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⇱ Python and Statistics Foundations | Coursera

Python and Statistics Foundations

Python and Statistics Foundations

Instructor: Edureka

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Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

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.

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

16 assignments

Taught in English

Build your subject-matter expertise

This course is part of the Mastering AI: Neural Nets, Vision System, Speech Recognition Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

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!

Welcome to Python and Statistics Foundations, the first course in the AI Exploration program's series! This module is designed to help learners take a significant step towards launching their careers in tech. In the first week, we'll explore how Python programming concepts are essential for creating efficient programs. Let's get started!

What's included

25 videos7 readings5 assignments1 discussion prompt

25 videos•Total 111 minutes
  • Specialization Introduction•5 minutes
  • Course Introduction•4 minutes
  • Programming Language and Myths •7 minutes
  • Python for AI/ML - Code Simplicity•4 minutes
  • Python for AI/ML - Ease of Learning•5 minutes
  • Python Tokens Types•4 minutes
  • Literals•6 minutes
  • Operators - Basic Operators•8 minutes
  • Operators - Membership and Identity Operators•4 minutes
  • Explaining Data Types in Python•2 minutes
  • Demonstration of Data Types: Numeric, Sequence and Mapping•5 minutes
  • Executing Conditional Statement•5 minutes
  • Demonstration of if - else statement•3 minutes
  • Executing while loop•4 minutes
  • Demonstration of for loop•5 minutes
  • Looping Multiple Conditions•3 minutes
  • File Handling•5 minutes
  • Manipulating Files•5 minutes
  • User Defined Functions•6 minutes
  • Variable Argument and Variable Keyword Argument•3 minutes
  • Lambda Functions•4 minutes
  • More on Functions and Arguments•4 minutes
  • Modules in Python•4 minutes
  • Demonstration of Modules•5 minutes
  • Summary of Python Essentials•2 minutes
7 readings•Total 75 minutes
  • Welcome to Python and Statistics Foundations Course•10 minutes
  • Unlocking Python: The Language for Every Developer•5 minutes
  • Python Installation•15 minutes
  • Python Variables•5 minutes
  • Python Data Types: Numerical and Strings•20 minutes
  • Python Data Types: Tuples, Sets, and Dictionaries•10 minutes
  • File Handling Basics•10 minutes
5 assignments•Total 31 minutes
  • Practice Quiz: Python Essentials•2 minutes
  • Practice Quiz: Data Types in Python•3 minutes
  • Practice Quiz: Conditional Statements•4 minutes
  • Practice Quiz: Functions and File Handling•2 minutes
  • Knowledge Check: Python Essentials•20 minutes
1 discussion prompt•Total 10 minutes
  • Introduce Yourself•10 minutes

In the second week of this course, Learn how to manipulate data using NumPy and Pandas, working with various data formats. Gain proficiency in visualizing data using a range of charts and graphics.

What's included

26 videos4 readings5 assignments1 discussion prompt

26 videos•Total 144 minutes
  • Exploring NumPy•6 minutes
  • NumPy Operation•7 minutes
  • Working with NumPy•7 minutes
  • Pandas Framework•6 minutes
  • Pandas DataFrame Operation•6 minutes
  • Working with DataFrames in Pandas•6 minutes
  • DataFrames with Pandas: Operations and Insights•8 minutes
  • Creating a Series in Pandas•6 minutes
  • Working with Pandas Series•6 minutes
  • DataFrame: Data Maniplulation•6 minutes
  • DataFrame: Joining•7 minutes
  • DataFrame: Grouping the Data•6 minutes
  • DataFrame : Data Cleaning•6 minutes
  • DataFrame : Data Adjusting•5 minutes
  • Matplotlib Library•5 minutes
  • Plotting Charts with Matplotlib•5 minutes
  • Plotting Histogram and Box Plot•3 minutes
  • Plotting Multiple Charts•6 minutes
  • Introduction to Seaborn - Scatter Plot•6 minutes
  • Basic Charts in Seaborn•6 minutes
  • Seaborn - Heatmap Manipulation•2 minutes
  • Seaborn : Flights Dataset•5 minutes
  • Seaborn: Gaining Insights in Flight Data•5 minutes
  • Visualizing Charts with Plotly•5 minutes
  • Customizing Different Charts in Plotly•6 minutes
  • Summary : Exploring NumPy and Pandas in Python•2 minutes
4 readings•Total 34 minutes
  • Memory and Performance Comparison Between Python Lists and NumPy Arrays•10 minutes
  • Data Manipulation and Analysis with Python Pandas•7 minutes
  • Data Visualization Fundamentals•7 minutes
  • Introduction to Matplotlib•10 minutes
5 assignments•Total 28 minutes
  • Practice Quiz: NumPy with Python•2 minutes
  • Practice Quiz: Data Manipulation using Pandas•2 minutes
  • Practice Quiz: Dataframes in Action•2 minutes
  • Practice Quiz: Data Visualization•2 minutes
  • Knowledge Check: Exploring Data with NumPy and Pandas•20 minutes
1 discussion prompt•Total 10 minutes
  • Which Python visualization library do you prefer?•10 minutes

In the third week of this course, we'll delve into statistics and probability. We'll explore measures of central tendency to handle various data inconsistencies. Additionally, we'll cover topics such as joint and marginal probability, as well as the fundamentals of hypothesis testing.

What's included

20 videos3 readings5 assignments

20 videos•Total 97 minutes
  • What is Statistics?•6 minutes
  • Measures of Central Tendency•6 minutes
  • Managing Statistical Data•5 minutes
  • Exploring and Analyzing Data•3 minutes
  • Measures of Dispersion•6 minutes
  • Dispersion: Demonstration•6 minutes
  • Application of Dispersion in Large Data•2 minutes
  • Probability - Sample Space and Events•5 minutes
  • Need for Probability•4 minutes
  • Types of Probability•5 minutes
  • Marginal and Joint Probability: Demonstration•4 minutes
  • Conditional Probability: Demonstration•4 minutes
  • What is Hypothesis Testing?•7 minutes
  • Steps for Hypothesis Testing•3 minutes
  • Null and Alternate Hypothesis•4 minutes
  • Statistical Test Interpretation•7 minutes
  • One - Tailed and Two Tailed Test•6 minutes
  • Hypothesis Testing: Demonstration•5 minutes
  • Margin of Error and Confidence Interval: Demonstration•6 minutes
  • Summary : Statistical Analysis with Python•2 minutes
3 readings•Total 22 minutes
  • The Importance of Statistics in Data Interpretation•5 minutes
  • Introduction to Probability•10 minutes
  • Introduction to Hypothesis Testing•7 minutes
5 assignments•Total 31 minutes
  • Practice Quiz: Statistics in Python•2 minutes
  • Practice Quiz: Statistical Dispersion•3 minutes
  • Practice Quiz: Probability Functions•3 minutes
  • Practice Quiz: Hypothesis Testing•3 minutes
  • Knowledge Check: Statistics in Python•20 minutes

This module is designed to assess an individual on the various concepts and teachings covered in this course. Evaluate your knowledge with a comprehensive graded quiz on Python programming concepts, Data manipulation with NumPy and Pandas with Statistical Analysis

What's included

1 video1 reading1 assignment1 discussion prompt

1 video•Total 3 minutes
  • Course Summary: Python and Statistics Foundations•3 minutes
1 reading•Total 60 minutes
  • Practice Project: Travel Aggregator Analysis•60 minutes
1 assignment•Total 20 minutes
  • End Course Knowledge Check: Python and Statistics•20 minutes
1 discussion prompt•Total 10 minutes
  • Describe Your Learning Journey•10 minutes

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Instructor

Edureka
203 Courses•185,285 learners

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Frequently asked questions

This course provides an introduction to Python programming and essential statistical concepts. Designed for Python enthusiasts, it emphasizes practical skills for data manipulation and analysis, enabling learners to tackle real-world data challenges.

Learners will explore Python fundamentals, harness libraries like NumPy and Pandas for efficient data handling, and utilize visualization tools such as Matplotlib, Seaborn, and Plotly to present their findings. Additionally, the course covers key statistical methods and probability theory, equipping you with the tools to make data-driven decisions.

By the end of the course, you'll be well-prepared to apply these skills in various data analysis scenarios, setting a foundation for further studies in data science and artificial intelligence.

This course is designed for:

- Freshers looking to enter the fields of data analysis, data science, or artificial intelligence.

- Individuals with a keen interest in programming and statistics who want to enhance their technical skills.

- Professionals seeking to upskill in Python and data manipulation for practical applications in their careers.

- Anyone curious about data and eager to learn how to analyze and visualize it effectively.

Whether you're starting your tech journey or looking to build a strong foundation, this course will guide you through the essentials of Python and statistics.

The Python and Statistics Foundations course spans approximately 15 hours in total and is designed to be completed at a pace of 3-4 hours per week. This allows learners to absorb the material effectively while balancing other commitments.

The course utilizes Google Colab as the primary platform for coding operations. Learners may also use integrated development environments (IDEs) like Jupyter Notebook, PyCharm, Spyder, or VS Code for more extensive coding projects if desired.

You will gain hands-on experience with NumPy, Pandas, Matplotlib, Seaborn, and Plotly for data exploration and visualization.

The course introduces statistical foundations and demonstrates how to implement tests like confidence intervals and p-values directly in Python.

Yes, the course supports both Google Colab and Jupyter Notebook as coding environments.

Yes, a shareable certificate is awarded upon completion, which you can add to LinkedIn or your resume.

Yes, the course is designed to be beginner-friendly, starting from Python basics and gradually introducing statistics.

It prepares you for careers in data analysis, business intelligence, data science, or as a foundation for advanced machine learning and AI courses.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,