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Certified Data Analyst Professional - Power BI

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Certified Data Analyst Professional - Power BI

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Certified Data Analyst Professional Certification  - PowerBI,  R, Python, SQL, Excel and Core Skills

The Certified Data Analyst Professional (CDAP) certification offered by Analytics Institute is a comprehensive program designed to validate the skills and knowledge required to excel in the field of data analysis. This certification equips professionals with the expertise to collect, analyze, and interpret complex data sets to uncover valuable insights and drive data-informed decision-making within organizations.

 

Benefits of Certified Data Analyst Professional Certification:

 

  1. Advanced Data Analysis Skills: The CDAP certification program is designed to enhance your data analysis skills, enabling you to confidently navigate through vast amounts of data and derive meaningful insights. You will gain expertise in various analytical techniques and tools, empowering you to make data-driven recommendations and solve complex business problems.

  2. Industry Recognition: By earning the CDAP certification, you establish yourself as a highly skilled and knowledgeable data analyst. This industry recognition enhances your professional credibility and distinguishes you as a sought-after expert in the field, increasing your career prospects and earning potential.

  3. Efficient Decision-Making: As a certified data analyst professional, you develop the ability to make informed decisions based on data-driven insights. You learn how to identify trends, patterns, and correlations within data, enabling you to provide valuable recommendations that drive organizational success.

 

Topics Covered in this Certification Exam:

Topics Covered in the Certified Data Analyst Professional Exam: The CDAP certification exam covers a wide range of topics related to data analysis, ensuring a comprehensive understanding of key concepts and techniques. The exam tests your knowledge and proficiency in the following areas:

Prepare to demonstrate your expertise in fundamental data analysis and database concepts with this certification. The exam covers a range of topics including data analysis methodologies, database fundamentals, and proficiency in Excel, SQL, R, Python and Power BI.

 

  • Format: Multiple Choice
  • Duration: 60 minutes
  • Number of Questions: 55
  • Passing Score: 80%

 

Exam Topics

1. Data Analysis Fundamentals

  • Data Analysis Process and Methodology
  • Types of Data Analysis: Descriptive, Diagnostic, Predictive, Prescriptive
  • Data Visualization Techniques
  • Role and Responsibilities of a Data Analyst
  • Key Skills and Tools for Data Analysts

 

2. Database Fundamentals

  • Database Concepts
  • Database Architecture
  • Database Security
  • Data Cleaning and Preparation
  • Data Collection Methods
  • Data Modeling
  • Data Integrity
  • Database design patterns

 

3. Excel for Data Analysis

  • Basic Excel functions
  • Data validation and data protection
  • Charts and graph creation
  • Conditional formatting
  • Pivot Tables and Pivot Charts
  • Cell formatting and styles
  • Data sorting and filtering
  • Data analysis and visualization
  • Text functions
  • Data de-duplication
  • Grouping and outlining data
  • Freeze panes and worksheet protection
  • Keyboard shortcuts
  • Data trends and time analysis

 

4. SQL for Data Analysis

  • Normalization and De-normalization
  • SQL Keywords and Basic Operations
  • SQL Clauses
  • SQL Operators
  • Aggregate Functions
  • Joins
  • Indexes
  • Database Constraints
  • Transactions
  • Temporary Tables
  • Views
  • Subqueries
  •  SQL Data Types
  • SQL Functions

 

5. Power BI for Data Analysis

  • Power BI Desktop vs. Power BI Service
  • Building Blocks of Power BI
  • Applications and Advantages of Power BI - Use cases for business and data analysts
  • Data Modeling Features
  • Data Cleansing and Transformation
  • Data Visualization benefits and challenges
  • Creating Interactive Dashboards
  • DAX (Data Analysis Expressions)
  • Performance Optimization
  • Power BI Slicers
  • Grouping in Power BI
  • Power Query
  • Power Pivot
  • Mapping in Power BI
  • Handling Large Datasets in Power BI
  • Integration with Microsoft Excel
  • Advanced Power BI Features for Data Connectivity, Security, and Report Optimization - Drill through, Direct Query, Data flows, Gateways, Running totals, Workspace collaboration, Report navigation, Row-level security (RLS), and Data refresh schedules

 

6. Data Warehousing Concepts

  • Purpose and Benefits to Data Warehousing
  • ETL (Extract, Transform, Load) Processes
  • Data Warehouse Architecture- Star Schema, Snowflake Schema
  • Data Modeling for Data Warehouses - Dimensional Modeling, Fact and Dimension Tables
  • Data Governance and Privacy

 

7. R Language

  • Data Reading and Writing: Reading CSV Files, Data Export
  • Data Manipulation and Transformation: dplyr Package, Filtering and Aggregation, Data Transformation, Data Reshaping, Joining and Merging Data Frames, Handling Large Datasets
  • Data Visualization: ggplot2 Package, Data Visualization Techniques
  • Statistical Analysis and Model Building: Statistical Analysis, Model Building
  • Data Cleaning and Handling: Data Cleaning, String Manipulation, Working with Dates and Times
  • Functions and Programming: apply() Function, Function Writing
  • Advanced Topics: Parallel Computing, R Markdown
  • Specialized Data Techniques: Time Series Analysis

 

8. Python

  • Data Manipulation and Analysis: Pandas Library, Reading CSV Files, DataFrame Operations, Data Cleaning and Transformation, Advanced DataFrame Merging, Data Aggregation and Grouping, Handling Missing Data, Data Visualization with Pandas, Time Series Analysis
  • Python Programming Concepts: Lambda Functions, String Methods, Class Initialization, Exception Handling, Date and Time Handling, Python Data Structures, Object-Oriented Programming in Python, Decorators and Higher-Order Functions, Regular Expressions
  • File Operations and Performance: File Operations, File I/O Operations, Performance Optimization
  • Testing and Libraries: Unit Testing in Python, Python Libraries for Data Analysis
  • Advanced Topics: DataFrame Indexing and Selection

 

By successfully passing the CDAP certification exam, you demonstrate your proficiency in these areas, positioning yourself as a skilled data analyst capable of extracting actionable insights and driving data-centric decision-making within organizations.

 

Note: The specific topics mentioned above are for illustrative purposes. The actual topics covered in the Certified Data Analyst Professional exam may vary based on the curriculum and focus of your analytics institute.

 

Passing this exam is required to earn the certification. 


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