The Data Analyst Course: Complete Data Analyst Bootcamp
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Training TypeLive Training
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CategoryData & Analytics
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Duration4 Hours
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Rating4.8/5
Course Introduction
About the Course
In today's data-driven world, data analysis is a critical skill for making informed decisions. This course is designed to equip participants with the knowledge and tools required to start a career as a data analyst. Through a combination of theory and practical exercises, you'll learn how to collect, clean, and explore data, visualize insights, and draw meaningful conclusions.
Course Objective
Introduce participants to the role of a data analyst and the data analysis process.
Teach data collection and data cleaning techniques.
Explore data using descriptive statistics, distributions, and correlations.
Master data visualization tools like Matplotlib and Seaborn.
Familiarize participants with data analysis methods, including hypothesis testing, regression, and classification.
Enable participants to draw conclusions and make data-driven recommendations.
Who is the Target Audience?
Introduce participants to the role of a data analyst and the data analysis process.
Teach data collection and data cleaning techniques.
Explore data using descriptive statistics, distributions, and correlations.
Master data visualization tools like Matplotlib and Seaborn.
Familiarize participants with data analysis methods, including hypothesis testing, regression, and classification.
Enable participants to draw conclusions and make data-driven recommendations.
Basic Knowledge
No prior experience in data analysis is required, but having a basic understanding of mathematics and statistics will be beneficial. Participants should have a working knowledge of computer operations and be comfortable using a computer.
Available Batches
Pricing
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Welcome and Course Overview
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Understanding the Role of a Data Analyst
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Introduction to Data Analysis Process (Data Collection, Data Cleaning, Data Exploration, Data Visualization, and Interpr
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Data Collection Methods
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Data Cleaning Techniques (Handling Missing Data, Outliers, etc.)
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Practical Exercise: Data Collection and Cleaning
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Data Exploration Techniques (Descriptive Statistics, Distributions, Correlations)
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Data Visualization Tools and Techniques (Matplotlib, Seaborn, etc.)
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Practical Exercise: Data Exploration and Visualization
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Data Analysis Methods (Hypothesis Testing, Regression, Classification)
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Drawing Conclusions and Making Recommendations
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Resources, Further Learning, and Q&A