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Data Science with Python Certification Course

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2 skill-building courses

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The Data Science with Python certification course provides a complete overview of Python's Data Analytics tools and techniques. Learning Python is a crucial skill for many Data Science roles.

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What you will learn

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105+ hours of blended learning

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24 hours of self-paced, online learning

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Jupyter notebooks integrated labs

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4 industry-based course-end projects

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Interactive learning

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Blended learning

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Dedicated mentoring sessions from industry experts

Skills Covered

  • Data wrangling

  • Data visualization

  • Data exploration

  • Hypothesis building

  • Mathematical computing

  • Web scraping

  • Python programming concepts

  • NumPy and SciPy package

  • Natural Language Processing

About the Program

What you will learn

  • Learn to excel at Python which makes machine learning, deep learning, and predictive analytics a piece of cake
  • Gain in-depth knowledge of the many techniques that can be applied to the Data at hand
  • Get equipped with the means to design beautiful charts and graphs using Python’s built-in Data visualization libraries like Seaborn and Matplotlib
  • Work with NumPy and SciPy, Python’s mathematical computing libraries, which host a wide variety of mathematical and statistical tools
  • Use Scikit-learn to build models and realise Machine learning algorithms and techniques such as K-NN, dimensionality reduction, linear regression, logistic regression, clustering, and pipeline.
  • Use Pandas, Python’s foremost Data Manipulation tool to manipulate seemingly gibberish data into something legible and presentable

What will I gain from this program?

This Applied Data Science with Python Program aims to discover ways to make use of the Python language to scrub clean, analyze as well as visualize data. Through our guided lectures & labs, you will get hands-on experience tackling fascinating data issues and exploring data stories. This's an action-packed learning path for data science enthusiasts who wish to work with real-life problems with Python. Be sure to take this particular learning road to solidify your data abilities within Python, prior to diving directly into machine learning, deep learning and big data in Python.

What are the pre-requisites for this course?

This course is for complete beginners and doesn’t need any pre-requisites or programming knowledge or experience.

Tools Covered


Learn from India’s leading Software Engineering faculty and Industry leaders

Course Content

Lessson 01 - Data Science with Python Course Overview

  • Introduction to Data Science
  • Different Sectors Using Data Science
  • Purpose and Components of Python
  • Quiz
  • Key Takeaways

Lesson 02 - Data Analytics Overview

  • Data Analytics Process
  • Knowledge Check
  • Exploratory Data Analysis(EDA)
  • EDA-Quantitative Technique
  • EDA - Graphical Technique
  • Data Analytics Conclusion or Predictions
  • Data Analytics Communication
  • Data Types for Plotting
  • Data Types and Plotting
  • Quiz
  • Key Takeaways
  • Knowledge Check

Lesson 03 - Statistical Analysis and Businesss Applications

  • Introduction to Statistics
  • Statistical and Non-statistical Analysis
  • Major Categories of Statistics
  • Statistical Analysis Considerations
  • Population and Sample
  • Statistical Analysis Process
  • Data Distribution
  • Dispersion
  • Knowledge Check
  • Histogram
  • Knowledge Check
  • Testing
  • Knowledge Check
  • Correlation and Inferential Statistics
  • Quiz
  • Key Takeaways

Lesson 04 - Mathematical Computing with python (NumPy)

  • Anaconda
  • Installation of Anaconda Python Distribution (contd.)
  • Data Types with Python
  • Basic Operators and Functions
  • Quiz
  • Key Takeaways

Lesson 05 - Python Environment Setup and Essentials

  • Anaconda
  • Installation of Anaconda Python Distribution (contd.)
  • Data Types with Python
  • Basic Operators and Functions
  • Quiz
  • Key Takeaways

Lesson 06 - Scientific computing with python (Scipy)

  • Introduction to SciPy
  • SciPy Sub Package - Integration and Optimization
  • Knowledge Check
  • SciPy sub package
  • Demo - Calculate Eigenvalues and Eigenvector
  • Knowledge Check
  • SciPy Sub Package - Statistics, Weave and IO
  • Solving Linear Algebra problem using SciPy
  • Assignment 01 Demo
  • Perform CDF and PDF using Scipy
  • Assignment 02 Demo
  • Quiz
  • Key Takeaways

Lesson 07 - Data Manipulation with Pandas

  • Introduction to Pandas
  • Knowledge Check
  • Understanding DataFrame
  • View and Select Data Demo
  • Missing Values
  • Data Operations
  • Knowledge Check
  • File Read and Write Support
  • Knowledge Check-Sequence it Right
  • Pandas Sql Operation
  • Analyse the Federal Aviation Authority Dataset using Pandas
  • Assignment 01 Demo
  • Analyse NewYork city fire department Dataset
  • Assignment 02 Demo
  • Quiz
  • Key Takeaways

Lesson 08 - Machine Learning with Scikit-Learn

  • Machine Learning Approach
  • Steps One and Two
  • Steps Three and Four
  • How it Works
  • Steps Five and Six
  • Supervised Learning Model Considerations
  • ScikitLearn
  • Supervised Learning Models - Linear Regression
  • Supervised Learning Models - Logistic Regression
  • Unsupervised Learning Models
  • Pipeline
  • Model Persistence and Evaluation
  • Knowledge Check
  • Analysing Ad Budgets for different media channels
  • Assignment One
  • Building a model to predict Diabetes
  • Assignment Two
  • Knowledge Check
  • 8.021 Key Takeaways
  • 01:1

Lesson 09 - Natural Language Processing with Scikit Learn

  • NLP Overview
  • NLP Applications
  • Knowledge Check
  • NLP Libraries-Scikit
  • Extraction Considerations
  • Scikit Learn-Model Training and Grid Search
  • Analysing Spam Collection Data
  • Demo Assignment 01
  • Sentiment Analysis using NLP
  • Demo Assignment 02
  • Quiz
  • Key Takeaway

Lesson 10 - Data Visualization in Python using matplotlib

  • Introduction to Data Visualization
  • Knowledge Check
  • Line Properties
  • (x,y) Plot and Subplots
  • Knowledge Check
  • Types of Plots
  • Draw a pair plot using seaborn library
  • Assignment 01 Demo
  • Analysing Cause of Death
  • Assignment 02 Demo
  • Quiz
  • Key Takeaways

Lesson 11 - Web Scraping with BeautifulSoup

  • Web Scraping and Parsing
  • Knowledge Check
  • Understanding and Searching the Tree
  • Navigating options
  • Demo3 Navigating a Tree
  • Knowledge Check
  • Modifying the Tree
  • Parsing and Printing the Document

  • Web Scraping of Simplilearn Website
  • Assignment 01 Demo
  • Web Scraping of Simplilearn Website Resource page
  • Assignment 02 demoQuiz
  • Key takeaways

Lesson 12 - Python integration with Hadoop Mapreduce and Spark

  • Why Big Data Solutions are Provided for Python
  • Hadoop Core Components
  • Python Integration with HDFS using Hadoop Streaming
  • Demo 01 - Using Hadoop Streaming for Calculating Word Count
  • Knowledge Check
  • Python Integration with Spark using PySpark
  • Demo 02 - Using PySpark to Determine Word Count
  • Knowledge Check
  • Determine the wordcount
  • Assignment 01 Demo
  • Display all the airports based in New York using PySpark
  • Assignment 02 Demo
  • Quiz
  • Key takeaways

Practice Projects

IBM HR Analytics Employee Attrition Modeling.


Upon successful completion of this data science with python program.




Enrolling Now


₹ 9,999

₹ 19,999


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Lifetime access to high-quality self-paced elearning content curated by industry experts

10 real-world projects guided by industry experts

48% annual growth in job openings

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24x7 learner assistance and support

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405+ hours of online learning content

Career Impact

Over 500 Careers Transformed


Average Salary Hike


Jobs Sourced

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Highest Salary


Hiring Partners

Frequently asked questions

What is Python?

Python is an object-oriented programming language with integrated dynamic semantics, used primarily for application and web development. The widely used language offers dynamic binding and dynamic typing options.

Why should I learn Python for Data Science?

Python is one of the most popular languages in Data Science, which can be used to perform data analysis, data manipulation, and data visualization. Python offers access to a wide variety of Data Science libraries and it is the ideal language for implementing algorithms and the rapid development of applications.

Can I learn Python Data Science course online?

The rapid evolution of learning methodologies, thanks to the influx of technology, has increased the ease and efficiency of online learning, making it possible to learn at your own pace. Simplilearn's Python Data Science course provides live classes and access to study materials from anywhere and at any time. Our extensive (and growing) collection of blogs, tutorials, and YouTube videos will help you get up to speed on the main concepts. Even after your class ends, we provide a 24/7 support system to help you with any questions or concerns you may have.

What is the job outlook for Data Science with Python programming professionals?

Harvard Business Review has already named Data Scientist as the ‘Sexiest Job of the 21st Century.’ The statement is echoed in LinkedIn Emerging Jobs Report 2021 in which Data Science specialists are one of the top emerging jobs in the US with Python as one of its key skills. The job role has witnessed an annual growth of 35 percent for Data scientists and Data engineers.

Which companies use Python?

Major companies like Google, Instagram, Goldman Sachs, Facebook, Quora, Netflix, Dropbox, and PayPal use Python.

Can I cancel my enrollment? Will I get a refund?

Yes, you can cancel your enrollment if necessary. We will refund the course price after deducting an administration fee. To learn more, you can view our Refund Policy.

Do I need to leave my job for this program?

No, the program is designed in such a way that, you can continue with your job along with this program. It will be a mix of pre-recorded videos, live classes as well as printed study material. Every topic would be project-based and will be taught as per the live market scenario. The course module will be covered under the guidance of Industry Experts.

Will Bygrad help me with project completion?

There are two types of projects:

a. Practice projects: Your mentor will first do 3-4 projects for you and then you will do the next 5-6 projects wherein you will get help from your mentor and on tickets.

b. Evaluation projects: Once you’re done with the practice projects, you get access to the evaluation projects.

What if I miss a live online class?

In case you miss a class, you need not to worry. All the live classes’ recordings will be available on your LMS. You can watch and practice the concepts at your own time.

What will be the training mode?

There are two training modes:

a. Self-paced: You will get access to Bygrad and IBM joint LMS wherein you will be assigned courses and projects. You will need to go through these courses and complete the projects at your own pace. Mentor support will be provided.

b. Blended: You will get access to Bygrad and IBM joint LMS wherein you will be assigned courses and projects. You will need to go through these courses and complete the projects at your own pace. In addition to these courses, live online classes are conducted on Saturdays and Sundays for you. Mentor support will be provided.