Applied Data Science with Python

Enroll Now
No Image
⭐ 4.8 Ratings
12,392 Learners

Course Overview: Applied Data Science with Python

This Python for Data Science course is designed to help you transform raw data into meaningful insights. You’ll learn to clean, analyze, and visualize data, engineer features, and apply statistical techniques using Python. With hands-on exercises and real-world projects, you’ll gain the confidence to tackle complex data challenges and make data-driven decisions in any industry.

Key Features

  • 60+ Hours of Blended Learning

    Gain comprehensive knowledge through a mix of self-paced content, interactive labs, and guided sessions.

  • 40+ Assisted Practices and Knowledge Checks

    Reinforce your learning with hands-on exercises and lesson-wise assessments.

  • Lifetime Access to Self-Paced Content

    Learn at your own pace with unlimited access to all course materials.

  • Interactive Jupyter Notebook Labs

    Practice Python and data science techniques in an engaging, hands-on environment.

  • Interactive Jupyter notebook labs for practical learning
  • Dedicated Live Sessions

    Receive guidance and mentorship from experienced industry faculty.

Skills Covered

  • Data wrangling
  • Data visualization
  • Web scraping
  • Python programming concepts
  • ScikitLearn package for Natural Language Processing
  • Data exploration
  • Mathematical computing
  • Hypothesis building
  • NumPy and SciPy package

Career Benefits of Applied Data Science with Python

Completing this course equips you with in-demand skills in Python programming, data analysis, and visualization, making you highly employable in roles like data analyst, data scientist, and business intelligence professional. The hands-on projects and practical exercises prepare you to tackle real-world problems, enhance decision-making, and accelerate your career growth across industries.

Turn Data into Insights and Propel Your Career with Python Expertise

Instructor-led
Learn from expert instructors in live, online sessions.
Get 24/7 learner support and access two full-length mock exams.
Choose a schedule that fits your availability.

$1125

Corporate Training
Choose from flexible pricing and billing options.
Join private cohorts tailored to your teams.
Track your training progress with intuitive dashboards.
Assess and benchmark your skills easily. Integrate seamlessly with your existing platforms.
Get support from a dedicated Customer Success Manager

Eligibility for Applied Data Science with Python

This course is ideal for learners at all experience levels. Whether you are an analytics professional seeking to strengthen your Python skills, a software or IT professional exploring data science, or someone with a genuine interest in Python and analytics, this program is designed to help you succeed.

Prerequisites

Learners should have a high school diploma or an undergraduate degree. A curiosity for data analysis and a keen interest in applying Python to real-world data science problems will help you make the most of this program.

Course Content: Applied Data Science with Python

Section 01 - Course Introduction

1.01 Course Introduction
1.02 What You Will Learn

2.01 Introduction
2.02 Data Science and Its Applications
2.03 The Data Science Process: Part 1
2.04 The Data Science Process: Part 2
2.05 Recap

3.01 Introduction
3.02 Setting Up Jupyter Notebook: Part 1
3.03 Setting Up Jupyter Notebook: Part 2
3.04 Python Functions
3.05 Python Types and Sequences
3.06 Python Strings Deep Dive
3.07 Python Demo: Reading and Writing CSV Files
3.08 Date and Time in Python
3.09 Objects in Python Map
3.10 Lambda and List Comprehension
3.11 Why Python for Data Analysis?
3.12 Python Packages for Data Science
3.13 StatsModels Package: Part 1
3.14 StatsModels Package: Part 2
3.15 Scipy Package
3.16 Recap
3.17 Spotlight

4.01 Introduction
4.02 Fundamentals of NumPy
4.03 Array Shapes and Axes in NumPy: Part A
4.04 NumPy Array Shapes and Axes: Part B
4.05 Arithmetic Operations
4.06 Conditional Logic
4.07 Common Mathematical and Statistical Functions in NumPy
4.08 Indexing and Slicing: Part 1
4.09 Indexing and Slicing: Part 2
4.10 File Handling
4.11 Recap

5.01 Introduction
5.02 Introduction to Linear Algebra
5.03 Scalars and Vectors
5.04 Dot Product of Two Vectors
5.05 Linear Independence of Vectors
5.06 Norm of a Vector
5.07 Matrix
5.08 Matrix Operations
5.09 Transpose of a Matrix
5.10 Rank of a Matrix
5.11 Determinant of a Matrix and Identity Matrix or Operator
5.12 Inverse of a Matrix and Eigenvalues and Eigenvectors
5.13 Calculus in Linear Algebra
5.14 Recap

6.01 Introduction
6.02 Importance of Statistics with Respect to Data Science
6.03 Common Statistical Terms
6.04 Types of Statistics
6.05 Data Categorization and Types
6.06 Levels of Measurement
6.07 Measures of Central Tendency
6.08 Measures of Central Tendency
6.09 Measures of Central Tendency
6.10 Measures of Dispersion
6.11 Random Variables
6.12 Sets
6.13 Measures of Shape (Skewness)
6.14 Measures of Shape (Kurtosis)
6.15 Covariance and Correlation
6.16 Recap

7.01 Introduction
7.02 Probability, Its Importance, and Probability Distribution
7.03 Probability Distribution: Binomial Distribution
7.04 Probability Distribution: Poisson Distribution
7.05 Probability Distribution: Normal Distribution
7.06 Probability Distribution: Uniform Distribution
7.07 Probability Distribution: Bernoulli Distribution
7.08 Probability Density Function and Mass Function
7.09 Cumulative Distribution Function
7.10 Central Limit Theorem
7.11 Estimation Theory
7.12 Recap

8.01 Introduction
8.02 Distribution
8.03 Kurtosis, Skewness, and Student's T-Distribution
8.04 Hypothesis Testing and Mechanism
8.05 Hypothesis Testing Outcomes: Type I and II Errors
8.06 Null Hypothesis and Alternate Hypothesis
8.07 Confidence Intervals
8.08 Margins of Error
8.09 Confidence Level
8.10 T-Test and P-Values (Lab)
8.11 Z-Test and P-Values
8.12 Comparing and Contrasting T-Test and Z-Test
8.13 Bayes Theorem
8.14 Chi-Square Distribution
8.15 Chi-Square Distribution: Demo
8.16 Chi-Square Test and Goodness of Fit
8.17 Analysis of Variance or ANOVA
8.18 ANOVA Terminologies
8.19 Assumptions and Types of ANOVA
8.20 Partition of Variance Using Python
8.21 F-Distribution
8.22 F-Distribution Using Python
8.23 F-Test
8.24 Recap
8.25 Spotlight

9.01 Introduction
9.02 Introduction to Pandas
9.03 Pandas Series
9.04 Querying a Series
9.05 Pandas DataFrames
9.06 Pandas Panel
9.07 Common Functions in Pandas
9.08 Pandas Functions: Data Statistical Function, Windows Function
9.09 Pandas Function: Data and Timedelta
9.10 IO Tools: Explain All the Read Functions
9.11 Categorical Data
9.12 Working with Text Data
9.13 Iteration
9.14 Sorting
9.15 Plotting with Pandas
9.16 Recap

10.01 Introduction
10.02 Understanding Data
10.03 Types of Data: Structured, Unstructured, Messy, etc.
10.04 Working with Data: Choosing Appropriate Tools, Data Collection, Data Wrangling
10.05 Importing and Exporting Data in Python
10.06 Regular Expressions in Python
10.07 Manipulating Text with Regular Expressions
10.08 Accessing Databases in Python
10.09 Recap
10.10 Spotlight

11.01 Introduction
11.02 Pandorable or Idiomatic Pandas Code
11.03 Loading, Indexing, and Reindexing
11.04 Merging
11.05 Memory Optimization in Python
11.06 Data Preprocessing: Data Loading and Dropping Null Values
11.07 Data Preprocessing: Filling Null Values
11.08 Data Binning, Formatting, and Normalization
11.09 Data Binning: Standardization
11.10 Describing Data
11.11 Recap

12.01 Introduction
12.02 Principles of Information Visualization
12.03 Visualizing Data Using Pivot Tables
12.04 Data Visualization Libraries in Python: Matplotlib
12.05 Graph Types
12.06 Data Visualization Libraries in Python: Seaborn
12.07 Data Visualization Libraries in Python: Seaborn
12.08 Data Visualization Libraries in Python: Plotly
12.09 Data Visualization Libraries in Python: Plotly
12.10 Data Visualization Libraries in Python: Bokeh
12.11 Data Visualization Libraries in Python: Bokeh
12.12 Using Matplotlib to Plot Graphs
12.13 Plotting 3D Graphs for Multiple Columns Using Matplotlib
12.14 Using Matplotlib with Other Python Packages
12.15 Using Seaborn to Plot Graphs
12.16 Using Seaborn to Plot Graphs
12.17 Plotting 3D Graphs for Multiple Columns Using Seaborn
12.18 Introduction to Plotly
12.19 Introduction to Bokeh
12.20 Recap

13.01 Introduction
13.02 Basic Statistics with Python: Problem Statement
13.03 Basic Statistics with Python: Solution
13.04 Scipy for Statistics: Problem Statement
13.05 Scipy for Statistics: Solution
13.06 Advanced Statistics Python
13.07 Advanced Statistics with Python: Solution
13.08 Recap
13.09 Spotlight

Applied Data Science with Python Projects

Project 1: Sales Analysis for Business Growth

Analyze retail clothing sales data to provide actionable insights and support management in strategic decision-making for sales and growth optimization.

Conduct exploratory data analysis and hypothesis testing to identify key factors driving customer acquisition and improve marketing strategies.

Visualize housing datasets using a variety of plots to uncover patterns, trends, and actionable insights for the real estate sector.

Analyze housing data to understand price determinants, evaluate the impact of different features, and generate insights to guide pricing strategies and investment decisions.

Exam & Certification FAQs

Who should enroll in the Data Science with Python course?

This course is ideal for analytics professionals, software or IT specialists, and anyone interested in learning Python for data science. Beginners and experienced learners alike can benefit from its practical, hands-on approach.

The Data Science with Python Certification is awarded by Nvidya upon successful completion of the program. This certificate is recognized in the industry and serves as proof of your proficiency in Python-based data analysis, machine learning, and data visualization. It can be a valuable addition to your professional portfolio and resume.

The steps to unlock your certificate depend on the learning mode you choose:

  • Instructor-Led Online Program: Attend all scheduled sessions for your batch and complete at least one hands-on project successfully.
  • Self-Paced Learning: Complete a minimum of 85% of the course modules and submit at least one completed project to demonstrate your applied skills.

Learners should have a high school diploma or undergraduate degree. A curiosity for data analysis and an interest in applying Python to real-world data science challenges is recommended. No prior programming experience is required.

You will work on multiple projects, including sales analysis, marketing campaign evaluation, real estate visualization, and housing price analysis. These exercises provide hands-on experience with Python, data visualization, statistical analysis, and predictive modeling.

Completing this program equips you with in-demand Python and data science skills, making you eligible for roles like data analyst, business intelligence professional, and data scientist. You’ll be able to analyze, visualize, and interpret data to support strategic decision-making across industries.

CERTIFICATE FOR Applied Data Science with Python
THIS CERTIFICATE IS AWARDED TO
Your Name
FOR SUCCESSFUL PARTICIPATION IN
Applied Data Science with Python
Issued By NVidya
Certificate ID __________
Date __________

Success Stories

Shivani Panwar
Shivani Panwar

““Python has made analyzing large datasets faster and more insightful.””

“Before this program, I struggled to make sense of complex marketing data. Through the hands-on projects and practical exercises, I learned to clean, visualize, and analyze data effectively. Now, I can generate actionable insights that directly impact campaign strategies and business decisions.”

Arjun Malhotra
Arjun Malhotra

““This course turned abstract data into actionable insights for me. I can now confidently work with Python and data analysis tools.””

“Before this program, I often felt overwhelmed by large datasets. The structured lessons, hands-on projects, and interactive exercises made Python approachable and practical. I now apply what I learned directly at work, improving reporting, analysis, and decision-making efficiency.”

Why Choose This Program?

Develop In-Demand Skills

Gain practical expertise crafted with industry and academic input.

Learn from Seasoned Professionals

Learn from seasoned professionals sharing real-world insights and case studies.

Engage in Applied Learning

Build skills through hands-on projects with real data and virtual labs.

Benefit from Continuous Support:

Enjoy 24/7 access to mentors and a supportive learning community.

Frequently Asked Questions

What is the Data Science with Python Certification?

The Data Science with Python Certification is a professional program designed to equip you with the skills to analyze, interpret, and visualize data using Python. The course focuses on practical applications, teaching you how to work with large datasets, perform statistical analysis, and implement machine learning models. Completing this certification proves your ability to solve real-world business problems with Python.

Through this program, you will master:

  • Data collection, cleaning, and preprocessing techniques
  • Data exploration and visualization for actionable insights
  • Python programming essentials for data analysis
  • Building and evaluating machine learning models using Scikit-learn and NumPy
  • Advanced statistical techniques and predictive modeling
  • Hands-on experience with real datasets to simulate business scenarios

Python data science skills are highly valued in sectors including:

  • Retail and e-commerce for customer analytics
  • Healthcare for predictive modeling and patient data analysis
  • Banking, insurance, and finance for risk assessment and fraud detection
  • Media, marketing, and entertainment for audience analysis
  • Telecommunications and IT for operational efficiency
  • Energy, utilities, and smart infrastructure management

Completing this course opens doors to various roles such as:

  • Data Analyst
  • Data Scientist
  • Machine Learning Engineer
  • Business Intelligence (BI) Developer
  • NLP or Computer Vision Specialist
  • MLOps Engineer

Analytics Consultant
With experience, you can progress to senior roles such as Data Science Manager, AI Strategist, or Head of Analytics.

Python data scientists extract insights from raw data to guide business decisions. They clean and preprocess datasets, create statistical models, build predictive algorithms, and design visual dashboards. Their work helps organizations optimize strategies, improve efficiency, and make informed decisions based on accurate data analysis.

Our instructors are industry experts with extensive hands-on experience in Python and data science. They are chosen for their ability to combine theoretical knowledge with real-world applications, ensuring learners gain practical skills and actionable insights.

Enrolling is simple:

  1. Complete the online registration form on the Nvidya website.
  2. The admissions team reviews your application and confirms eligibility.
  3. Make the course payment securely online to reserve your spot.
  4. Access your learning materials and start your Python data science journey.

Python data science professionals are in high demand. Typical annual salaries include:

  • India: ₹14.5 LPA
  • United States: $102,000

Salaries vary by experience, role, location, and industry.

Yes. The program includes practical projects that cover data cleaning, visualization, predictive modeling, and machine learning. These projects help you build a professional portfolio and gain confidence in applying Python to real-world scenarios.

All live sessions are recorded and available for you to watch anytime. This ensures you stay on track, review complex topics, and complete the course without missing critical content.

Yes. Nvidya offers custom corporate training programs designed for businesses looking to upskill employees. These include role-based learning tracks, interactive workshops, and hands-on projects tailored to company requirements, helping teams leverage data science for smarter business decisions.

Starting as a data analyst or junior data scientist, you can grow into senior roles like data engineer, AI specialist, or analytics consultant. Experienced professionals can advance to leadership positions such as Head of Data Science or Chief Analytics Officer, driving strategic decision-making with data insights.