Base Python For Data Analytics

lesedev317 · 16 Februar 2025

Step-by-step Python training for beginners-no prior coding experience required!
What you'll learn
Understand and apply core Python concepts - Learn variables, data types, operators, and how Python works behind the scenes.
Work with essential data structures - Master lists, dictionaries, tuples, and sets to efficiently store and manipulate data.
Write and control program flow - Use if-else statements, for loops, and while loops to build logical and dynamic programs.
Create reusable code with functions and classes - Write functions and object-oriented programs to structure and optimize your code.
Requirements
Time to practice coding
Free Anaconda software
Free Chrome browser
No prior Python coding knowledge
Description
Who Should NOT Take This Course?This course is designed for those who want to truly learn Python and build a strong foundation for data analytics-not just memorize syntax.If you enjoy learning by solving problems rather than just copying and pasting code, you'll benefit the most.This course focuses on base Python fundamentals without relying on libraries like NumPy, Pandas, or Matplotlib.If you're looking for a shortcut to a certificate or a quick-fix approach, this may not be the right course for you. But if you want to develop real skills and confidence in Python, you're in the right place!"If you want to truly learn Python and build a strong foundation for data analytics, this course is exactly what you need!"Want to build a solid foundation in Python? This course is for you!Get 570+ practice questions, including standalone exercises and projects to apply your Python skills!!!Python is one of the most widely used programming languages, especially in data analytics, machine learning, and automation. Before diving into specialized libraries like NumPy and pandas, it's crucial to master base Python concepts-and that's exactly what this course will help you do. Note that numpy, pandas, matplotlib is not covered in this course.This course focuses on pure base Python for analytics. It does not cover NumPy, pandas, or matplotlib.What You Will Learn:- Python syntax, variables, and data types-Working with lists, tuples, dictionaries, and sets- Using if-else statements and loops to control program flow- Writing functions and understanding function arguments- Introduction to object-oriented programming (OOP) with classes and objects- Best practices for writing clean and efficient Python codeCourse Structure

1) Introduction to Python - Why Python? Installation and setup (Anaconda & Jupyter Notebook).(2) Python Basics - Variables, data types, type conversions, operators, and expressions.(3) Data Structures - Lists, tuples, dictionaries, sets, and their use cases.(4) Control Flow - If-else conditions, for loops, while loops, and iteration techniques.(5) Functions & Modular Programming - Writing reusable functions, function arguments, and lambda functions.(6) Object-Oriented Programming (OOP) - Understanding classes, objects, methods, and basic OOP concepts.(7) Hands-on Coding Exercises - Apply your skills with real-world examples and challenges.This course is designed for absolute beginners and requires no prior programming experience. By the end, you'll have a strong grasp of Python fundamentals, preparing you for advanced topics like data analytics and automation.Join now and start your Python journey today!
Who this course is for
Beginner in Python
Want to learn coding
Student
Working professional
Looking for a career change into data analytics or data science
Want to build a strong Python foundation before diving into data analytics, machine learning, or AI? Start here!
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0dayddl · Sonntag um 14:28

1.99 GB | 00:05:52 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English

Files Included :

01 welcome-to-course-3.mp4 (50.53 MB)
02 generative-ai-in-this-course.mp4 (6.81 MB)
03 module-1-introduction.mp4 (9.51 MB)
01 computer-programming.mp4 (17.58 MB)
02 navigating-the-jupyter-notebook-environment.mp4 (18.64 MB)
03 input-processing-output.mp4 (16.5 MB)
04 python-or-a-spreadsheet.mp4 (21.84 MB)
01 types-and-expressions.mp4 (13.58 MB)
02 printing-and-comments.mp4 (20.37 MB)
04 storing-information-variables.mp4 (20.35 MB)
05 debugging-with-variables.mp4 (24.67 MB)
08 creating-lists.mp4 (24.08 MB)
09 list-operations.mp4 (22.51 MB)
10 taking-action-calling-functions.mp4 (27.5 MB)
12 state.mp4 (20.64 MB)
01 control-flow.mp4 (11.3 MB)
02 comparison.mp4 (26.8 MB)
03 branching-code-if-else.mp4 (16.27 MB)
05 repeating-actions-for-loops.mp4 (20.91 MB)
06 indentation.mp4 (22.76 MB)
07 branching-code-elif.mp4 (21.84 MB)
09 repeating-actions-range.mp4 (20.02 MB)
11 execution-order.mp4 (18.56 MB)
01 your-first-graded-lab.mp4 (24.03 MB)
01 module-2-introduction.mp4 (10.39 MB)
01 beyond-lists.mp4 (15.83 MB)
02 importing-modules.mp4 (18.52 MB)
03 pandas.mp4 (11.03 MB)
04 reading-csv-into-python.mp4 (25.51 MB)
06 dataframes.mp4 (22.18 MB)
07 attributes-and-methods.mp4 (11.56 MB)
09 selecting-columns.mp4 (23.37 MB)
10 counts-sums-histograms.mp4 (20.29 MB)
01 sorting.mp4 (14.4 MB)
02 sorting-by-multiple-columns.mp4 (12.12 MB)
04 filtering.mp4 (19.99 MB)
05 filtering-by-multiple-conditions.mp4 (14.03 MB)
07 selecting-rows.mp4 (28.06 MB)
01 central-tendency-variability-and-skewness.mp4 (22.51 MB)
02 categorical-data.mp4 (25.2 MB)
04 correlation.mp4 (31.91 MB)
05 segmentation-by-one-feature.mp4 (16.59 MB)
06 segmentation-by-multiple-features.mp4 (27.9 MB)
01 module-3-introduction.mp4 (7.99 MB)
01 plotting-with-matplotlib.mp4 (24.72 MB)
03 colors-grids-saving-plots.mp4 (18.47 MB)
04 text-annotations.mp4 (28.01 MB)
05 ticks-spines.mp4 (25.28 MB)
06 grouped-column-charts.mp4 (24.43 MB)
07 stacked-column-charts.mp4 (17.05 MB)
08 scatter-plots.mp4 (22.66 MB)
09 method-chaining.mp4 (19.95 MB)
01 plotting-with-seaborn.mp4 (19.88 MB)
02 themes-palettes.mp4 (20.97 MB)
03 box-plots.mp4 (21.13 MB)
04 histograms.mp4 (20.86 MB)
05 other-charts.mp4 (30.67 MB)
01 combining-charts.mp4 (20.76 MB)
02 matplotlib-subplots.mp4 (19.22 MB)
03 looping-with-subplots.mp4 (19.63 MB)
04 seaborn-pairplot.mp4 (26.2 MB)
01 module-4-introduction.mp4 (8.84 MB)
01 confidence-intervals.mp4 (26.49 MB)
03 one-sample-t-tests.mp4 (21.98 MB)
04 two-sample-t-tests.mp4 (22.26 MB)
05 simulation-uniform.mp4 (22.05 MB)
06 simulation-normal.mp4 (31.93 MB)
01 what-is-linear-regression.mp4 (26.77 MB)
02 choosing-an-independent-variable.mp4 (19.54 MB)
03 training-the-model.mp4 (21.02 MB)
04 interpreting-the-output-of-a-regression-model.mp4 (27.89 MB)
05 prediction.mp4 (30.59 MB)
01 multiple-linear-regression.mp4 (16.59 MB)
02 training-a-multiple-linear-regression-model.mp4 (27.59 MB)
03 interpreting-multiple-linear-regression.mp4 (19.58 MB)
04 encoding-categorical-data.mp4 (18.21 MB)
05 modeling-with-categorical-data.mp4 (29.18 MB)
06 prediction-multiple-linear-regression.mp4 (14.15 MB)
07 evaluating-your-model.mp4 (25.99 MB)
08 llms-for-model-iteration.mp4 (46.25 MB)
09 the-linear-regression-process.mp4 (15.11 MB)
01 module-5-introduction.mp4 (8.98 MB)
01 datetimes.mp4 (26.77 MB)
02 using-datetimes-as-indices.mp4 (19.75 MB)
04 line-charts.mp4 (25.11 MB)
05 formatting-date-axis-labels.mp4 (27.31 MB)
01 moving-average.mp4 (30.94 MB)
02 percent-change.mp4 (21.21 MB)
03 segmentation.mp4 (25.15 MB)
04 multiple-line-charts-reshaping.mp4 (26.77 MB)
06 resampling.mp4 (30.32 MB)
01 forecasting-with-the-trend.mp4 (24.39 MB)
02 forecasting-with-seasonality.mp4 (26.55 MB)
03 error-metrics-for-forecasting.mp4 (29.59 MB)
01 your-next-steps.mp4 (29.23 MB)

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