Course description

  • Why learn Data Science with Python?

    Python is a multi-paradigm or versatile programming language that can be considered as a sort of swiss knife for the coding world. This is because it supports structured programming, Object Oriented Programming, and even functional programming patterns. The versatility of Python undoubtedly makes it the best-suited programming language for the data scientists. Here are some of the other advantages of python for data science, which will help you understand why you should learn data science with Python:

    • Python is a powerful open source programming language, which means that it’s free to use while having all the properties that a programming language should have.
    • It is a versatile programming language that supports Object-Oriented Programming, Structured Programming, and functional programming patterns.
    • Python has some 72,000 libraries in the Python Package Index that aid in scientific calculations and machine learning applications.
    • Python sports an easy to understand and readable syntax that ensures that the development time is cut into half when compared with other programming languages.
    • Python enables you to perform data analysis, data manipulation, and data visualization, which are very important in data science.

    All the above-mentioned advantages of Python programming language makes it ideal to be used for data science by the data scientists. Owing to the extensibility and general purpose nature, it is recommended that you learn data science with Python.

    why learn data science with python

  • What are the course objectives?

    The Data Science with Python course will furnish you with in-depth knowledge of the various libraries and packages required to perform data analysis, data visualization, web scraping, machine learning and natural language processing using Python. 
    Python has surpassed Java as the top language used to introduce US students to programming and computer science, and 46 percent of data science jobs list Python as a required skill.

  • What skills will you learn?

    This Python for Data Science training course will enable you to:
    • Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics
    • Install the required Python environment and other auxiliary tools and libraries
    • Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
    • Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions
    • Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave
    • Perform data analysis and manipulation using data structures and tools provided in the Pandas package
    • Gain expertise in machine learning using the Scikit-Learn package
    • Gain an in-depth understanding of supervised learning and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline
    • Use the Scikit-Learn package for natural language processing
    • Use the matplotlib library of Python for data visualization
    • Extract useful data from websites by performing web scrapping using Python
    • Integrate Python with Hadoop, Spark and MapReduce

  • Who should take this Python for Data Science course?

    There is a booming demand for skilled data scientists across all industries that make this course suited for participants at all levels of experience. We recommend this Data Science with Python training particularly for the following professionals:
    • Analytics professionals who want to work with Python
    • Software professionals looking to get into the field of analytics
    • IT professionals interested in pursuing a career in analytics
    • Graduates looking to build a career in analytics and data science
    • Experienced professionals who would like to harness data science in their fields
    • Anyone with a genuine interest in the field of data science
    Prerequisites: There are no prerequisites for this Data Science with Python course. The Python basics course included with this program provides additional coding guidance.

  • What projects are included in this Python for Data Science certification course?

    The course includes four real-world, industry-based projects. Successful evaluation of one of the following projects is a part of the certification eligibility criteria:

    Project 1: Products rating prediction for Amazon

    Amazon, one of the leading US-based e-commerce companies, recommends products within the same category to customers based on their activity and reviews on other similar products. Amazon would like to improve this recommendation engine by predicting ratings for the non-rated products and add them to recommendations accordingly.

    Domain: E-commerce

    Project 2: Demand Forecasting for Walmart

    Predict accurate sales for 45 stores of Walmart, one of the US-based leading retail stores, considering the impact of promotional markdown events. Check if macroeconomic factors like CPI, unemployment rate, etc. have an impact on sales.

    Domain: Retail

    Project 3: Improving customer experience for Comcast

    Comcast, one of the US-based global telecommunication companies wants to improve customer experience by identifying and acting on problem areas that lower customer satisfaction if any. The company is also looking for key recommendations that can be implemented to deliver the best customer experience.

    Domain: Telecom

    Project 4: Attrition Analysis for IBM

    IBM, one of the leading US-based IT companies, would like to identify the factors that influence attrition of employees. Based on the parameters identified, the company would also like to build a logistics regression model that can help predict if an employee will churn or not.

    Domain: Workforce Analytics

    Project 5: NYC 311 Service Request Analysis

    Perform a service request data analysis of New York City 311 calls. You will focus on data wrangling techniques to understand patterns in the data and visualize the major complaint types.

    Domain: Telecommunication
    Project 6: MovieLens Dataset Analysis

    The GroupLens Research Project is a research group in the Department of Computer Science and Engineering at the University of Minnesota. The researchers of this group are involved in several research projects in the fields of information filtering, collaborative filtering and recommender systems. Here, we ask you to perform an analysis using the Exploratory Data Analysis technique for user datasets.

    Domain: Engineering
    Project 7: Stock Market Data Analysis

    As a part of this project, you will import data using Yahoo data reader from the following companies: Yahoo, Apple, Amazon, Microsoft and Google. You will perform fundamental analytics, including plotting, closing price, plotting stock trade by volume, performing daily return analysis, and using pair plot to show the correlation between all of the stocks.

    Domain: Stock Market
    Project 8: Titanic Dataset Analysis

    On April 15, 1912, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This tragedy shocked the world and led to better safety regulations for ships. Here, we ask you to perform an analysis using the exploratory data analysis technique, in particular applying machine learning tools to predict which passengers survived the tragedy.

    Domain: Hazard

Course preview

    • Lesson 00 - Course Overview 04:34
      • 0.001 Course Overview 04:34
    • Lesson 01 - Data Science Overview 20:27
      • 1.001 Introduction to Data Science 08:42
      • 1.002 Different Sectors Using Data Science 05:59
      • 1.003 Purpose and Components of Python 05:02
      • 1.4 Quiz
      • 1.005 Key Takeaways 00:44
    • Lesson 02 - Data Analytics Overview 18:20
      • 2.001 Data Analytics Process 07:21
      • 2.2 Knowledge Check
      • 2.3 Exploratory Data Analysis(EDA)
      • 2.4 EDA-Quantitative Technique
      • 2.005 EDA - Graphical Technique 00:57
      • 2.006 Data Analytics Conclusion or Predictions 04:30
      • 2.007 Data Analytics Communication 02:06
      • 2.8 Data Types for Plotting
      • 2.009 Data Types and Plotting 02:29
      • 2.10 Knowledge Check
      • 2.11 Quiz
      • 2.012 Key Takeaways 00:57
    • Lesson 03 - Statistical Analysis and Business Applications 23:53
      • 3.001 Introduction to Statistics 01:31
      • 3.2 Statistical and Non-statistical Analysis
      • 3.003 Major Categories of Statistics 01:34
      • 3.4 Statistical Analysis Considerations
      • 3.005 Population and Sample 02:15
      • 3.6 Statistical Analysis Process
      • 3.007 Data Distribution 01:48
      • 3.8 Dispersion
      • 3.9 Knowledge Check
      • 3.010 Histogram 03:59
      • 3.11 Knowledge Check
      • 3.012 Testing 08:18
      • 3.13 Knowledge Check
      • 3.014 Correlation and Inferential Statistics 02:57
      • 3.15 Quiz
      • 3.016 Key Takeaways 01:31
    • Lesson 04 - Python Environment Setup and Essentials 23:58
      • 4.001 Anaconda 02:54
      • 4.2 Installation of Anaconda Python Distribution (contd.)
      • 4.003 Data Types with Python 13:28
      • 4.004 Basic Operators and Functions 06:26
      • 4.5 Quiz
      • 4.006 Key Takeaways 01:10
    • Lesson 05 - Mathematical Computing with Python (NumPy) 30:31
      • 5.001 Introduction to Numpy 05:30
      • 5.2 Activity-Sequence it Right
      • 5.003 Demo 01-Creating and Printing an ndarray 04:50
      • 5.4 Knowledge Check
      • 5.5 Class and Attributes of ndarray
      • 5.006 Basic Operations 07:04
      • 5.7 Activity-Slice It
      • 5.8 Copy and Views
      • 5.009 Mathematical Functions of Numpy 05:01
      • 5.10 Assignment 01
      • 5.011 Assignment 01 Demo 03:55
      • 5.12 Assignment 02
      • 5.013 Assignment 02 Demo 03:16
      • 5.14 Quiz
      • 5.015 Key Takeaways 00:55
    • Lesson 06 - Scientific computing with Python (Scipy) 23:35
      • 6.001 Introduction to SciPy 06:57
      • 6.002 SciPy Sub Package - Integration and Optimization 05:51
      • 6.3 Knowledge Check
      • 6.4 SciPy sub package
      • 6.005 Demo - Calculate Eigenvalues and Eigenvector 01:36
      • 6.6 Knowledge Check
      • 6.007 SciPy Sub Package - Statistics, Weave and IO 05:46
      • 6.8 Assignment 01
      • 6.009 Assignment 01 Demo 01:20
      • 6.10 Assignment 02
      • 6.011 Assignment 02 Demo 00:55
      • 6.12 Quiz
      • 6.013 Key Takeaways 01:10
    • Lesson 07 - Data Manipulation with Pandas 47:34
      • 7.001 Introduction to Pandas 12:29
      • 7.2 Knowledge Check
      • 7.003 Understanding DataFrame 05:31
      • 7.004 View and Select Data Demo 05:34
      • 7.005 Missing Values 03:16
      • 7.006 Data Operations 09:56
      • 7.7 Knowledge Check
      • 7.008 File Read and Write Support 00:31
      • 7.9 Knowledge Check-Sequence it Right
      • 7.010 Pandas Sql Operation 02:00
      • 7.11 Assignment 01
      • 7.012 Assignment 01 Demo 04:09
      • 7.13 Assignment 02
      • 7.014 Assignment 02 Demo 02:34
      • 7.15 Quiz
      • 7.016 Key Takeaways 01:34
    • Lesson 08 - Machine Learning with Scikit–Learn 1:02:10
      • 8.001 Machine Learning Approach 03:57
      • 8.002 Steps 1 and 2 01:00
      • 8.3 Steps 3 and 4
      • 8.004 How it Works 01:24
      • 8.005 Steps 5 and 6 01:54
      • 8.006 Supervised Learning Model Considerations 00:30
      • 8.7 Knowledge Check
      • 8.008 Scikit-Learn 02:10
      • 8.9 Knowledge Check
      • 8.010 Supervised Learning Models - Linear Regression 11:19
      • 8.011 Supervised Learning Models - Logistic Regression 08:43
      • 8.012 Unsupervised Learning Models 10:40
      • 8.013 Pipeline 02:37
      • 8.014 Model Persistence and Evaluation 05:45
      • 8.15 Knowledge Check
      • 8.16 Assignment 01
      • 8.017 Assignment 01 05:45
      • 8.18 Assignment 02
      • 8.019 Assignment 02 05:14
      • 8.20 Quiz
      • 8.021 Key Takeaways 01:12
    • Lesson 09 - Natural Language Processing with Scikit Learn 49:03
      • 9.001 NLP Overview 10:42
      • 9.2 NLP Applications
      • 9.3 Knowledge check
      • 9.004 NLP Libraries-Scikit 12:29
      • 9.5 Extraction Considerations
      • 9.006 Scikit Learn-Model Training and Grid Search 10:17
      • 9.7 Assignment 01
      • 9.008 Demo Assignment 01 06:32
      • 9.9 Assignment 02
      • 9.010 Demo Assignment 02 08:00
      • 9.11 Quiz
      • 9.012 Key Takeaway 01:03
    • Lesson 10 - Data Visualization in Python using matplotlib 32:46
      • 10.001 Introduction to Data Visualization 08:02
      • 10.2 Knowledge Check
      • 10.3 Line Properties
      • 10.004 (x,y) Plot and Subplots 10:01
      • 10.5 Knowledge Check
      • 10.006 Types of Plots 09:34
      • 10.7 Assignment 01
      • 10.008 Assignment 01 Demo 02:23
      • 10.9 Assignment 02
      • 10.010 Assignment 02 Demo 01:47
      • 10.11 Quiz
      • 10.012 Key Takeaways 00:59
    • Lesson 11 - Web Scraping with BeautifulSoup 52:27
      • 11.001 Web Scraping and Parsing 12:50
      • 11.2 Knowledge Check
      • 11.003 Understanding and Searching the Tree 12:56
      • 11.4 Navigating options
      • 11.005 Demo3 Navigating a Tree 04:22
      • 11.6 Knowledge Check
      • 11.007 Modifying the Tree 05:38
      • 11.008 Parsing and Printing the Document 09:05
      • 11.9 Assignment 01
      • 11.010 Assignment 01 Demo 01:55
      • 11.11 Assignment 02
      • 11.012 Assignment 02 demo 04:57
      • 11.13 Quiz
      • 11.014 Key takeaways 00:44
    • Lesson 12 - Python integration with Hadoop MapReduce and Spark 40:39
      • 12.001 Why Big Data Solutions are Provided for Python 04:55
      • 12.2 Hadoop Core Components
      • 12.003 Python Integration with HDFS using Hadoop Streaming 07:20
      • 12.004 Demo 01 - Using Hadoop Streaming for Calculating Word Count 08:52
      • 12.5 Knowledge Check
      • 12.006 Python Integration with Spark using PySpark 07:43
      • 12.007 Demo 02 - Using PySpark to Determine Word Count 04:12
      • 12.8 Knowledge Check
      • 12.9 Assignment 01
      • 12.010 Assignment 01 Demo 02:47
      • 12.11 Assignment 02
      • 12.012 Assignment 02 Demo 03:30
      • 12.13 Quiz
      • 12.014 Key takeaways 01:20
    • Statistics Essential for Data Science 30:50
      • Statistics for Data Science 30:50
    • Getting Started with Python 20:04
      • Installation 09:31
      • Print and Strings 07:47
      • Math 02:46
    • Variables, Loops and Statements 36:54
      • Variables 04:49
      • While Loops 06:00
      • For Loops 05:00
      • If Statements 06:43
      • If Else Statements 04:01
      • If Elif Else Statements 10:21
    • Functions and Global and Local Variables 28:20
      • Functions 05:03
      • Function Parameters 14:04
      • Global and Local Variables 09:13
    • Understanding Error Detection 11:35
      • Common Python Errors 11:35
    • Working with Files and Classes 15:49
      • Writing to a File 04:29
      • Appending to a File 03:23
      • Reading From a File 03:34
      • Classes 04:23
    • Intermediate Python 39:09
      • Input and Statistics 07:22
      • Import Syntax 06:39
      • Making Modules 06:20
      • Lists vs Tuples and List Manipulation 10:34
      • Dictionaries 08:14
    • Project 26:15
      • Problem Statement
      • Solution 26:15
    • Math Refresher 30:36
      • Math Refresher 30:36
    • {{childObj.title}}
      • {{childObj.childSection.chapter_name}}
        • {{lesson.title}}
      • {{lesson.title}}

    View More

    View Less

Exam & certification FREE PRACTICE TEST

  • How do I earn my Simplilearn certificate?

    To become a Certified Data Scientist with Python, you must fulfil the following criteria:
    • Complete one project out of the two provided in the course. Submit the deliverables of the project in the LMS which will be evaluated by our lead trainer
    • Score a minimum of 60% in any one of the two simulation tests
    • Complete 85% of the course
    • Attend one complete batch.

Course advisor

Alvaro Fuentes
Alvaro Fuentes Founder and Data Scientist at Quant Company

Alvaro is a Data Scientist who founded Quant Company and has also worked as a lead Economic analyst in the Central Bank of Guatemala. He is a M.S. in Quantitative Economics and Applied Mathematics and is actively involved in consulting and training in the data science space.


Gaurav Dubey
Gaurav Dubey Associate Consultant at Syntel, Pune

Prior to joining Data Science course with Simplilearn, I had little knowledge about it. The certification helped me to understand the Machine Learning, Web Scraping, Natural Language Processing in detail. The trainer was very helpful and was always there to guide me in every step. The certification helped me to enhance my career from Software Engineer to Associate Consultant with a salary hike. I am planning to take a few more course from Simplilearn in future.

Read more Read less
Jatin Alwani
Jatin Alwani Student at Lovely Professional University, Jalandhar

I have enrolled for Data Science certification from Simplilearn. The course materials are great and the trainers are also very helpful. The industry-based project is the best part of the course. Simplilearn is better than any others in the market.

Read more Read less
Shoeb Mohammad
Shoeb Mohammad Analyst at Accenture, Delhi

I had joined the Data Science certification from Simplilearn. The course content was really good. The trainer puts a lot of efforts into explaining every detail which made the learning very absorbing. The customer support is always available whenever you need help. I actually feel one step forward towards my goal. Thank you.

Read more Read less
Solomon Olutu
Solomon Olutu Snr Principal QA Architect at Comcast, Philadelphia

Simplilearn's Data Science with Python training was a great experience. Their trainers are the best that I have come across since I started learning with Silplilearn. He is always prepared for class with a well-documented note session which is also useful for hands-on learning after class to enhance the learning experience. Thanks Simplilearn. This is the best platform that I have come across.

Read more Read less
Tham Chup Wai
Tham Chup Wai Singapore

I just completed 3 classes under this program - Data Science Using SAS, R and Big Data Hadoop and Spark Developer. I am currently enrolled in Python training. What I like the most is that the live recordings from each class are lifetime references for us to review in the future. The self-running videos in each topic were also very useful as they cover theory which might not have been covered during the live classes. I have made significant gains so far in my knowledge of key technologies and tools in Data Science. Together with electives offered under this program, I will eventually be getting a comprehensive foundation training in Data Science.

Read more Read less


  • What are the system requirements?

    To run Python, your system must fulfill the following basic requirements:
    • 32 or 64-bit Operating System
    • 1GB RAM 
    The instruction uses Anaconda and Jupyter notebooks. The e-learning videos provide detailed instruction on how to install them.

  • Who are our instructors and how are they selected?

    All of our highly qualified trainers are industry experts with at least 10-12 years of relevant teaching experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.

  • What are the modes of training offered for this Python for Data Science course?

    Live Virtual Classroom or Online Classroom: In online classroom training, you have the convenience of attending the course remotely from your desktop via video conferencing to enhance your productivity and reduce the time spent away from work or home.
    Online Self-Learning: In this mode, you will receive lecture videos and can proceed through the course at your convenience.
    WinPython portable distribution is the open source environment on which all hands-on exercises will be performed. Instructions for installation will be given during the training.

  • What if I miss a class?

    Simplilearn provides recordings of each class so you can review them as needed before the next session.

  • 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.

  • Who provides the certification?

    At the end of the training, subject to satisfactory evaluation of the project as well as passing the online exam (minimum score 80%), you will receive a certificate from Simplilearn stating that you are a certified data scientist with Python.

  • Are there any group discounts for classroom training programs?

    Yes, we have group discount packages for classroom training programs. Contact Help & Support to learn more about the group discounts.

  • How do I enroll for the Data Science with Python online training?

    You can enroll for this training on our website and make an online payment using any of the following options: 
    • Visa Credit or Debit Card
    • MasterCard
    • American Express
    • Diner’s Club
    • PayPal 
    Once payment is received you will automatically receive a payment receipt and access information via email.

  • What is Global Teaching Assistance?

    Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours.

  • What is covered under the 24/7 Support promise?

    We offer 24/7 support through email, chat, and calls. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your course with us.

  • * Disclaimer

    * The projects have been built leveraging real publicly available data-sets of the mentioned organizations.

    • Disclaimer
    • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.