Advanced Data Analysis Course – 6 Months
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Course Overview:
This Advanced Data Analysis Course is designed for students and corporate professionals looking to master the art of data-driven decision-making. Over the course of a 6 months period, youâ€™ll gain a deep understanding of data manipulation, statistical analysis, and visualization. Youâ€™ll learn to work with tools like Python, SQL, Excel, Power BI, Tableau, and cloud platforms such as AWS and Google Cloud, empowering you to tackle complex datasets and solve real-world problems.
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Course Structure-
The course features weekly interactive classes, followed by quizzes to reinforce your understanding of key concepts. Assignments based on real-world datasets will help you practice data cleaning, statistical analysis, and advanced visualization techniques. Youâ€™ll also participate in real-time problem-solving sessions, applying these techniques to live datasets. In addition, intensive bootcamps will be held, focusing on topics like machine learning, predictive analytics, and big data platforms.
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Career Benefits-
Upon completing this course, youâ€™ll receive aÂ globally recognized Data Analyst Certification**, making you eligible for data analysis roles across industries worldwide. Whether you’re a student looking to break into the field or a corporate employee seeking to enhance your data skills, this course will give you a competitive edge in todayâ€™s data-driven market.
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Equip yourself with in-demand skills and secure a future in data analysis.
The content covers key components of data science, applications across various industries, the data science process, required skills, and ethical considerations.
Mathematics for Data Science: Linear Algebra and Calculus
Statistics and Probability Theory
Programming Basics with Python
Data Science Tools and Environments (Jupyter, Anaconda)
1. Data Acquisition Techniques and APIs
Web Scraping and Data Extraction
Data Cleaning and Preprocessing
Exploratory Data Analysis (EDA)
Data Visualization with Matplotlib and Seaborn
1. Introduction to Machine Learning
Supervised Learning: Regression and Classification
Unsupervised Learning: Clustering and Dimensionality Reduction
Model Evaluation and Validation Techniques
Feature Engineering and Selection
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