Applied Data Science with Python

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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

Self Paced / Live Virtual
E-learning videos

$845

Instructor-led

$1125

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 provides the Data Science with Python certification, and how long is it valid?

Upon successful completion of the Data Science with Python course, you will receive an industry-recognized certificate from Nvidya. This certificate comes with lifetime validity.

 

For Online Classroom Learning:
• Attend an entire batch of the Data Science with Python training.
• Submit at least one project for evaluation.
For Self-Paced Learning:
• Complete a minimum of 85% of the course content.
• Submit at least one project.

The Data Science with Python Certification validates your proficiency in leveraging Python for data analysis, machine learning, and data visualization. To earn this credential, learners must complete the coursework and pass an assessment covering key topics such as statistical analysis, data manipulation using libraries like Pandas and NumPy, and building predictive models. Nvidya’s Applied Data Science with Python program equips you with practical skills to solve real-world data challenges.

 

A successful data science expert should master the following competencies:
• Data wrangling
• Data visualization
• Web scraping
• Python programming fundamentals
• Natural Language Processing using Scikit-learn
• Data exploration
• Mathematical computing
Nvidya’s Applied Data Science with Python course is designed to help you acquire all these in-demand skills.

 

Data science is critical across various sectors, with the highest adoption seen in:
• Retail
• Healthcare
• Banking & Finance
• Construction
• Communications
• Media & Entertainment
• Education
• Energy & Utilities

 

Yes. Nvidya offers a wide range of data science programs tailored to various career stages. These include certifications, master’s programs, and postgraduate options.
Other available courses:
• Data Engineering Course
• SQL Certification
• Azure Data Engineering Certification
• Purdue Data Science Program
• Introduction to Data Science
• Data Science Course
• Introduction to Data Analytics

 

Not at all. With Nvidya’s ‘flexi-learn’ model, you can watch recorded sessions of any missed class at your convenience. This ensures uninterrupted learning and allows you to catch up at your own pace.

 

A Python-skilled data scientist uses the language to analyze complex datasets, build statistical and machine learning models, and visualize data for actionable insights. Proficiency in libraries such as Pandas, NumPy, and Scikit-learn enables effective data cleaning, predictive modeling, and creating dynamic visualizations.

 

Python is a top choice for data science due to its simplicity and powerful libraries. This course offers:
• 60+ hours of blended learning
• Lifetime access to self-paced content
• Hands-on experience with industry-based projects
• Interactive labs using Jupyter Notebooks
• 40+ guided practices and assessments

 

Our instructors are seasoned industry professionals with deep expertise in data science. Each trainer is carefully selected through a rigorous process that includes technical assessments, interviews, and demo sessions to ensure top-quality instruction.

 

To enroll:
1. Complete the application form via the “Enroll Now” button.
2. Make a secure payment via Visa, MasterCard, AmEx, Diners Club, or PayPal.
3. Once payment is successful, you’ll receive a confirmation email with access details.

Data scientists with Python expertise are in high demand. On average:
• India: ₹14.5 Lakhs per annum
• United States: $101,399 per annum
Salaries vary based on experience, location, and industry.

 

This course opens doors to multiple roles, including:
• Junior Data Scientist
• Data Analyst
• Machine Learning Engineer
With experience, you can transition to:
• Senior Data Scientist
• Data Engineer
• Analytics Consultant
• Data Strategy or Leadership roles

 

Certified professionals can explore positions such as:
• Business Analyst
• Database Administrator
• Big Data Engineer or Data Architect
• Data Analyst
• Machine Learning Engineer
• BI Developer or Analyst
• Statistician
• Computer Vision Engineer
• NLP Engineer
• MLOps Engineer

 

Yes. Nvidya for Business partners with Fortune 500 and mid-sized companies to provide role-based learning paths and skill-based certifications. We also offer Nvidya Learning Hub+, a robust training platform with live and on-demand sessions, curated for enterprise-wide upskilling.

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 __________

Why Choose This Program?

Gain in-demand, job-ready skills through a cutting-edge curriculum developed in collaboration with industry leaders and academic experts.

Tackle real business challenges using actual datasets in capstone projects, supported by virtual labs for immersive, practical learning.

Enjoy 24/7 access to mentor support and a vibrant community of peers, ensuring your learning journey stays on track and your doubts are always addressed.