Python for Data Science
Learn how to use Python effectively for data analysis, visualization, and real-world machine learning applications.Preview Python for Data Science course
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Exploratory data analysis on large datasets
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Building predictive models with scikit-learn
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Creating dashboards and data visualizations
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Performing feature engineering and data cleaning
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Beginners in data science and analytics
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Python developers transitioning into data roles
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Students preparing for data science careers
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Professionals aiming to upskill in data analysis
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Freelancers and entrepreneurs handling data-heavy tasks
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Follow the sequence of modules for a structured experience
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Code along with examples for better retention
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Practice with the datasets provided in each project
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Take notes on libraries, methods, and error-handling techniques
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Participate in the discussion forums and ask for feedback
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Revisit key concepts and improve existing projects as you progress
Course/Topic 1 - Coming Soon
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The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.
By the end of the course, you will be able to:
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Work with Python for data manipulation and analysis
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Use libraries like Pandas and NumPy to manage structured data
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Perform data visualization using Matplotlib and Seaborn
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Apply machine learning techniques using Scikit-learn
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Conduct real-world data analysis projects
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Interpret model results and evaluate performance
Course Syllabus
Module 1: Introduction to Python and Data Science
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Python syntax and Jupyter Notebook setup
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Variables, loops, functions
Module 2: Working with NumPy
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Arrays and mathematical operations
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Broadcasting and reshaping
Module 3: Data Manipulation with Pandas
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DataFrames, Series, Indexing
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Filtering, merging, and groupby operations
Module 4: Data Cleaning
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Handling missing values
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Data type conversion
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Duplicates and outlier removal
Module 5: Data Visualization
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Basic plots using Matplotlib
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Statistical plots with Seaborn
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Interactive plots with Plotly
Module 6: Exploratory Data Analysis (EDA)
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Correlation analysis
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Trend detection
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Feature distribution
Module 7: Introduction to Machine Learning
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Supervised vs unsupervised learning
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Scikit-learn workflow
Module 8: Building ML Models
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Regression and classification
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Model training and testing
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Cross-validation
Module 9: Model Evaluation
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Accuracy, precision, recall, F1-score
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ROC curve and confusion matrix
Module 10: Capstone Project
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End-to-end data science workflow
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Final presentation and peer review
After completing this course, you’ll be ready for roles such as:
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Junior Data Scientist
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Data Analyst
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Machine Learning Intern
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Python Developer in Data Teams
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Data Science Research Assistant
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What are the key differences between NumPy arrays and Python lists?
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How do you handle missing values in Pandas?
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What is the difference between
.loc[]
and.iloc[]
in Pandas? -
How do you choose the right evaluation metric for a classification problem?
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What’s the difference between overfitting and underfitting?
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Explain the difference between regression and classification.
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What is the role of feature scaling in machine learning?
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How does a decision tree algorithm work?
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What is cross-validation and why is it important?
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How can you visualise the correlation between multiple variables?