Complete Python Data Analytics Bootcamp in 2025

Name: Complete Python Data Analytics Bootcamp in 2025
Rating: 4.3 (54 reviews)

Master Python for A-Z Data Analysis and Become Pro Data Analyst with Basics to Hands-on Coding Exercises and Assignments

Created byShahriar's Analytical Academy

Last updated 3/2025

English

What you'll learn

You will get proficient in Python for thorough data analysis. Prepare for a career as a data analyst by acquiring practical skills and expertise.
You will master the fundamentals of data analytics, including facts and theories, statistical analysis, hypothesis testing, and machine learning.
You will learn the important Python programming basics such as variables naming, data types, lists, dictionaries, dataframes, sets, loops, functions etc.
You will master a range of methods and techniques for data cleaning, sorting, filtering, data manipulation, transformation, and data preprocessing in Python.
You will learn to use Python for data visualizations, exploratory data analysis, statistical analysis, hypothesis testing methods and machine learning models.
You will work on practical data analysis projects to apply learned skills. Enhance problem-solving abilities through hands-on data analysis exercises.
You will pass practical assignments, 85+ coding exercises, 10 quizzes with 100+ questions, on all the topics over the entire course.
You will accomplish one capstone project on Sport data analysis at the end to get the full view of data analysis workflow in Python.

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

Course content

26 sections • 126 lectures • 14h 29m total length

Instructions to accomplish the course0:44
Python cheatsheet for data analysis0:08
Things make you complete data analyst!0:56
Resources used in the course0:12

Various aspects of data cleaning14:12
Various aspects of Joining datasets8:50
Methods of exploratory data analysis Part 112:15
Methods of exploratory data analysis Part 211:17
Methods of exploratory data analysis Part 315:36
Explore exploratory data analysis techniques, including frequency and percentage analysis, group by analysis, cross tabulation, and correlation using scatter plots and Pearson's r.
Data analysis techniques

Population v/s sample and its methods11:05
Types of statistical data analysis4:58
Describe descriptive and inferential statistics, using metrics like mean, median, mode, and standard deviation. Inferential analysis uses samples to infer population trends via tests such as a t test.
A Recap on descriptive statistics methods3:14
Inferential statistics Part 1 – T-tests and ANOVA6:54
Explore inferential statistics by applying one sample t test, independent and paired t tests, and one-way ANOVA to compare means across single and multiple groups.
Inferential statistics Part 2 – Relationships measures3:16
Inferential statistics Part 3 – Linear regression11:53
Statistical Data Analysis Methods

Hypothesis testing for inferential statistics6:03
Explore how hypothesis testing uses sample data to infer population parameters, compare groups, assess evidence with p-values, and decide on null versus alternative hypotheses.
Selecting statistical test and assumption testing10:26
Select the right statistical test by aligning the scenario and hypotheses, then apply the appropriate tests (t tests, anova, chi-square, Pearson correlation, or regression) and check core assumptions.
Confidence level, significance level, p-value3:37
Making decision and conclusion on findings2:55
A-Z statistical analysis and hypothesis testing7:01
The concept of Hypothesis testing

Getting started with first python code4:36
Printing function
Assigning variable names correctly9:05
Learn how to define Python variables with the assignment operator, store strings and numbers, and follow naming conventions using letters, digits, and underscores, while respecting case sensitivity and reserved keywords.
Creating variables
Various data types and data structures7:22
Converting and casting data types9:49
Learn how to convert Python data types—int, float, and string—using int(), float(), and str(), preparing data for analysis and arithmetic in data frames.
Converting data types #1
Converting data types #2
Starting with Variables to Data Types0:03

Arithmetic operators (+, -, *, /, %, **)6:44
Learn how Python's arithmetic operators—plus, minus, multiply, divide, modulus, and exponent—perform numeric calculations, from simple additions to remainders and powers.
Arithmetic operation #1
Arithmetic operation #2
Comparison operators (>, <, >=, <=, ==, !=)7:35
Explore Python comparison operators such as greater than, less than, greater than or equal to, less than or equal to, equal to, and not equal to, and their boolean outputs.
Comparison operation #1
Comparison operation #2
Logical operators (and, or, not)7:06
Operators in Python Programming0:03

Requirements

Access to computer and internet
Basic computer literacy
No coding experience required

Description

Welcome to the Become A Complete Python Data Analyst in 2025! In this comprehensive course, you'll embark on a journey from Python novice to proficient data analyst, equipped with the essential skills and knowledge to excel in the field.

Throughout this course, you will delve deep into the realm of Python programming, focusing on its application in data analysis. Starting from the basics, you'll master fundamental concepts such as variable naming, data types, lists, dictionaries, dataframes, sets, loops, and functions. With a solid foundation in Python, you'll seamlessly transition to advanced topics, including data cleaning, sorting, filtering, manipulation, transformation, and preprocessing.

But that's not all. As you progress, you'll learn how to harness the power of Python for data visualization, exploratory data analysis, statistical analysis, hypothesis testing, and even delve into the exciting world of machine learning. Through a combination of theoretical understanding and hands-on practice, you'll gain proficiency in a wide range of methods and techniques essential for data analysis.

What sets this course apart is its emphasis on practical application. You won't just learn the theory; you'll put your newfound knowledge to the test through practical data analysis projects and hands-on exercises. With over 85 coding exercises, 10 quizzes featuring 100+ questions, and practical assignments covering all topics, you'll have ample opportunities to reinforce your skills and enhance your problem-solving abilities.

As the culmination of your journey, you'll undertake a capstone project focused on sports data analysis. This final project will allow you to apply all the skills you've acquired throughout the course, providing you with a comprehensive understanding of the data analysis workflow in Python.

Whether you're a seasoned professional looking to upskill or someone just starting their journey in data analysis, this course is designed to equip you with the expertise and confidence needed to succeed. Join us on this exciting adventure and unlock your potential as a data analyst in Python.

Who this course is for:

Those who are highly interested in learning complete data analytics using Python.
This course is NOT for those who are interested to learn data science or advanced machine learning application.

Complete Python Data Analytics Bootcamp in 2025

What you'll learn

Explore related topics

Coding Exercises

Course content

Start Here: MUST Follow the Instructions4 lectures • 2min

Data Analysis and Its Application2 lectures • 12min

Data Analysis Tools, Techniques and Methods5 lectures • 1hr 2min

Statistical Analysis Methods and Techniques6 lectures • 41min

Clarifying the Concept of Hypothesis Testing5 lectures • 30min

Data Transformation and Visualisation Methods5 lectures • 27min

Data Modeling with Machine Learning Model3 lectures • 25min

Setting Up Python and Jupyter Notebook3 lectures • 1min

Starting with Variables to Data Types5 lectures • 31min

Various Operators in Python Programming4 lectures • 21min

Requirements

Description

Who this course is for: