Data Science

Master Data Science – No Prior Coding Needed!

Created byJewel Alam

Last updated 6/2025

English

What you'll learn

Memorize Python fundamentals for data analysis to help manage all departments of a startups and make smart business decisions
Execute data cleaning to prepare datasets for analysis, ensuring accuracy and consistency, which helps make informed decisions in startup management
Develop and use data manipulation techniques to transform, analyze, and optimize datasets for making data-driven decisions in business growth
Design and compare data visualizations to effectively communicate insights, make data-driven decisions and present information clearly in business strategies

Course content

12 sections • 155 lectures • 6h 2m total length

Introduction of the mentor0:31
This lecture provides an overview of my journey as a mentor, highlighting my background, educational qualifications, and key achievements

Introduction of the module3:29
In this section, students will explore Anaconda, including how to download and install it. They will also take a tour of Jupyter Notebook and learn about the print function, variables, data types, and operators.
Downloading Anaconda1:07
In this lecture, you will learn how to download Anaconda.
Installing Anaconda1:30
In this lecture, you will learn how to install Anaconda.
Opening Anaconda and Jupiter Notebook1:43
In this lecture, you will learn how to open Anaconda and Jupyter Notebook.
Tour of Jupiter Notebook2:11
In this lecture, you will take a tour of Jupyter Notebook.
Print function3:03
In this lecture, you will learn about the print function.
Variables introduction5:26
Variables1:57
In this lecture, you will learn about variables.
Variables 13:15
Variables 24:56
Variables 38:16
Variables
Variables
Assignment 2.1: Understanding Variable0:12
Coding excercise 2.1 : Variable and Printing0:07
This coding excercise is all about variable and printing
Data Types Introduction2:37
Data Types2:39
In this lecture, you will learn about data types.
List7:08
Tuples9:38
Dictionary9:08
Sets9:04
Dataframe3:16
Data Types
Data Types
Assignment 2.2: Data Types0:20
Coding exercise 2.2: Data Types0:10
Operators2:39
In this lecture, you will learn about operators.
Operators
Operators
Coding exercise 2.3: Basic Operators0:14
Project 1: Profit Calculator0:13

Introduction2:24
In this section, you will learn about various concepts such as if-else statements, for loops, and while loops.
If-Else Statements2:20
In this lecture, you will learn about if-else statements.
If -Else statement example2:40
If-Else Statement
If Else Statement
Assignment 3.1: If Else Statement0:16
Coding Exercise 3.1: If Else Statement0:11
For Loop3:23
In this lecture, you will learn about for loops.
For loop example2:31
For Loop
For Loop
For Else2:32
Assignment 3.2: For Loop0:14
Coding Exercise 3.2: For Loop0:12
While Loop2:43
In this lecture, you will learn about while loops.
While Loop Example2:31
While Loop
While Loop
Assignment 3.3: While Loop0:15
Coding Exercise 3.3: While Loop0:12
Example of combining loops with If statement2:33
Assignment 3.4: Combining Loops with If Statement0:18
Coding Exercise 3.4: Loops with condition0:17
Summary 12:11
This is the summary of the section: the concepts covered include if-else statements, for loops, and while loops.
Summery 22:52

Introduction1:49
This lecture focuses on the introduction of this module.
Pandas library2:00
This lecture is all about the introduction of pandas library.
Pandas- Managing Data Like a Pro
Pandas – Managing Data Like a Pro
Matplotlib library1:52
This lecture is all about the introduction of matplotlib library.
Matplotlib – Visualizing Data
Matplotlib – Visualizing Data
NumPy library1:57
This lecture is all about the introduction of NumPy library.
NumPy – Handling Numbers Efficiently
NumPy – Handling Numbers Efficiently
Seaborn library2:02
This lecture is all about the introduction of Seaborn library.
Seaborn – Beautiful and Informative Charts
Seaborn – Beautiful and Informative Charts
Revision of this module2:24
This lecture is all about the revision of this complete module

Introduction2:55
This lecture is all about the introduction of this module
Import Datasets0:40
This lecture is all about the import of dataset.
Importing Libraries and Datasets3:36
This lecture is all about the importing libraries and dataset
Importing Libraries and Datasets
Importing Libraries and Datasets
Assignment 5.1 : Importing libraries and datasets0:13
Viewing All Rows and Columns2:37
This lecture is all about viewing all rows and columns
Viewing Rows and Columns
Viewing Rows and Columns
Coding Exercise 5.1: Dataset dimensions0:09
Viewing the First 8 Rows2:49
This lecture is all about viewing the first 8 rows
Coding Exercise 5.2: Preview Dataset0:05
Checking Full Information of the DataFrame3:27
This lecture is all about checking full information of the dataset
Checking full information of the dataframe
Checking full information of the dataframe
Assignment 5.2: Inspecting the Dataset0:13
Coding Exercise 5.3: Dataset Summery0:07
Conclusion3:24
This lecture is all about the conclusion of this module

Introduction of this module3:57
Unique function and Describe function5:05
This lecture is all about the unique function and the describe function
Unique function
describe function
Assignment 6.1 : Analyzing Columns0:07
Assignment 6.2: Analyzing Columns0:06
Coding Exercise 6.1 : Inspecting Columns0:26
Max and min function3:18
This lecture is all about Max and Min function
Assignment 6.3: Max and Min Functions0:08
Assignment 6.3: Explaination or the previous assignment on Max and Min Functions0:38
Coding Excercise 6.2: Finding Maximum and Minimum0:24
Creating a Subset from the Existing Dataset3:27
This lecture is all about creating a subset from the existing dataset
Assignment 6.4: Creating a Subset0:09
Assignment 6.4: Creating a Subset0:05
Coding Excercise 6.3: Creating Subset0:32
Checking the Number of Rows in the Subset3:10
This lecture is all about checking the number of Rows in the subset
Dataframe Subset
Applying Conditions to FIlter Data2:44
This lecture is all about applying conditions to filtered data
Extract Columns with a Single Condition3:27
This lecture is all about extracting columns with a single condition
loc function
Assignment 6.5: Filtering Rows with Conditions0:05
Assignment 6.6: Filtering Rows with Conditions0:05
Coding Exercise 6.4: Filtering Data with Conditions0:31
Extracting columns with multiple conditions3:25
This lecture is all about extracting columns with multiple conditions
Multiple conditions , AND
Assignment 6.7: Combining Conditions0:10
Coding Exercise 6.5: Applying Multiple Conditions0:30
Extract columns with conditions on different columns2:41
This lecture is all about extracting columns with conditions on different columns
Summary of this module5:25
This is the summery of this module

Introduction4:34
Selecting Data3:13
This lecture is all about selecting data
Function
Assignment 7.1.1 : Selecting Data0:13
Assignment 7.1.2: Selecting Data0:13
Coding Exercise 7.0: Selecting Specific Rows and Columns0:14
Using "where" for Conditional Selection3:51
This lecture is all about using " Were " " for conditional selection
Where function
Assignment 7.2.1 : Using where for conditional Selection0:26
Assignment 7.2.2: Using where for Conditional Selection0:18
Coding Exercise 7.2: Conditional Selection with where0:15
Adding New Columns with dataframe.assign()4:06
This lecture is all about adding new columns with dataframe.assign()
Assign function
Assignment 7.3: Adding New Columns0:17
Using "AND" for Multiple Conditions4:05
This lecture is all about using "AND" for multiple conditions
Coding Exercise 7.4: Filtering Data with AND Conditions0:12
Using "OR" for Multiple Conditions3:57
This lecture is all about using "OR" for multiple conditions
Conditions, OR
Conditions
Assignment 7.4.1: Using AND and OR for Conditions0:14
Assignment 7.4.2: Using AND and OR for Conditions0:14
Coding Exercise 7.5: Filtering Data with OR Conditions0:14
Summery4:56
This lecture is all about the summery of the whole module

Introduction4:30
This lecture is all about the introduction of this module
Group by with size function3:05
This lecture is all about the combination of group by function along with size function
groupby function
df.groupby("Gender").size()
Assignment 8.1: Understanding group by Basics0:24
Coding Exercise 8.1: Basic Grouping0:17
Group By with count function3:05
This lecture is all about the group by function in combination with count function
Difference between size() and count() when used with groupby
Assignment 8.2: Counting Values Across Groups0:22
Group By with individual column3:40
This lecture is all about the application of group by function and the count function on a specific column
Groupby
Assignment 8.3: Analyzing Specific Columns in Groups0:23
Coding Exercise 8.2: Counting Items by Color0:15
Summery2:38
This lecture is all about the summery of this module

Requirements

No programming experience needed. You will learn everything you need to know

Description

Master Data Like a Pro - Ethically and Effectively

In today's data-driven world, these skills separate the competitive from the obsolete. Whether you're:

A student seeking an edge in the job market
A freelancer expanding your service offerings
A startup founder making crucial business decisions
A professional transitioning to data-focused roles
A curious learner wanting to understand our data-powered world
Data skills are no longer optional - they're essential for anyone who wants to stay competitive. Whether you're:
- A student looking to boost your employability
- A freelancer wanting to offer more valuable services
- A startup founder needing to make smarter decisions
- A professional aiming to transition into data-related roles
This course gives you real, practical skills you can actually use. You'll learn how to:
1. Transform raw data into meaningful insights
2. Create clear, impactful visualizations
3. Ask the right questions of your data
4. Make confident, data-backed decisions
What makes this different?
1. No unrealistic promises - just solid, foundational skills
2. No "get rich quick" claims - real career-building knowledge
3. No tool hype - focus on transferable concepts that last
You'll walk away with:
- A portfolio of practical projects
- Confidence working with data
- Skills that apply across industries
- A certificate to validate your learning
This isn't magic - it's education. The only requirement is your willingness to learn. Enroll today and start building skills that will serve you for years to come.
Preview available now - see for yourself if it's right for you.

Who this course is for:

Startup founders
Business owners
Working Professionals
College Students
Side Hustlers
Freelancers
Data Analyst: complete Beginners
Individuals from both technical and non-technical backgrounds

Data Science

What you'll learn

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Course content

Introduction1 lecture • 1min

Python fundamentals: Introduction25 lectures • 1hr 24min

Python fundamentals: Loops19 lectures • 31min

Python Fundamentals: Functions10 lectures • 31min

Introduction: Numpy, Pandas and Matplotlib6 lectures • 12min

Data Analysis: Dataset12 lectures • 20min

Data Analysis: 124 lectures • 41min

Data Analysis 218 lectures • 32min

Data Analysis 310 lectures • 19min

Google Collab Tour1 lecture • 6min

Requirements

Description

Who this course is for: