Python for Data Science : Database and Visualization

Name: Python for Data Science : Database and Visualization
Rating: 5.0 (2 reviews)

Master NumPy, Jupyter, Pandas, Matplotlib, MySQL and MongoDB from scratch.

Created byAI Practical Courses Nirmal Joshi

Last updated 9/2026

English

What you'll learn

Master Data Science Fundamentals: Understand the role of a data scientist and how to set up a professional data science environment.
Perform High-Performance Vector Computing: Use NumPy to handle multi-dimensional arrays, perform fast mathematical operations, and manipulate numerical data.
Connect Python with Relational Databases: Write Python scripts to connect to MySQL, run SQL queries, and extract structured data for analysis.
Create Data Visualizations: Use Matplotlib to build clear, impactful charts (line, bar, scatter, histograms) to communicate your data insights.
Navigate Jupyter Notebooks: Learn to write, debug, and document your Python code efficiently using Jupyter's powerful interactive environment.
Manipulate and Analyze Complex Datasets: Master Pandas to clean messy data, handle missing values, filter datasets, and perform advanced data aggregations.
Work with NoSQL and Unstructured Data: Connect Python to MongoDB to store, query, and manage flexible, document-based JSON/BSON data.

Course content

7 sections • 164 lectures • 10h 33m total length

Course Introduction4:26
Introduction to Data Science4:29
Tools & Libraries3:51
Environment setup for Data Science6:00

Introduction to NumPy2:26
Installing NumPy4:27
Creating ndarray - array() method2:51
Array Fundamentals3:13
Dimensional Arrays - 2D4:02
Dimensional Arrays - 3D & ND4:26
Multidimensional Arrays - Beyond 3D - ND3:12
Attributes of ndarray - size4:36
Attributes of ndarray - itemsize3:21
Attributes of ndarray - dtype3:44
Attributes of ndarray - shape3:53
Attributes of ndarray - ndim4:07
Array Indexing4:32
Array Slicing4:24
Multi-Dimensional Slicing4:01
Element-wise Operations - Addition4:10
Element-wise Operations - Subtraction1:56
Element-wise Operations - Multiplication2:05
Element-wise Operations - Division2:17
Element-wise Operations - Exponentiation2:39
Matrix Operations (Dot product)6:15
Array operation - sum()3:40
Array operation - min()3:27
Array operation - max()2:07
Array Creation – ones()3:49
Understanding Broadcasting5:10
Getting unique items and counts6:22
Transposing a Matrix4:52
Reshaping a Matrix4:47
Reversing array4:31
Flattening an array3:56
T1-Create a (3,4,2) array and calculate the size2:59
T2-Flattening 3D Array3:40
T3-Creating an Array of Uppercase Alphabets4:51
T4-Accessing & Slicing Data in a 2D Array2:56
IQ1-What is an ndarray and how is it different from a Python list?0:36
IQ2-Explain the concept of dimensions in NumPy using 1D, 2D, and 3D arrays.1:40
IQ3-What are axes in a NumPy array? How do axis 0 and axis 1 differ?0:52
IQ4-How does array slicing work in NumPy? How is it different from list slicing?0:48
IQ5-How would you create a 3x4 2D array, filled with 64-bit integer zeros?0:44
IQ6-What is broadcasting? Explain with example0:54
IQ7-How is the dot product different from element-wise multiplication?0:55

Introduction to Jupyter Notebook3:40
Setting up the environment - Installing Jupyter4:40
Quick Fundamentals & Walkthrough6:32
Using NumPy in Jupyter6:02
Creating Random Numpy Arrays4:11
Data Types & Type Casting6:16
Array Reshaping - Reshape3:18
Array Reshaping - Flatten & Ravel2:57
Array Indexing & Slicing4:56
Multi-dimensional Slicing4:11
Concatenating Arrays2:32
Stacking of Arrays2:32
Repeat & Tile in NumPy Arrays3:55
Conditional Based Operations - Part 12:50
Conditional Based Operations - Part 22:00
Masking Arrays4:09
Vectorization in Arrays4:41
Dealing with Missing Values5:33
Identifying NaN Values1:55
T5-Data Exploration with NumPy5:23
T6-Filtering and Analyzing Data with NumPy7:24

Introduction to Pandas5:00
Installing Pandas with uv3:30
Understanding DataFrame4:03
Creating a DataFrame4:03
DataFrame from a List5:51
DataFrame from a Dictionary5:50
Checking Columns and Index Attributes2:02
DataFrame from a NumPy Array4:37
Previewing Data with head()3:08
Previewing Data with tail()2:31
Previewing Data with sample()3:58
Indexing & Selection - Column Selection3:12
Indexing & Selection - Row Selection3:22
Label-Based Selection With “loc”3:38
Position-Based Selection With “iloc”3:49
Conditional Filtering - AND/OR5:33
Filtering With isin()3:02
String Filtering With str.contains()3:43
Filtering With query()3:03
Droping a Column3:27
Joining DataFrames - concat() method4:44
Joining DataFrames - merge() Method4:03
Understanding Series3:03
Creating a Series3:47
Accessing Series Data5:30
Binary Operations on Series3:56
Pandas Series Index Methods3:37
Create a Pandas Series from Array3:54
Conditional Indexing with Series4:31
Modifying a Series2:09
IQ8-When do we use .iloc over .loc?1:43
IQ9-What is the difference between drop() with and without inplace=True?1:11
T5- Data Cleaning Mini Project — Part 1 (Structural Cleaning)9:47
T6-Data Cleaning Mini Project — Part 2 (Column-Level Cleaning)10:52

Introduction1:44
Setting Up The Environment4:16
Installing MySQL Connector3:09
Making connection with MySQL6:34
Creating a Cursor Object4:59
Understanding fetchone() Method4:26
Understanding fetchall() Method1:41
Closing the cursor2:05
Creating a Database3:34
Selecting and Using a Database4:46
Creating a Table8:10
Inserting Single Record6:00
Inserting Multiple Records1:29
Reading Records4:06
Updating Records5:08
Deleting Records3:00
Dropping Tables & Databases4:52
Creating Connection Pool5:20
T-Retrieve all products with a price greater than ₹5000 from the products table.2:40
T-Find all products where stock quantity is less than 20.1:40
T-Filter Products using BETWEEN…AND Operator1:53
T-Retrieve all products whose name contains the word “Wireless”.2:38
T-Display all products with a stock quantity between 20 and 40, sorted by price1:55
T-Fetch all products with a price between $500 and $2000, and calculate their to4:50
T-The task is to retrieve products and convert the results into a dictionary.2:28
T-Exporting Query Results to CSV5:04
IQ-What happens if you call fetchone() but there are no more rows?1:03
IQ-Difference between cursor() and execute().1:22
IQ-What is a buffered and non-buffered cursor and when to use it?1:11
IQ-How to return query results as dictionaries?1:15

Introduction3:15
Setting Up The Environment4:56
Installing PyMongo3:14
Creating Database with MongoDB3:54
Creating Collection & Inserting First Document7:24
Inserting Multiple Documents3:13
Reading Data with find_one()5:18
Reading Data with find()3:28
Using Projection to Select Fields4:02
Filtering Documents with Comparison Operators4:36
Counting Documents3:13
Sorting & Limiting Results2:28
Updating Documents with update_one()6:33
Updating Multiple Fields1:22
Updating Documents with update_many()2:45
Deleting Documents with delete_one()3:32
Deleting Documents with delete_many()2:11
Dropping a Collection1:56
Dropping the Database1:55
Creating Connection Pool4:14
T-Applying Discount on Products4:55
T-Export MongoDB Query Results to CSV4:47

Requirements

Basic Python Knowledge: Students should know fundamental Python concepts like variables, loops, functions, and basic data types (lists, dictionaries). Note: They don’t need to be experts, but they shouldn't be seeing Python for the very first time.
A Computer (Windows, Mac, or Linux): A desktop or laptop with internet access to download the necessary open-source software (like Anaconda, MySQL, and MongoDB).
No Prior Data Science Experience Needed: This course starts from the absolute basics of data manipulation and takes you step-by-step through the process.
No Advanced Math or Statistics Background Required: Any necessary mathematical or data concepts will be explained clearly as we go.
An Eagerness to Learn: A desire to work with data, solve puzzles, and build real-world coding skills!

Description

Unlock the Power of Data: Master Data Science Foundations and Modern Database Integration in One Complete Course.

Data is the new oil, but only if you know how to extract, clean, analyze, and store it. Today, top tech companies aren't just looking for data scientists who can build charts; they need professionals who can bridge the gap between data analysis and real-world database systems.

This course is designed to take you from a basic understanding of Python to a confident, job-ready data analyst and data wrangler. You won't just learn how to analyze mock datasets—you will master how to connect Python directly to MySQL (SQL) and MongoDB (NoSQL) databases to handle data pipelines like a pro.

Why Choose This Course?

Unlike standard Python data science courses that stop at basics, this curriculum bridges the gap between Data Wrangling and Database Architecture. You will gain hands-on experience using industry-standard tools in a structured, step-by-step manner that ensures you never feel lost.

What We Will Cover Step-by-Step:

The Foundations: Kick things off with a smooth introduction to the Data Science ecosystem and quickly get comfortable with Jupyter Notebook, the definitive interactive environment used by data professionals worldwide.
Numerical Computing with NumPy: Learn how to handle massive datasets efficiently using multi-dimensional arrays, vector operations, and high-performance mathematical functions.
Data Manipulation with Pandas: Master the crown jewel of Python data science. You'll learn to clean messy data, handle missing values, merge complex datasets, and perform powerful data aggregations.
The SQL Pipeline (MySQL): Learn how to seamlessly integrate Python with relational databases. Connect to MySQL, execute queries, and extract structured data directly into your Python data pipeline.
The NoSQL Pipeline (MongoDB): Step into modern web and unstructured data storage. Learn how Python interacts with document-oriented databases to manage JSON/BSON data collections.
Data Visualization with Matplotlib: Bring your data stories to life. Learn to build professional, presentation-ready line plots, scatter plots, bar charts, and histograms.

By the End of This Course, You Will Be Able To:

Confidently clean and manipulate raw, disorganized real-world data.
Query both Relational (SQL) and Non-Relational (NoSQL) databases natively using Python.
Build stunning data visualizations to communicate trends and insights to stakeholders.
Think like a Data Analyst and Data Engineer when solving technical problems.

Enroll Today Risk-Free!

Whether you want to upgrade your current career, break into tech, or build a stellar academic project portfolio, this course gives you the modern skillset you need. You'll get lifetime access to all code notebooks, clear video lectures, and active instructor support in the Q&A section.

Click the "Buy Now" button and take your first step toward becoming a data professional today!

Who this course is for:

Aspiring Data Scientists looking for a solid foundation in core Python tools.
Python Beginners wanting to transition into data analysis and visualization.
Data Analysts & Excel Users ready to upgrade to powerful Python libraries.
Developers & DBAs who want to bridge Python with SQL (MySQL) and NoSQL (MongoDB).
Students & Professionals needing to process and visualize data for their projects.

Python for Data Science : Database and Visualization

What you'll learn

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Course content

Introduction4 lectures • 19min

Numpy42 lectures • 2hr 20min

Understanding Jupyter21 lectures • 1hr 30min

Pandas34 lectures • 2hr 20min

SQL with MySQL30 lectures • 1hr 43min

NoSQL with MongoDB22 lectures • 1hr 23min

Matplotlib11 lectures • 59min

Requirements

Description

Who this course is for: