Data Manipulation in Python: Master Python, Numpy & Pandas

Data Manipulation in Python: Master Python, Numpy & Pandas

Data Manipulation in Python: Master Python, Numpy & Pandas

Learn Python, NumPy & Pandas for Data Science: Master essential data manipulation for data science in python

Language: english

Note: 4.4/5 (1,123 notes) 93,531 students

Instructor(s): Meta Brains

Last update: 2022-04-24

What you’ll learn

  • Learn to use Pandas for Data Analysis
  • Learn to work with numerical data in Python
  • Learn statistics and math with Python
  • Learn how to code in Jupyter Notebook
  • Learn how to install packages in Python

 

Requirements

  • No prior data science knowledge required
  • No programming experience needed

 

Description

When it comes to being attractive, data scientists are already there. In a highly competitive job market, it is tough to keep them after they have been hired. People with a unique mix of scientific training, computer expertise, and analytical abilities are hard to find.

Like the Wall Street “quants” of the 1980s and 1990s, modern-day data scientists are expected to have a similar skill set. People with a background in physics and mathematics flocked to investment banks and hedge funds in those days because they could come up with novel algorithms and data methods.


That being said, data science is becoming one of the most well-suited occupations for success in the twenty-first century. It is computerized, programming-driven, and analytical in nature. Consequently, it comes as no surprise that the need for data scientists has been increasing in the employment market over the last several years.

The supply, on the other hand, has been quite restricted. It is challenging to get the knowledge and abilities required to be recruited as a data scientist.

Lots of resources for learning Python are available online. Because of this, students frequently get overwhelmed by Python’s high learning curve.


It’s a whole new ball game in here! Step-by-step instruction is the hallmark of this course. Throughout each subsequent lesson, we continue to build on what we’ve previously learned. Our goal is to equip you with all the tools and skills you need to master Python, Numpy & Pandas.

You’ll walk away from each video with a fresh idea that you can put to use right away!

All skill levels are welcome in this course, and even if you have no prior programming or statistical experience, you will be able to succeed!

 

Who this course is for

  • No previous skills or expertise required. Only a drive to succeed!

 

Course content

  • Python Quick Refresher (Optional)
    • 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
    • Quiz 1
    • Working with different datatypes
    • Variables
    • Quiz 2
    • Quiz 3
    • Arithmetic Operators
    • Quiz 4
    • Quiz 5
    • Quiz 6
    • Comparison Operators
    • Logical Operators
    • Quiz 7
    • Quiz 8
    • Quiz 9
    • Conditional statements
    • Loops
    • Sequences: Lists
    • Sequences: Dictionaries
    • Sequences: Tuples
    • Quiz 10
    • Quiz 11
    • Quiz 12
    • Functions: Built-in Functions
    • Functions: User-defined Functions
    • Quiz 13
    • Quiz 14
  • Essential Python Libraries 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
  • Fundamental NumPy Properties
    • Introduction to NumPy arrays
    • Creating NumPy arrays
    • Quiz 15
    • Indexing NumPy arrays
    • Quiz 16
    • Array shape
    • Iterating Over NumPy Arrays
  • Mathematics for Data Science
    • Basic NumPy arrays: zeros()
    • Basic NumPy arrays: ones()
    • Basic NumPy arrays: full()
    • Quiz 17
    • 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
    • Quiz 18
    • Statistics
  • Python Pandas DataFrames & Series
    • 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
    • Quiz 19
    • Indexing columns using bracket operators
    • Boolean list
    • Filtering Rows
    • Filtering rows using & and | operators
    • Filtering data using loc()
    • Quiz 20
    • Filtering data using iloc()
    • Quiz 21
    • Quiz 22
    • Adding and deleting rows and columns
    • Sorting Values
    • Exporting and saving pandas DataFrames
    • Concatenating DataFrames
    • groupby()
  • Data Cleaning
    • 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
  • Data Visualization using Python
    • 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
  • Exploratory Data Analysis
    • Introduction
    • What is Exploratory Data Analysis?
    • Univariate Analysis
    • Univariate Analysis: Continuous Data
    • Univariate Analysis: Categorical Data
    • Bivariate analysis: Continuous & Continuous
    • Bivariate analysis: Categorical & Categorical
    • Bivariate analysis: Continuous & Categorical
    • Detecting Outliers
    • Categorical Variable Transformation
  • Time Series in Python
    • 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

 

Data Manipulation in Python: Master Python, Numpy & PandasData Manipulation in Python: Master Python, Numpy & Pandas

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