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About This Course
- Overview
Educational accomplishments and income are closely correlated. Education and skill with endorsed certificates from credible and renowned authorities typically lead to better jobs with attractive salaries. Educated and skilled workers also have lower rates of unemployment. Therefore, skills and endorsed certificates to showcase are essential for people early in their careers.
- Why Choose Imperial Academy?
Imperial Academy offers this premium Data Manipulation in Python: Master Python, Numpy & Pandas course to ensure security in your career. In addition, this comprehensive Data Manipulation in Python: Master Python, Numpy & Pandas course will assist you in building relevant skills that will help you find a job in the related sectors. Also, the Certificate you’ll get after completing the Data Manipulation in Python: Master Python, Numpy & Pandas will put your head and shoulder above others in front of potential employers.
Become the person who would attract the results you seek. What you plant now, you will harvest later. So, grab this opportunity and start learning Data Manipulation in Python: Master Python, Numpy & Pandas!
- What Imperial Academy Offers You
- QLS/ CPD/ CIQ Accredited
- 24/7 Assistance from our Support Team
- 100% Online
- Self-paced course
- Bite-sized Audio-visual Modules
- Rich Learning Materials
- Developed by Industry Specialists
- Career Guidance
- Course Design
Learn at your own pace from the comfort of your home, as the rich learning materials of this premium course is accessible from any place at any time. The advanced course curriculums are divided into tiny bite-sized audio-visual modules by industry specialists with years of experience behind them.
- Audio-visual Lessons
- Online Study Materials
Course Curriculum
-
Welcome to the course!
00:01:00 -
Introduction to Python
00:01:00
-
Course Materials
-
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
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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
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Sequences: Tuples
00:01:00
-
Functions: Built-in Functions
00:01:00
-
Functions: User-defined Functions
00:04:00
-
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
-
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
-
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
-
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
-
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
-
Introduction
00:01:00
-
Setting Up Matplotlib
00:01:00
-
Plotting Line Plots using Matplotlib
00:02:00
-
Plotting Line Plots using Matplotlib
00:02:00
-
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
-
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
-
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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