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We're here to help you find itWelcome 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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Classroom Training price is on request
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♱ Excluding VAT/GST
Classroom Training price is on request
You can request classroom training in any city on any date by Requesting More Information
Prerequisites for the Certified Associate Data Analyst with Python (PCAD) Course:
To ensure you get the most out of our Certified Associate Data Analyst with Python course, we recommend that participants have the following minimum knowledge and skills:
These prerequisites are designed to ensure that learners can easily follow along with the course material and gain maximum benefit from the training program. If you meet these criteria, you'll be well-prepared to embark on your journey to becoming a 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.
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,
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.
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,
