUP BY, HAVING
  • JOINS - Inner, Left, Right, Full
  • Subquery, View and Aggregate Functions
  • Window Functions and CTE
  • Data Extraction for Reporting
  • Project
  • Python for Data Analysis
  • Python Basics - Variable, Data Type, Loop, Function
  • NumPy - Array and Numeric Operations
  • Pandas - Series, DataFrame, Data Cleaning, Merge and Groupby
  • Matplotlib and Seaborn - Data Visualization
  • Working with CSV, Excel and Database Files
  • Project
  • Power BI
  • Data Import and Data Connection
  • Power Query Editor and Data Shaping
  • Data Model, Relationship and Star Schema
  • DAX Measures and Calculated Columns
  • Visuals, Filters, Bookmarks and Drill Through
  • Interactive Dashboard Design
  • Report Publishing, Sharing and Refresh
  • Power BI Service and Workspace
  • Data Visualization Principles
  • Chart Selection and Colour Rules
  • Storytelling with Data
  • KPI and Metrics Design
  • MIS Reporting
  • Daily, Weekly and Monthly MIS Format
  • Sales, Stock, Attendance and Financial Reports
  • Automated Report Preparation
  • Google Sheets and Looker Studio
  • Google Apps (Sheet, Docs, Slide, Drive, Form)
  • Google Apps Script Automation Concept
  • Office 365, Microsoft Forms and OneDrive
  • Power Automate and SharePoint
  • VBA for Report Automation
  • Data Cleaning and Data Quality Management
  • Handling Missing, Duplicate and Wrong Data
  • Data Migration and Data Security
  • Cloud Computing and Cloud Data Basics
  • Business Analysis Concept
  • Case Study on Real Data Sets
  • AI Tools for Data Cleaning, Formula Building and Insight Writing
  • Emerging Technologies in Data Science
  • Resume, Portfolio and Interview Preparation
  • Project Work
  • Internship