logo
GBP
GBP
USD
AUD
CAD
EUR
BDT
SGD
RUB
KWD
KRW
CNY
AED
SAR
logo
logo
Discounted Courses
Popular Courses
Top Courses
New Courses
Free Courses

Course Features

Price

Original price was: £490.00.Current price is: £14.99.

Study Method

Online | Self-paced

Course Format

Reading Material - PDF, article

Duration

6 hours, 55 minutes

Qualification

No formal qualification

Certificate

At completion

Additional info

Coming soon

  • Share
Learn practical data analysis techniques in Python using NumPy and Pandas for real-world data processing.

Overview

In the Data Analysis with NumPy and Pandas: Practical Techniques in Python course, learners are introduced to the fundamental tools needed to work with large datasets and perform complex numerical analysis. The course begins with mastering NumPy, one of the most popular libraries for numerical computing in Python. Students will learn how to create NumPy arrays, index and slice them, and perform statistical operations. By the end of the section, learners will understand how to reshape arrays, handle missing or infinite values, and perform conditional selection with NumPy's efficient functions.

Next, the course delves into Pandas, the go-to library for data manipulation and analysis. Learners will begin with an introduction to Pandas Series and DataFrames, which are essential for handling and analyzing structured data. The course covers a wide range of techniques for data manipulation, such as applying functions, handling NULL values, filtering data, renaming columns, and handling duplicates. Moreover, you will learn how to group and merge data, making it easier to aggregate and summarize large datasets.

For real-world applications, the course also includes sections on handling DateTime objects, working with stock data from Yahoo Finance, and performing arithmetic operations on dataframes. These skills are key for analyzing time series data and working with financial datasets. With these tools, students will be well-equipped to tackle the challenges faced by data analysts and data scientists in various fields, from business to finance to health care.

This course is ideal for individuals interested in data analysis, particularly those with a basic understanding of Python who want to deepen their knowledge of NumPy and Pandas. It is suited for aspiring data analysts, data scientists, business analysts, or anyone looking to improve their Python skills for analyzing and working with data.
Basic knowledge of Python programming is required for this course. Familiarity with general programming concepts such as variables, loops, and functions will make it easier to grasp the concepts taught in the course. No prior knowledge of NumPy or Pandas is necessary, as this course covers the fundamentals in detail.
After completing the Data Analysis with NumPy and Pandas course, learners will be prepared to pursue roles such as data analyst, data scientist, business intelligence analyst, and Python developer. The skills acquired will open up opportunities in various industries, including finance, technology, healthcare, and marketing, where data analysis is key to decision-making. Furthermore, the expertise gained in handling and analyzing data can also lead to more advanced learning in machine learning, artificial intelligence, and big data analytics.

Who is this course for?

In the Data Analysis with NumPy and Pandas: Practical Techniques in Python course, learners are introduced to the fundamental tools needed to work with large datasets and perform complex numerical analysis. The course begins with mastering NumPy, one of the most popular libraries for numerical computing in Python. Students will learn how to create NumPy arrays, index and slice them, and perform statistical operations. By the end of the section, learners will understand how to reshape arrays, handle missing or infinite values, and perform conditional selection with NumPy's efficient functions.

Next, the course delves into Pandas, the go-to library for data manipulation and analysis. Learners will begin with an introduction to Pandas Series and DataFrames, which are essential for handling and analyzing structured data. The course covers a wide range of techniques for data manipulation, such as applying functions, handling NULL values, filtering data, renaming columns, and handling duplicates. Moreover, you will learn how to group and merge data, making it easier to aggregate and summarize large datasets.

For real-world applications, the course also includes sections on handling DateTime objects, working with stock data from Yahoo Finance, and performing arithmetic operations on dataframes. These skills are key for analyzing time series data and working with financial datasets. With these tools, students will be well-equipped to tackle the challenges faced by data analysts and data scientists in various fields, from business to finance to health care.

This course is ideal for individuals interested in data analysis, particularly those with a basic understanding of Python who want to deepen their knowledge of NumPy and Pandas. It is suited for aspiring data analysts, data scientists, business analysts, or anyone looking to improve their Python skills for analyzing and working with data.
Basic knowledge of Python programming is required for this course. Familiarity with general programming concepts such as variables, loops, and functions will make it easier to grasp the concepts taught in the course. No prior knowledge of NumPy or Pandas is necessary, as this course covers the fundamentals in detail.
After completing the Data Analysis with NumPy and Pandas course, learners will be prepared to pursue roles such as data analyst, data scientist, business intelligence analyst, and Python developer. The skills acquired will open up opportunities in various industries, including finance, technology, healthcare, and marketing, where data analysis is key to decision-making. Furthermore, the expertise gained in handling and analyzing data can also lead to more advanced learning in machine learning, artificial intelligence, and big data analytics.

Requirements

In the Data Analysis with NumPy and Pandas: Practical Techniques in Python course, learners are introduced to the fundamental tools needed to work with large datasets and perform complex numerical analysis. The course begins with mastering NumPy, one of the most popular libraries for numerical computing in Python. Students will learn how to create NumPy arrays, index and slice them, and perform statistical operations. By the end of the section, learners will understand how to reshape arrays, handle missing or infinite values, and perform conditional selection with NumPy's efficient functions.

Next, the course delves into Pandas, the go-to library for data manipulation and analysis. Learners will begin with an introduction to Pandas Series and DataFrames, which are essential for handling and analyzing structured data. The course covers a wide range of techniques for data manipulation, such as applying functions, handling NULL values, filtering data, renaming columns, and handling duplicates. Moreover, you will learn how to group and merge data, making it easier to aggregate and summarize large datasets.

For real-world applications, the course also includes sections on handling DateTime objects, working with stock data from Yahoo Finance, and performing arithmetic operations on dataframes. These skills are key for analyzing time series data and working with financial datasets. With these tools, students will be well-equipped to tackle the challenges faced by data analysts and data scientists in various fields, from business to finance to health care.

This course is ideal for individuals interested in data analysis, particularly those with a basic understanding of Python who want to deepen their knowledge of NumPy and Pandas. It is suited for aspiring data analysts, data scientists, business analysts, or anyone looking to improve their Python skills for analyzing and working with data.
Basic knowledge of Python programming is required for this course. Familiarity with general programming concepts such as variables, loops, and functions will make it easier to grasp the concepts taught in the course. No prior knowledge of NumPy or Pandas is necessary, as this course covers the fundamentals in detail.
After completing the Data Analysis with NumPy and Pandas course, learners will be prepared to pursue roles such as data analyst, data scientist, business intelligence analyst, and Python developer. The skills acquired will open up opportunities in various industries, including finance, technology, healthcare, and marketing, where data analysis is key to decision-making. Furthermore, the expertise gained in handling and analyzing data can also lead to more advanced learning in machine learning, artificial intelligence, and big data analytics.

Career path

In the Data Analysis with NumPy and Pandas: Practical Techniques in Python course, learners are introduced to the fundamental tools needed to work with large datasets and perform complex numerical analysis. The course begins with mastering NumPy, one of the most popular libraries for numerical computing in Python. Students will learn how to create NumPy arrays, index and slice them, and perform statistical operations. By the end of the section, learners will understand how to reshape arrays, handle missing or infinite values, and perform conditional selection with NumPy's efficient functions.

Next, the course delves into Pandas, the go-to library for data manipulation and analysis. Learners will begin with an introduction to Pandas Series and DataFrames, which are essential for handling and analyzing structured data. The course covers a wide range of techniques for data manipulation, such as applying functions, handling NULL values, filtering data, renaming columns, and handling duplicates. Moreover, you will learn how to group and merge data, making it easier to aggregate and summarize large datasets.

For real-world applications, the course also includes sections on handling DateTime objects, working with stock data from Yahoo Finance, and performing arithmetic operations on dataframes. These skills are key for analyzing time series data and working with financial datasets. With these tools, students will be well-equipped to tackle the challenges faced by data analysts and data scientists in various fields, from business to finance to health care.

This course is ideal for individuals interested in data analysis, particularly those with a basic understanding of Python who want to deepen their knowledge of NumPy and Pandas. It is suited for aspiring data analysts, data scientists, business analysts, or anyone looking to improve their Python skills for analyzing and working with data.
Basic knowledge of Python programming is required for this course. Familiarity with general programming concepts such as variables, loops, and functions will make it easier to grasp the concepts taught in the course. No prior knowledge of NumPy or Pandas is necessary, as this course covers the fundamentals in detail.
After completing the Data Analysis with NumPy and Pandas course, learners will be prepared to pursue roles such as data analyst, data scientist, business intelligence analyst, and Python developer. The skills acquired will open up opportunities in various industries, including finance, technology, healthcare, and marketing, where data analysis is key to decision-making. Furthermore, the expertise gained in handling and analyzing data can also lead to more advanced learning in machine learning, artificial intelligence, and big data analytics.

    • NumPy Introduction – Creating NumPy Arrays 00:10:00
    • Array Indexing and Slicing 00:10:00
    • Understanding NumPy Data Types 00:10:00
    • Handling np.nan and np.inf 00:10:00
    • Statistical Operations in NumPy 00:10:00
    • Reshaping Arrays – shape(), reshape(), ravel(), flatten() 00:10:00
    • Array Creation – arange(), linspace(), random(), zeros(), ones() 00:10:00
    • Conditional Selection with where() 00:10:00
    • Reading and Writing NumPy Arrays 00:10:00
    • Array Concatenation and Sorting 00:10:00
    • Pandas Series Introduction – Part 1 00:10:00
    • Pandas Series Introduction – Part 2 00:10:00
    • Reading Series Data from Files 00:10:00
    • Using Built-in Functions with Series 00:10:00
    • Applying apply() on Pandas Series 00:10:00
    • Creating DataFrames from Scratch 00:10:00
    • Reading Files as DataFrames 00:10:00
    • Column Manipulation – Part 1 00:10:00
    • Column Manipulation – Part 2 00:10:00
    • Arithmetic Operations in DataFrames 00:10:00
    • Handling NULL Values 00:10:00
    • DataFrame Filtering – Part 1 00:10:00
    • DataFrame Filtering – Part 2 00:10:00
    • Handling Unique and Duplicated Values 00:10:00
    • Retrieving Rows by Index Label 00:10:00
    • Replacing Cell Values in DataFrames 00:10:00
    • Renaming and Deleting Index and Columns 00:10:00
    • Using Lambda with apply() 00:10:00
    • Grouping Data with groupby() 00:10:00
    • Grouping Data with groupby() 00:10:00
    • Merging, Joining, and Concatenation – Part 1 00:10:00
    • Concatenating DataFrames 00:10:00
    • Merging and Joining DataFrames 00:10:00
    • Working with DateTime in Pandas 00:10:00
    • Reading Stock Data from Yahoo Finance 00:10:00
    • Exam of Data Analysis with NumPy and Pandas: Practical Techniques in Python 00:50:00
    • Premium Certificate 00:15:00
certificate-new

No Reviews found for this course.

Yes, our premium certificate and transcript are widely recognized and accepted by embassies worldwide, particularly by the UK embassy. This adds credibility to your qualification and enhances its value for professional and academic purposes.

Yes, this course is designed for learners of all levels, including beginners. The content is structured to provide step-by-step guidance, ensuring that even those with no prior experience can follow along and gain valuable knowledge.

Yes, professionals will also benefit from this course. It covers advanced concepts, practical applications, and industry insights that can help enhance existing skills and knowledge. Whether you are looking to refine your expertise or expand your qualifications, this course provides valuable learning.

No, you have lifetime access to the course. Once enrolled, you can revisit the materials at any time as long as the course remains available. Additionally, we regularly update our content to ensure it stays relevant and up to date.

I trust you’re in good health. Your free certificate can be located in the Achievement section. The option to purchase a CPD certificate is available but entirely optional, and you may choose to skip it. Please be aware that it’s crucial to click the “Complete” button to ensure the certificate is generated, as this process is entirely automated.

Yes, the course includes both assessments and assignments. Your final marks will be determined by a combination of 20% from assignments and 80% from assessments. These evaluations are designed to test your understanding and ensure you have grasped the key concepts effectively.

We are a recognized course provider with CPD, UKRLP, and AOHT membership. The logos of these accreditation bodies will be featured on your premium certificate and transcript, ensuring credibility and professional recognition.

Yes, you will receive a free digital certificate automatically once you complete the course. If you would like a premium CPD-accredited certificate, either in digital or physical format, you can upgrade for a small fee.

View all

Course Features

Price

Original price was: £490.00.Current price is: £14.99.

Study Method

Online | Self-paced

Course Format

Reading Material - PDF, article

Duration

6 hours, 55 minutes

Qualification

No formal qualification

Certificate

At completion

Additional info

Coming soon

  • Share
Skills
Verify Your Certificate
tp-logo
Review on Trustpilot
Useful Links
Logo-White

With a team of industry professionals producing and delivering our course content, you can be sure the skills and knowledge you learn are applicable to your career aspirations, whether that’s in management, administration or beyond.

Facebook Twitter Linkedin Pinterest Instagram
© Course Line 2025

Share This Course