Becoming a Data Analyst Using Python - Free Sample

Name: Becoming a Data Analyst Using Python - Free Sample
Rating: 4.5 (280 reviews)

A free sample of the course An introduction to data analysis using Python, from Scratch

Created byNajib Mozahem

Last updated 6/2023

English

English [Auto],

What you'll learn

Perform exploratory data analysis using the pandas library in python
Write code in python that will assist them in analyzing data
Produce visually appealing graphs that summarise their findings
Understand the data analysis process, from reading the data set to formatting the data, leading to producing summary statistics

Course content

4 sections • 22 lectures • 1h 51m total length

Introduction2:10
Read data sets, filter records with conditions, and create summary tables; produce graphs, including interactive geographic visuals, using Python and pandas with libraries for beginners.
Downloading the necessary program2:05
Run Python for data analysis using Jupyter notebooks or labs. Access online cloud options or install Anaconda to launch notebooks and manage libraries.
The Anaconda interface2:48
Launch the Anaconda navigator, a graphical user interface, then use Jupyter Lab to write and run code. Open or create a new Python notebook and write code from scratch.
Jupyter lab - navigating the user interface and executing code4:18
Navigate the Jupyter lab interface, understand notebooks and coding or markdown cells, run code with the play button or ctrl-enter, and organize cells to support data analysis.
Back to the Anaconda interface - viewing libraries3:22
Explore how to view and manage libraries in the Anaconda Navigator, install pandas and seaborn, and launch notebooks with Jupyter Lab.

First look at the Pandas library5:23
Learn how to start analyzing data with the Pandas library in Python, importing it as pd, and using read_csv to load a dataset from the web into a local variable.
First look at the data7:24
Using pandas, view the first five rows with head and inspect column types with dtypes to identify int, float, bool, and object.
Adjusting the name of the data set4:01
Explore how to inspect data with head and tail, view column names and types with dtypes, and rename and copy the dataset to Chicago Crimes.

Using the info() function6:38
Explore the dataset with the pandas info function to inspect column types, counts, and non-null values, revealing 22 columns, 238,317 rows, and non-null counts like 237,540 for location description.
Counting the null values10:44
Count null values in a data set with pandas by chaining isna() and sum, then compare to non-null counts. Identify columns with nulls, such as location description or x coordinate.
Zooming in to a specific row4:47
Use pandas dot loc to display a specific row or a range by index, noting zero-based indexing means the 10th row is index 9.
Zooming into a specific column6:29
Learn to zoom into a single column by taking a subset with the column name in square brackets. Display multiple columns using a Python array and double brackets.
Zooming in to a certain row and column6:38
Learn to combine column subsetting with the dot loc utility to view selected columns for chosen rows, count nulls with isna and sum, and treat subsets as independent datasets.
Removing rows and columns9:54
Learn to remove rows and columns in pandas using the drop function, control changes with inplace or assignment, and verify results with head, while reloading data with read_csv.

One condition - part 17:13
Learn to subset a dataset by a condition using Python. Use the double equals operator to check if the primary type equals theft and filter records accordingly.
One condition - part 26:41
Create a subset where primary type equals theft, save it as a new variable, and apply boolean vs object rules: use true/false without quotes for booleans and quotes for text.
Two conditions - the AND operator3:49
Learn to combine conditions with the and operator in Python to filter Chicago crimes data, requiring both the primary type is theft and the arrest to be true, using parentheses.
Two conditions - the OR operator2:49
Learn how the or operator in Python filters records by at least one true condition, selecting cases where arrest or domestic is true in a dataset.
Less than or equal to and greater than or equal to3:03
Learn to filter data with and, using greater than or equal to and less than or equal to on numeric columns like community area, ward, and x coordinate in Python.
Not equal to1:36
Learn to filter records not equal to a value using the Python not-equal operator, applied to the Chicago crimes dataset to exclude community area 23.
The isin() method6:34
Becoming a data analyst using Python: learn to filter records with multiple conditions using pandas isin, listing theft, battery, and assault in the Chicago crimes dataset.
The isna() method2:51
Learn to use the isna() and notna() functions to list records with null or non-null values, by filtering the location description column in the Chicago crimes dataset.

Requirements

No prior experience with python or data analysis is required. This course is for the absolute begginer.

Description

This course provides a free sample of the full course. You can enroll in this sample for free. If you like what you see, then you can enroll in the full course. This is done in order to help the students know exactly what the course is and the teaching style before having to pay any money.

The purpose of this course is to introduce non-technical students to data analysis using python. This course is for anyone who is interested in becoming a data analyst and has no experience at all with data analysis or with python. If you have no experience at all with python and with data analysis then this course is for you. At the start of the course the students will have no knowledge of data analysis or python. By the end of the course the student will be able to:

Use the Pandas library to read, modify, and analyze data
Install packages in python
Produce visually appealing graphs that help you tell a story
Uncover relationships between different types of variables
Understand how a data analyst thinks
Have a basic but solid understanding of how python can be used to analyze data
Create interactive maps
Manipulate the data so that it is in a proper format

The course starts from the very basics and slowly introduces the student to the most important functions and commands used in data analysis. This is done without relying heavily on programming. This course will show how to analyze data without having to write long lines of code.

Who this course is for:

Anyone would would like to take the first steps to become a data analyst

Becoming a Data Analyst Using Python - Free Sample

What you'll learn

Explore related topics

Course content

Introduction5 lectures • 15min

A First Look at the Pandas library3 lectures • 17min

Looking More Closely at the Data6 lectures • 45min

Using Conditions to Perform Operations8 lectures • 35min

Requirements

Description

Who this course is for: