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Data Manipulation in Python: Master Python, Numpy & Pandas teaches you everything on the topic thoroughly from scratch so you can achieve a professional certificate for free to showcase your achievement in professional life. This Data Manipulation in Python: Master Python, Numpy & Pandas is a comprehensive, instructor-guided course, designed to provide a detailed understanding of the nature of the related sector and your key roles within it.
To become successful in your profession, you must have a specific set of skills to succeed in today’s competitive world. In this in-depth training course, you will develop the most in-demand skills to kickstart your career, as well as upgrade your existing knowledge & skills.
The training materials of this course are available online for you to learn at your own pace and fast-track your career with ease.
- Accredited by CPD
- Instant e-certificate and hard copy dispatch by next working day
- Fully online, interactive course with audio voiceover
- Self-paced learning and laptop, tablet, smartphone-friendly
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Sneak Peek
Who should take the course
Anyone with a knack for learning new skills can take this Data Manipulation in Python: Master Python, Numpy & Pandas. While this comprehensive training is popular for preparing people for job opportunities in the relevant fields, it also helps to advance your career for promotions.
Certification
Once you’ve successfully completed your course, you will immediately be sent a digital certificate. Also, you can have your printed certificate delivered by post (shipping cost £3.99). All of our courses are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.
Accreditation
All of our courses, including this Data Manipulation in Python: Master Python, Numpy & Pandas, are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field.
Course Curriculum
The detailed curriculum outline of our Data Manipulation in Python: Master Python, Numpy & Pandas is as follows:
- Welcome to the course!
- Introduction to Python
- Course Materials
- Setting up Python
- What is Jupyter?
- Anaconda Installation: Windows, Mac & Ubuntu
- How to implement Python in Jupyter?
- Managing Directories in Jupyter Notebook
- Input/Output
- Working with different datatypes
- Variables
- Arithmetic Operators
- Comparison Operators
- Logical Operators
- Conditional statements
- Loops
- Sequences: Lists
- Sequences: Dictionaries
- Sequences: Tuples
- Functions: Built-in Functions
- Functions: User-defined Functions
- Installing Libraries
- Importing Libraries
- Pandas Library for Data Science
- NumPy Library for Data Science
- Pandas vs NumPy
- Matplotlib Library for Data Science
- Seaborn Library for Data Science
- Installing Libraries
- Importing Libraries
- Pandas Library for Data Science
- NumPy Library for Data Science
- Pandas vs NumPy
- Matplotlib Library for Data Science
- Seaborn Library for Data Science
- Basic NumPy arrays: zeros()
- Basic NumPy arrays: ones()
- Basic NumPy arrays: full()
- Adding a scalar
- Subtracting a scalar
- Multiplying by a scalar
- Dividing by a scalar
- Raise to a power
- Transpose
- Element wise addition
- Element wise subtraction
- Element wise multiplication
- Element wise division
- Matrix multiplication
- Statistics
- What is a Python Pandas DataFrame?
- What is a Python Pandas Series?
- DataFrame vs Series
- Creating a DataFrame using lists
- Creating a DataFrame using a dictionary
- Loading CSV data into python
- Changing the Index Column
- Inplace
- Examining the DataFrame: Head & Tail
- Statistical summary of the DataFrame
- Slicing rows using bracket operators
- Indexing columns using bracket operators
- Boolean list
- Filtering Rows
- Filtering rows using & and | operators
- Filtering data using loc()
- Filtering data using iloc()
- Adding and deleting rows and columns
- Sorting Values
- Exporting and saving pandas DataFrames
- Concatenating DataFrames
- groupby()
- Introduction to Data Cleaning
- Quality of Data
- Examples of Anomalies
- Median-based Anomaly Detection
- Mean-based anomaly detection
- Z-score-based Anomaly Detection
- Interquartile Range for Anomaly Detection
- Dealing with missing values
- Regular Expressions
- Feature Scaling
- Introduction
- Setting Up Matplotlib
- Plotting Line Plots using Matplotlib
- Title, Labels & Legend
- Plotting Histograms
- Plotting Bar Charts
- Plotting Pie Charts
- Plotting Scatter Plots
- Plotting Log Plots
- Plotting Polar Plots
- Handling Dates
- Creating multiple subplots in one figure
- Introduction
- What is Exploratory Data Analysis?
- Univariate Analysis
- Univariate Analysis: Continuous Data
- Univariate Analysis: Categorical Data
- Bivariate analysis: Categorical & Categorical
- Bivariate analysis: Continuous & Categorical
- Detecting Outliers
- Categorical Variable Transformation
- Introduction to Time Series
- Getting Stock Data using Yfinance
- Converting a Dataset into Time Series
- Working with Time Series
- Time Series Data Visualization with Python
Course Curriculum
| Python Quick Refresher (Optional) | |||
| Welcome to the course! | 00:01:00 | ||
| Introduction to Python | 00:01:00 | ||
| Course Materials | 00:00:00 | ||
| Setting up Python | 00:02:00 | ||
| What is Jupyter? | 00:01:00 | ||
| Anaconda Installation: Windows, Mac & Ubuntu | 00:01:00 | ||
| How to implement Python in Jupyter? | 00:01:00 | ||
| Managing Directories in Jupyter Notebook | 00:03:00 | ||
| Input/Output | 00:02:00 | ||
| Working with different datatypes | 00:01:00 | ||
| Variables | 00:02:00 | ||
| Arithmetic Operators | 00:02:00 | ||
| Comparison Operators | 00:01:00 | ||
| Logical Operators | 00:03:00 | ||
| Conditional statements | 00:02:00 | ||
| Loops | 00:04:00 | ||
| Sequences: Lists | 00:03:00 | ||
| Sequences: Dictionaries | 00:03:00 | ||
| Sequences: Tuples | 00:01:00 | ||
| Functions: Built-in Functions | 00:01:00 | ||
| Functions: User-defined Functions | 00:03:00 | ||
| Essential Python Libraries For Data Science | |||
| Installing Libraries | 00:01:00 | ||
| Importing Libraries | 00:02:00 | ||
| Pandas Library for Data Science | 00:01:00 | ||
| NumPy Library for Data Science | 00:15:00 | ||
| Pandas vs NumPy | 00:01:00 | ||
| Matplotlib Library for Data Science | 00:01:00 | ||
| Seaborn Library for Data Science | 00:01:00 | ||
| Fundamental NumPy Properties | |||
| Introduction to NumPy arrays | 00:01:00 | ||
| Creating NumPy arrays | 00:06:00 | ||
| Indexing NumPy arrays | 00:06:00 | ||
| Array shape | 00:01:00 | ||
| Iterating Over NumPy Arrays | 00:05:00 | ||
| Mathematics For Data Science | |||
| Basic NumPy arrays: zeros() | 00:02:00 | ||
| Basic NumPy arrays: ones() | 00:01:00 | ||
| Basic NumPy arrays: full() | 00:01:00 | ||
| Adding a scalar | 00:02:00 | ||
| Subtracting a scalar | 00:01:00 | ||
| Multiplying by a scalar | 00:01:00 | ||
| Dividing by a scalar | 00:01:00 | ||
| Raise to a power | 00:01:00 | ||
| Transpose | 00:01:00 | ||
| Element wise addition | 00:02:00 | ||
| Element wise subtraction | 00:01:00 | ||
| Element wise multiplication | 00:01:00 | ||
| Element wise division | 00:01:00 | ||
| Matrix multiplication | 00:02:00 | ||
| Statistics | 00:03:00 | ||
| Python Pandas DataFrames & Series | |||
| What is a Python Pandas DataFrame? | 00:01:00 | ||
| What is a Python Pandas Series? | 00:01:00 | ||
| DataFrame vs Series | 00:01:00 | ||
| Creating a DataFrame using lists | 00:03:00 | ||
| Creating a DataFrame using a dictionary | 00:01:00 | ||
| Loading CSV data into python | 00:02:00 | ||
| Changing the Index Column | 00:01:00 | ||
| Inplace | 00:01:00 | ||
| Examining the DataFrame: Head & Tail | 00:01:00 | ||
| Statistical summary of the DataFrame | 00:01:00 | ||
| Slicing rows using bracket operators | 00:01:00 | ||
| Indexing columns using bracket operators | 00:01:00 | ||
| Boolean list | 00:01:00 | ||
| Filtering Rows | 00:01:00 | ||
| Filtering rows using & and | operators | 00:02:00 | ||
| Filtering data using loc() | 00:04:00 | ||
| Filtering data using iloc() | 00:02:00 | ||
| Adding and deleting rows and columns | 00:03:00 | ||
| Sorting Values | 00:02:00 | ||
| Exporting and saving pandas DataFrames | 00:02:00 | ||
| Concatenating DataFrames | 00:01:00 | ||
| groupby() | 00:03:00 | ||
| Data Cleaning | |||
| Introduction to Data Cleaning | 00:01:00 | ||
| Quality of Data | 00:01:00 | ||
| Examples of Anomalies | 00:01:00 | ||
| Median-based Anomaly Detection | 00:03:00 | ||
| Mean-based anomaly detection | 00:03:00 | ||
| Z-score-based Anomaly Detection | 00:03:00 | ||
| Interquartile Range for Anomaly Detection | 00:05:00 | ||
| Dealing with missing values | 00:06:00 | ||
| Regular Expressions | 00:07:00 | ||
| Feature Scaling | 00:03:00 | ||
| Data Visualization Using Python | |||
| Introduction | 00:01:00 | ||
| Setting Up Matplotlib | 00:01:00 | ||
| Plotting Line Plots using Matplotlib | 00:02:00 | ||
| Title, Labels & Legend | 00:07:00 | ||
| Plotting Histograms | 00:01:00 | ||
| Plotting Bar Charts | 00:02:00 | ||
| Plotting Pie Charts | 00:03:00 | ||
| Plotting Scatter Plots | 00:06:00 | ||
| Plotting Log Plots | 00:01:00 | ||
| Plotting Polar Plots | 00:02:00 | ||
| Handling Dates | 00:01:00 | ||
| Creating multiple subplots in one figure | 00:03:00 | ||
| Exploratory Data Analysis | |||
| Introduction | 00:01:00 | ||
| What is Exploratory Data Analysis? | 00:01:00 | ||
| Univariate Analysis | 00:02:00 | ||
| Univariate Analysis: Continuous Data | 00:06:00 | ||
| Univariate Analysis: Categorical Data | 00:02:00 | ||
| Bivariate analysis: Categorical & Categorical | 00:03:00 | ||
| Bivariate analysis: Continuous & Categorical | 00:02:00 | ||
| Detecting Outliers | 00:06:00 | ||
| Categorical Variable Transformation | 00:04:00 | ||
| Time Series In Python | |||
| Introduction to Time Series | 00:02:00 | ||
| Getting Stock Data using Yfinance | 00:03:00 | ||
| Converting a Dataset into Time Series | 00:04:00 | ||
| Working with Time Series | 00:04:00 | ||
| Time Series Data Visualization with Python | 00:03:00 | ||
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