Certified Associate Data Analyst with Python (PCAD) Course Overview

Certified Associate Data Analyst with Python (PCAD) Course Overview

Welcome to Koenig Solutions' Certified Associate Data Analyst with Python (PCAD) course! This comprehensive program is designed to equip you with strong data analytics skills using Python. You will learn essential techniques, including data ingestion, preparation, and cleaning. The course covers Exploratory Data Analysis (EDA), Statistical Analysis, and practical applications like time series analysis and machine learning. By the end, you'll be proficient in data manipulation, visualization, and can handle real-world data analysis projects. Whether you're aiming to enhance your current skills or pursue a career in data analytics, our course offers valuable insights and hands-on experience. Join us and become a certified expert!

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  • Live Training (Duration : 40 Hours)
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  • Classroom Training fee on request

♱ Excluding VAT/GST

You can request classroom training in any city on any date by Requesting More Information

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Course Advisor

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Simmi Anand

9+ Years Experience

I am Simmi Anand, a Microsoft Certified Trainer, and UiPath Certified Advanced RPA Developer with over 12 years of rich experience in corporate training and the IT sector. I have been working with Koenig Solutions for the past 8+ years, providing quality training on technologies related to Development, Automation and Testing using various Languages (e.g., Python, Java) and Tools e.g., Robotic Process Automation using UiPath and Automation Anywhere. In addition, I also deliver training on Microsoft and Google technologies, including Mobile Application Development for Android, iOS with Swift, Hybrid Mobile Application using PhoneGap, Microsoft SQL Server, Microsoft PowerApps & Flow, Power Platform and PostgreSQL to global and corporate clients.   One reason I enjoy this field and the challenges that come with it is the opportunity to connect with people and solve their IT needs. I am also passionate about learning technologies and gaining solid knowledge about them. I have conducted various onsite training assignments globally with esteemed clients such as Microsoft - India, KPMG, Raw Bank - DRC, FRCI - Mauritius, Google Train the Trainer Project, Infosys, TCS, Wipro, and many others. Associated with Koenig since February-2016.

Target Audience for Certified Associate Data Analyst with Python

Certified Associate Data Analyst with Python is an in-depth course designed for those seeking to master data analysis using Python, offering insights into data ingestion, preparation, statistical analysis, and machine learning.


  • Aspiring Data Analysts
  • Data Scientists
  • Business Analysts
  • IT Professionals
  • Software Developers
  • Statisticians
  • Market Researchers
  • Academic Researchers
  • Data Engineers
  • Machine Learning Engineers
  • Database Administrators
  • Students in Data Science or Analytics Programs
  • Professionals seeking a career pivot into data analytics
  • Financial Analysts
  • Operations Analysts


Learning Objectives - What you will Learn in this Certified Associate Data Analyst with Python?

Course Overview: The Certified Associate Data Analyst with Python (PCAD) course is designed to equip students with comprehensive data analysis skills using Python, covering key areas such as data ingestion, exploration, statistical analysis, machine learning, and big data processing.

Learning Objectives and Outcomes:

  • Understand the Data Analytics Process: Gain insights into the complete data analytics workflow, from data collection to analysis and visualization.

  • Master Data Exploration Techniques: Learn Exploratory Data Analysis (EDA) with both quantitative and graphical techniques to derive meaningful insights from data.

  • Data Preparation Skills: Develop the ability to clean, transform, normalize, and standardize data, handling outliers and missing values effectively for accurate analysis.

  • Statistical Analysis Proficiency: Gain competence in performing descriptive statistics, understanding data distributions, identifying patterns, and visualizing data using Matplotlib and Seaborn.

  • Data Manipulation Expertise: Acquire skills in data manipulation and aggregation with Pandas, including grouping, pivoting, and merging datasets.

  • Time Series Analysis: Understand the fundamentals of time series data, including indexing, slicing, visualization, and forecasting techniques like ARIMA.

  • Introduction to Machine Learning: Get acquainted with machine learning concepts, including supervised vs unsupervised learning,

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