| 01-Python_Essentials | |||
| 01-Programming_With_Python | |||
| 01-specialization_introduction.mp4 | 21.47 MB | ||
| 02-welcome_to_python_and_statistics_foundations_course_instructions.html | 7.15 KB | ||
| 03-course_introduction.mp4 | 15.57 MB | ||
| 04-unlocking_python_the_language_for_every_developer_instructions.html | 3.83 KB | ||
| 05-python_installation_instructions.html | 3.2 MB | ||
| 06-programming_language_and_myths.mp4 | 25.7 MB | ||
| 07-python_for_ai_ml_code_simplicity.mp4 | 21.82 MB | ||
| 08-python_for_ai_ml_ease_of_learning.mp4 | 20.21 MB | ||
| 09-python_tokens_types.mp4 | 17.19 MB | ||
| 10-literals.mp4 | 36.17 MB | ||
| 11-operators_basic_operators.mp4 | 71.05 MB | ||
| 12-operators_membership_and_identity_operators.mp4 | 33.32 MB | ||
| 02-Data_Types | |||
| 01-explaining_data_types_in_python.mp4 | 10.74 MB | ||
| 02-demonstration_of_data_types_numeric_sequence_and_mapping.mp4 | 53.5 MB | ||
| 03-python_variables_instructions.html | 442.23 KB | ||
| 04-python_data_types_numerical_and_strings_instructions.html | 6.88 KB | ||
| 05-python_data_types_tuples_sets_and_dictionaries_instructions.html | 5.16 KB | ||
| 03-Conditional_Statements | |||
| 01-executing_conditional_statement.mp4 | 31.4 MB | ||
| 02-demonstration_of_if_else_statement.mp4 | 22.25 MB | ||
| 03-executing_while_loop.mp4 | 20.16 MB | ||
| 04-demonstration_of_for_loop.mp4 | 29.5 MB | ||
| 05-looping_multiple_conditions.mp4 | 22.93 MB | ||
| 04-Advanced_Functions_And_File_Handling | |||
| 01-file_handling.mp4 | 26.08 MB | ||
| 02-file_handling_basics_instructions.html | 5.27 KB | ||
| 03-manipulating_files.mp4 | 47.77 MB | ||
| 04-user_defined_functions.mp4 | 47.71 MB | ||
| 05-variable_argument_and_variable_keyword_argument.mp4 | 27.71 MB | ||
| 06-lambda_functions.mp4 | 38.4 MB | ||
| 07-more_on_functions_and_arguments.mp4 | 32.09 MB | ||
| 08-modules_in_python.mp4 | 17.74 MB | ||
| 09-demonstration_of_modules.mp4 | 26.99 MB | ||
| 05-Module_Wrap_Up_And_Assessment | |||
| 01-summary_of_python_essentials.mp4 | 9.68 MB | ||
| 02-Exploring_Data_With_Numpy_And_Pandas | |||
| 01-Numpy_With_Python | |||
| 01-memory_and_performance_comparison_between_python_lists_and_numpy_arrays_instructions.html | 4.97 KB | ||
| 02-exploring_numpy.mp4 | 20.32 MB | ||
| 03-numpy_operation.mp4 | 44.53 MB | ||
| 04-working_with_numpy.mp4 | 48.82 MB | ||
| 02-Data_Manipulation_Using_Pandas | |||
| 01-data_manipulation_and_analysis_with_python_pandas_instructions.html | 3.8 KB | ||
| 02-pandas_framework.mp4 | 26.66 MB | ||
| 03-pandas_dataframe_operation.mp4 | 47.06 MB | ||
| 04-working_with_dataframes_in_pandas.mp4 | 37.19 MB | ||
| 05-dataframes_with_pandas_operations_and_insights.mp4 | 43.72 MB | ||
| 06-creating_a_series_in_pandas.mp4 | 44.66 MB | ||
| 07-working_with_pandas_series.mp4 | 28.95 MB | ||
| 03-Dataframes_In_Action | |||
| 01-dataframe_data_maniplulation.mp4 | 36.41 MB | ||
| 02-dataframe_joining.mp4 | 46.45 MB | ||
| 03-dataframe_grouping_the_data.mp4 | 32.52 MB | ||
| 04-dataframe_data_cleaning.mp4 | 30.31 MB | ||
| 05-dataframe_data_adjusting.mp4 | 34.82 MB | ||
| 04-Data_Visualization | |||
| 01-data_visualization_fundamentals_instructions.html | 5.71 KB | ||
| 02-introduction_to_matplotlib_instructions.html | 75.07 KB | ||
| 03-matplotlib_library.mp4 | 29.39 MB | ||
| 04-plotting_charts_with_matplotlib.mp4 | 26.47 MB | ||
| 05-plotting_histogram_and_box_plot.mp4 | 14.15 MB | ||
| 06-plotting_multiple_charts.mp4 | 39.91 MB | ||
| 07-introduction_to_seaborn_scatter_plot.mp4 | 37.61 MB | ||
| 08-basic_charts_in_seaborn.mp4 | 31.38 MB | ||
| 09-seaborn_heatmap_manipulation.mp4 | 12.7 MB | ||
| 10-seaborn_flights_dataset.mp4 | 26.21 MB | ||
| 11-seaborn_gaining_insights_in_flight_data.mp4 | 32.94 MB | ||
| 12-visualizing_charts_with_plotly.mp4 | 25.59 MB | ||
| 13-customizing_different_charts_in_plotly.mp4 | 33.08 MB | ||
| 05-Module_Wrap_Up_And_Assessment | |||
| 01-summary_exploring_numpy_and_pandas_in_python.mp4 | 8.48 MB | ||
| 03-Statistics_In_Python | |||
| 01-Comprehending_Statistics | |||
| 00. Support - Onehack.Us.txt | 94 B | ||
| 01-the_importance_of_statistics_in_data_interpretation_instructions.html | 5.09 KB | ||
| 02-what_is_statistics.mp4 | 24.24 MB | ||
| 03-measures_of_central_tendency.mp4 | 31.19 MB | ||
| 04-managing_statistical_data.mp4 | 33.27 MB | ||
| 05-exploring_and_analyzing_data.mp4 | 23.21 MB | ||
| 02-Statistical_Dispersion | |||
| 01-measures_of_dispersion.mp4 | 27.46 MB | ||
| 02-dispersion_demonstration.mp4 | 36.9 MB | ||
| 03-application_of_dispersion_in_large_data.mp4 | 12.54 MB | ||
| 03-Probability_Foundation | |||
| 01-introduction_to_probability_instructions.html | 444.65 KB | ||
| 03-need_for_probability.mp4 | 14.28 MB | ||
| 04-types_of_probability.mp4 | 19.52 MB | ||
| 05-marginal_and_joint_probability_demonstration.mp4 | 24.44 MB | ||
| 06-conditional_probability_demonstration.mp4 | 25.08 MB | ||
| 04-Hypothesis_Testing | |||
| 00. Support - Onehack.Us.txt | 94 B | ||
| 01-introduction_to_hypothesis_testing_instructions.html | 36.67 KB | ||
| 02-what_is_hypothesis_testing.mp4 | 25.63 MB | ||
| 03-steps_for_hypothesis_testing.mp4 | 16.87 MB | ||
| 04-null_and_alternate_hypothesis.mp4 | 30.05 MB | ||
| 05-statistical_test_interpretation.mp4 | 28.52 MB | ||
| 06-one_tailed_and_two_tailed_test.mp4 | 24.22 MB | ||
| 07-hypothesis_testing_demonstration.mp4 | 31.72 MB | ||
| 08-margin_of_error_and_confidence_interval_demonstration.mp4 | 49.57 MB | ||
| 05-Module_Wrap_Up_And_Assessment | |||
| 01-summary_statistical_analysis_with_python.mp4 | 10.45 MB | ||
| 04-Course_Wrap_Up_And_Assessment | |||
| 01-course_summary_python_and_statistics_foundations.mp4 | 14.17 MB | ||
| 02-practice_project_travel_aggregator_analysis_instructions.html | 5.77 KB | ||
| Data Resources.zip | 434.2 KB | ||
| Support - Onehack.Us.txt | 94 B |
Visit >>> http://onehack.us/
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Coursera - Python and Statistics Foundations 2025
What you'll learn
- Write Python programs using core concepts like variables, data types, and control flow.
- Apply NumPy and Pandas to manipulate and analyze data efficiently.
- Create insightful data visualizations using Matplotlib, Seaborn, and Plotly for effective reporting.
- Perform statistical analysis and probability tests to solve data-driven problems and validate hypotheses.
There are 4 modules in this course
This course introduces Python programming and fundamental statistics concepts, equipping learners with essential skills for data-driven roles in tech and AI. Through hands-on experience, you'll learn how to manipulate data, visualize insights, and apply statistical techniques for data analysis.
By the end of this course, you will be able to:
- Understand and apply Python programming concepts such as data types, operators, and control structures
- Manipulate data using popular libraries like NumPy and Pandas
- Visualize data with Python libraries such as Matplotlib, Seaborn, and Plotly
- Analyze data using statistical techniques, including measures of central tendency, dispersion, and probability
- Perform hypothesis testing and draw insights from the data
This course is designed for beginners, data enthusiasts, and aspiring data scientists who want to build a strong foundation in Python programming and statistical analysis.
No prior programming experience is required, although familiarity with basic statistics will be helpful.
Join us to start your journey into data analysis and programming with Python!
General Details:
Duration: 5h 50m 51s
Updated: 3/2025
Language: English
Source: https://www.coursera.org/learn/python-and-statistics-foundations
Instructor: https://www.edureka.co/
MP4 | Video: AVC, 1920x1080p | Audio: AAC, 44.100 KHz, 2 Ch
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Coursera - AI Agents and Agentic AI in Python: Powered by Generative AI Specialization Posted by
notimmune in Other
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4.3 GB | notimmune | 1 year | 26 | 6 |
| 2.66 GB | Prom3th3uS | 1 year | 7 | 1 | |
| 2.31 GB | Prom3th3uS | 1 year | 3 | 0 | |
| 2.4 GB | freecoursewb | 1 year | 2 | 3 | |
| 670.9 MB | Anonymous | 1 year | 3 | 1 |
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