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USD30000
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Duration: 2 Months
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Delivery mode: Online
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Group size: 5
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Instruction language: English, Urdu
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Certificate provided:
Yes
Course Title: Introduction to Data Science in Python
Course Duration: 2 Months
Weekly Time Commitment: 5 hours
Course Description:
The Introduction to Data Science in Python course is a comprehensive and immersive program designed to provide participants with a strong foundation in data science using the Python programming language. Spanning two months, this course allows ample time for in-depth learning, practical exercises, and hands-on projects, ensuring a thorough understanding of the subject matter.
Throughout the course, participants will delve into the core principles, techniques, and tools used in data science, focusing on real-world applications and practical problem-solving. The curriculum encompasses a wide range of topics, including data manipulation, data visualization, exploratory data analysis, statistical analysis, machine learning, and more.
The course will cover the following topics:
Month 1:
Introduction to Data Science: Overview of the data science field, its methodologies, and the role of data scientists. Participants will gain an understanding of the data science process and its applications in various industries.
Python Essentials for Data Science: Review of essential Python programming concepts and syntax relevant to data science. Participants will reinforce their knowledge of variables, control structures, functions, and file handling in Python.
Data Manipulation with Pandas: In-depth exploration of the Pandas library for data manipulation and analysis. Participants will learn advanced techniques for data cleaning, transformation, merging, and reshaping.
Data Visualization with Matplotlib and Seaborn: Comprehensive study of data visualization using the Matplotlib and Seaborn libraries. Participants will master the creation of impactful visualizations, including line plots, scatter plots, bar plots, histograms, and heatmaps.
Month 2:
5. Exploratory Data Analysis: Techniques for exploratory data analysis, including descriptive statistics, data summarization, and visual exploration. Participants will learn how to uncover patterns, identify outliers, and gain insights from datasets.
Statistical Analysis with NumPy and SciPy: Introduction to statistical analysis using the NumPy and SciPy libraries. Participants will study concepts such as hypothesis testing, confidence intervals, and regression analysis.
Machine Learning Fundamentals: Introduction to machine learning algorithms and concepts. Participants will learn about supervised and unsupervised learning, model evaluation, feature engineering, and the application of common algorithms.
Applied Machine Learning: Practical implementation of machine learning algorithms using scikit-learn. Participants will gain hands-on experience in training, evaluating, and fine-tuning models for various tasks, such as classification and regression.
By the end of the course, participants will have a comprehensive understanding of data science principles and be equipped with the necessary skills to apply Python-based data analysis techniques. They will have engaged in a variety of coding exercises, projects, and real-world scenarios to solidify their knowledge and develop practical expertise.
Prerequisites: Basic programming knowledge, preferably in Python. No prior experience in data science is required.
Note: This course requires a commitment of 5 hours per week, including attending lectures, completing assignments, and engaging in coding exercises. Regular participation and practice are essential for successful completion of the course.