13 Python Data Analytics Real World Hands-on Projects

About 13 Python Data Analytics Real World Hands-on Projects

In this comprehensive course, we present to you

13 Data Analytics projects

solved using Python

, a language renowned for its versatility and effectiveness in the realm of data analysis. These projects serve as an invaluable resource for individuals embarking on their journey towards a

career in Data Science domain

, offering practical insights and hands-on experience essential for success in the field. Moreover, for those contemplating a

transition into the dynamic and rewarding domain of data analytics

, these projects provide a solid foundation, equipping learners with the requisite skills and knowledge. Designed with students in mind, these projects are

not only educational but also serve as potential submissions for academic institutions

. As part of our commitment to fostering a supportive learning environment, we provide

access to the source codes and datasets

for all projects. Each project is accompanied by

clear and concise explanations

, ensuring accessibility for learners of all levels. Central to the completion of these projects is the utilization of the

Python Pandas Library

, a powerful toolset for data manipulation and analysis. Now, let's delve into the diverse array of projects awaiting you: Project 1 - Weather Data Analysis Project 2 - Cars Data Analysis Project 3 - Police Data Analysis Project 4 - Covid Data Analysis Project 5 - London Housing Data Analysis Project 6 - Census Data Analysis Project 7 - Udemy Data Analysis Project 8 - Netflix Data Analysis Project 9 - Sales Data Analysis Project 10 - Spotify & YouTube Data Analysis Project 11 - Airlines' Flights Data Analysis Project 12 - AI Financial Market Data Analysis Project 13 - HR Data Analysis Some examples of commands used in these projects are :

reset_index() - To convert the index of a Series into a column to form a DataFrame.

loc[ ] - To show any row's values.

info() - To provide the basic information about the dataframe.

drop() - To drop any column or row from the dataframe.

str.strip().str.replace(r'\s+', ' ', regex=True) - To remove extra spaces in any text column.

duplicated() - To show all the duplicate records from a dataframe.

drop_duplicates(inplace=True) - To remove the duplicate records from the dataframe.

round() - To round-off the values of a numerical column.

to_datetime() - To convert the datatype of date column into datetime format.

groupby() - To make the group of all unique values of a column.

std() - To check the standard deviation of any numerical column.

var() - To check the variance of any numerical column.

mean() - To check the mean of any numerical column.

agg() - Using agg() with groupby().

head() - It shows the first N rows in the data (by default, N=5).

columns - To show all the column names of the dataframe.

unique() - In a column, it shows all the unique values. It can be applied on a single column only, not on the whole dataframe.

nunique() - It shows the total no. of unique values in each column. It can be applied on a single column as well as on the whole dataframe.

describe() - To show some summary about the columns.

astype() - To change the datatype of any column.

dtype - To check the datatype of any column.

value_counts - In a column, it shows all the unique values with their count. It can be applied on a single column only.

plot(kind='bar') - To draw the bar graph.

type() - To the type of any variable.

plt.figure(figsize = ()) - To set the size of any figure.

plt.title(), plt.xlabel(), plt.ylabel() - To set the Title, x-axis label, y-axis label.

sort_values(ascending = False) - To sort the values in descending order.

dt.month - To create a new column showing Month only.

shape - It shows the total no. of rows and no. of columns of the dataframe

index - This attribute provides the index of the dataframe

dtypes - It shows the data-type of each column

count - It shows the total no. of non-null values in each column. It can be applied on a single column as well as on the whole dataframe.

isnull( ) - To show where Null value is present.

dropna( ) - It drops the rows that contains all missing values.

isin( ) - To show all records including particular elements.

str.contains( ) - To get all records that contains a given string.

str.split( ) - It splits a column's string into different columns.

dt.year.value_counts( ) - It counts the occurrence of all individual years in Time column.

sns.countplot(df['Col_name']) - To show the count of all unique values of any column in the form of bar graph.

max( ), min( ) - It shows the maximum/minimum value of the series Through these projects and commands, learners will not only acquire essential skills in data analysis but also gain a deeper understanding of the underlying principles and methodologies driving the field of data analytics. Whether you're pursuing a career as a Data Analyst, seeking to enhance your academic portfolio, or simply eager to expand your knowledge and skills in Python-based data analysis, this course is tailored to meet your needs and aspirations.