Course Highlights
This course is designed for anyone who wants to learn Python for data science. No prior experience with Python is required. The course covers all the essential topics in a clear and concise way. You’ll learn by doing, with plenty of exercises and projects to give you real-life examples of what you’ve learned. This course unlocks the potential of Python, Pandas, and NumPy, equipping you with the tools to reshape, analyse, and visualise data. From the fundamentals of strings and numbers to the intricacies of data frames and arrays, you’ll traverse the landscape of data manipulation.
Explore Pandas and NumPy’s dynamic functionalities, enabling you to conquer missing data, aggregate insights, and navigate complex datasets. You’ll delve into CSV and JSON interactions, merge and pivot data, and craft compelling visualisations using Matplotlib.
As you traverse the course’s labyrinth, you’ll encounter the dynamic duo of Pandas and NumPy, unlocking their potential to sculpt and refine data. From unravelling the mysteries of data frames to orchestrating multi-dimensional arrays, you’ll ascend to new heights of data prowess. With the power to manage missing data and navigate intricate hierarchies, you’ll forge a path towards insights that others overlook.
Throughout this immersive journey, you’ll develop a sophisticated skill set, enabling you to wrangle complex datasets, derive meaningful patterns, and craft compelling visual narratives. Every click and every line of code will transform you into a data maestro. The “Python Data Science Complete Guide” is not just a course; it’s a transformative experience that equips you with the skills and confidence to master the language of data. Your journey starts now – immerse yourself and let data unveil its secrets.
Learning outcome
- Master Python's core concepts and syntax.
- Harness Pandas for data manipulation, including summarisation and grouping.
- Utilise NumPy arrays and multi-dimensional arrays for efficient data handling.
- Manage missing data and explore hierarchical indexing strategies.
- Excel in data import/export with CSV, JSON, and Excel sheets.
Course media
Why should I take this course?
- Acquire essential skills for data-centric roles.
- Master Python, Pandas, and NumPy for comprehensive data analysis.
- Create impactful visualisations using Matplotlib.
- Develop proficiency in transforming raw data into actionable insights.
Career Path
- Data Scientist
- Business Analyst
- Data Analyst
- Research Scientist
- Financial Analyst
- Market Analyst
Requirements
- Basic familiarity with programming concepts.
- A computer with Python and the required libraries.
- Eagerness to explore data science's intricate realm.
Course Curriculum
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Course Introduction and Table of Contents
00:09:00
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Introduction to Python, Pandas and Numpy
00:07:00
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System and Environment Setup
00:08:00
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Python Strings – Part 1
00:11:00
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Python Strings – Part 2
00:09:00
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Python Numbers and Operators – Part 1
00:06:00
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Python Numbers and Operators – Part 2
00:07:00
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Python Lists – Part 1
00:05:00
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Python Lists – Part 2
00:06:00
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Python Lists – Part 3
00:05:00
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Python Lists – Part 4
00:07:00
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Python Lists – Part 5
00:07:00
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Tuples in Python
00:06:00
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Sets in Python – Part 1
00:05:00
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Sets in Python – Part 2
00:04:00
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Python Dictionary – Part 1
00:07:00
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Python Dictionary – Part 2
00:07:00
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NumPy Library Intro – Part 1
00:05:00
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NumPy Library Intro – Part 2
00:05:00
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NumPy Library Intro – Part 3
00:06:00
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NumPy Array Operations and Indexing – Part 1
00:04:00
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NumPy Array Operations and Indexing – Part 2
00:06:00
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NumPy Multi-Dimensional Arrays – Part 1
00:07:00
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NumPy Multi-Dimensional Arrays – Part 2
00:06:00
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NumPy Multi-Dimensional Arrays – Part 3
00:05:00
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Introduction to Pandas Series
00:08:00
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Introduction to Pandas Dataframes
00:07:00
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Pandas Dataframe conversion and drop – Part 1
00:06:00
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Pandas Dataframe conversion and drop – Part 2
00:06:00
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Pandas Dataframe conversion and drop – Part 3
00:07:00
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Pandas Dataframe summary and selection – Part 1
00:06:00
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Pandas Dataframe summary and selection – Part 2
00:06:00
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Pandas Dataframe summary and selection – Part 3
00:07:00
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Pandas Missing Data Management and Sorting – Part 1
00:07:00
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Pandas Missing Data Management and Sorting – Part 2
00:07:00
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Pandas Hierarchical-Multi Indexing
00:06:00
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Pandas CSV File Read Write – Part 1
00:05:00
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Pandas CSV File Read Write – Part 2
00:07:00
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Pandas JSON File Read Write Operations
00:07:00
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Pandas Concatenation Merging and Joining – Part 1
00:05:00
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Pandas Concatenation Merging and Joining – Part 2
00:04:00
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Pandas Concatenation Merging and Joining – Part 3
00:04:00
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Pandas Stacking and Pivoting – Part 1
00:06:00
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Pandas Stacking and Pivoting – Part 2
00:05:00
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Pandas Duplicate Data Management
00:07:00
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Pandas Mapping
00:04:00
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Pandas Groupby
00:06:00
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Pandas Aggregation
00:09:00
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Pandas Binning or Bucketing
00:08:00
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Pandas Re-index and Rename – Part 1
00:04:00
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Pandas Re-index and Rename – Part 2
00:05:00
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Pandas Replace Values
00:05:00
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Pandas Dataframe Metrics
00:07:00
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Pandas Random Permutation
00:08:00
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Pandas Excel sheet Import
00:07:00
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Pandas Condition Selection and Lambda Function – Part 1
00:05:00
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Pandas Condition Selection and Lambda Function – Part 2
00:05:00
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Pandas Ranks Min Max
00:06:00
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Pandas Cross Tabulation
00:07:00
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Graphs and plots using Matplotlib – Part 1
00:06:00
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Graphs and plots using Matplotlib – Part 2
00:02:00
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Matplotlib Histograms
00:03:00
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Duration:6 hours, 20 minutes
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Access:1 Year
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Units:62
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