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Data Manipulation: Python, Numpy and Pandas Overview
Data Manipulation: Python, Numpy and Pandas Course is yet another “Teacher’s Choice” course from Teachers Training for a complete understanding of the fundamental topics. You are also entitled to exclusive tutor support and a professional CPD-accredited certificate in addition to the special discounted price for a limited time. Just like all our courses, this Data Manipulation: Python, Numpy and Pandas Course and its curriculum have also been designed by expert teachers so that teachers of tomorrow can learn from the best and equip themselves with all the necessary skills.
Consisting of several modules, the course teaches you everything you need to succeed in this profession.
The course can be studied part-time. You can become accredited within 05 Hours studying at your own pace. Your qualification will be recognised and can be checked for validity on our dedicated website.
Why Choose Teachers Training
Some of our website features are:
- This is a dedicated website for teaching
- 24/7 tutor support
- Interactive Content
- Affordable price
- Courses accredited by the UK's top awarding bodies
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Entry Requirements
No formal entry requirements. You need to have:
- Passion for learning
- A good understanding of the English language
- Be motivated and hard-working
- Over the age of 16.
Certification
CPD Certification from The Teachers Training
Successfully completing the MCQ exam of this course qualifies you for a CPD-accredited certificate from The Teachers Training. You will be eligible for both PDF copy and hard copy of the certificate to showcase your achievement however you wish.
- You can get your digital certificate (PDF) for £4.99 only
- Hard copy certificates are also available, and you can get one for only £10.99
- You can get both PDF and Hard copy certificates for just £12.99!
The certificate will add significant weight to your CV and will give you a competitive advantage when applying for jobs.
Course Curriculum
| Data Manipulation Python Numpy and Pandas | |||
| 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 | 00:04:00 | ||
| 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 | 00:05:00 | ||
| 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 | 00:01:00 | ||
| 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: Continuous & Continuous | 00:05:00 | ||
| 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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