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Python for Data Analytics

Master data analysis with Python - NumPy, Pandas, visualization, statistics and real-world projects.

SU Taught by Sukhwinder Singh 0 enrolled Intermediate

What you will learn

Learn data analytics with Python - the most in-demand skill in 2026.

Covers Python review, NumPy for numerical computing, Pandas for data manipulation, data cleaning, visualization with Matplotlib and Seaborn, statistics, exploratory data analysis and end-to-end projects.

Who this is for:
- Professionals transitioning into data analytics
- Students who completed the Python Programming course
- Anyone wanting to analyze data for business insights

Course Syllabus

0 lessons · 9 modules
1. Python Review for Data Analytics 5 lessons
  1. Lesson content for "Python setup for data science - Anaconda and Jupyter".

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  2. Lesson content for "Data types, lists, dicts for data work".

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  3. Lesson content for "List comprehensions for data transformation".

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  4. Lesson content for "File I/O - reading CSV, JSON and Excel".

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  5. Lesson content for "Functions and lambda for data pipelines".

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2. NumPy Fundamentals 6 lessons
  1. Lesson content for "Why NumPy - arrays vs Python lists".

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  2. Lesson content for "Creating arrays - zeros, ones, arange, linspace".

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  3. Lesson content for "Array operations and broadcasting".

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  4. Lesson content for "Indexing, slicing and boolean masks".

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  5. Lesson content for "Statistical functions with NumPy".

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  6. Lesson content for "Linear algebra basics with NumPy".

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3. Pandas Essentials 6 lessons
  1. Lesson content for "Series and DataFrame basics".

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  2. Lesson content for "Reading data - CSV, Excel, SQL, JSON".

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  3. Lesson content for "Selecting and filtering data".

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  4. Lesson content for "Adding, removing and renaming columns".

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  5. Lesson content for "Sorting and ranking data".

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  6. Lesson content for "Value counts and describe for summaries".

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4. Data Cleaning 5 lessons
  1. Lesson content for "Handling missing values - drop, fill, interpolate".

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  2. Lesson content for "Removing duplicates and fixing formats".

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  3. Lesson content for "String cleaning with .str methods".

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  4. Lesson content for "Type conversion and parsing dates".

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  5. Lesson content for "Outlier detection and treatment".

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5. Data Visualization 5 lessons
  1. Lesson content for "Visualization principles and when to use what".

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  2. Lesson content for "Matplotlib basics - line, bar, scatter".

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  3. Lesson content for "Seaborn for statistical plots".

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  4. Lesson content for "Customizing plots - colors, labels, themes".

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  5. Lesson content for "Interactive plots with Plotly".

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6. Advanced Visualization 4 lessons
  1. Lesson content for "Heatmaps and correlation matrices".

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  2. Lesson content for "Facet grids and multi-panel plots".

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  3. Lesson content for "Time series visualization".

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  4. Lesson content for "Geographic data and choropleth maps".

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7. Statistics for Data Analytics 6 lessons
  1. Lesson content for "Descriptive statistics - mean, median, mode".

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  2. Lesson content for "Standard deviation and variance".

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  3. Lesson content for "Probability distributions".

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  4. Lesson content for "Correlation and causation".

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  5. Lesson content for "Hypothesis testing fundamentals".

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  6. Lesson content for "Confidence intervals".

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8. Exploratory Data Analysis 5 lessons
  1. Lesson content for "EDA workflow and checklist".

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  2. Lesson content for "Univariate analysis techniques".

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  3. Lesson content for "Bivariate and multivariate analysis".

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  4. Lesson content for "GroupBy operations and pivot tables".

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  5. Lesson content for "Feature engineering basics".

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9. Real-World Projects 4 lessons
  1. Lesson content for "Sales data analysis and dashboard".

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  2. Lesson content for "Customer segmentation project".

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  3. Lesson content for "Time series - sales forecasting basics".

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  4. Lesson content for "Building a reusable analysis template".

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