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Data Science with Python Course Overview

The Python Data Science course teaches you to master the concepts of Python programming. Through this Data Science with Python certification training, you will learn Data Analysis, Machine Learning, Data Visualization, Web Scraping, & NLP. Upon course completion, you will master the essential tools of Data Science with Python.

Data Science with Python Training Key Features

100% Money Back Guarantee
No questions asked refund*

At Simplilearn, we value the trust of our patrons immensely. But, if you feel that this Data Science with Python course does not meet your expectations, we offer a 7-day money-back guarantee. Just send us a refund request via email within 7 days of purchase and we will refund 100% of your payment, no questions asked!
  • 68 hours of blended learning
  • 4 industry-based projects
  • Interactive learning with Jupyter notebooks labs
  • Lifetime access to self-paced learning
  • Dedicated mentoring session from faculty of industry experts

Skills Covered

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

Benefits

Data Science is an evolving field and Python has become a required skill for 46-percent of jobs in Data Science. According to the US Bearue of Labor Statistics around 11.6 million data science jobs will be created by 2026  and professionals with Python skills will have an additional advantage.

  • Designation
  • Annual Salary
  • Hiring Companies
  • Data Analyst
    Annual Salary
    $43KMin
    $62KAverage
    $95KMax
    Source: Glassdoor
    Hiring Companies
    Amazon hiring for Data Analyst professionals in Philadelphia
    JPMorgan Chase hiring for Data Analyst professionals in Philadelphia
    Genpact hiring for Data Analyst professionals in Philadelphia
    VMware hiring for Data Analyst professionals in Philadelphia
    LarsenAndTurbo hiring for Data Analyst professionals in Philadelphia
    Citi hiring for Data Analyst professionals in Philadelphia
    Accenture hiring for Data Analyst professionals in Philadelphia
    Source: Indeed
  • Data Scientist
    Annual Salary
    $83KMin
    $113KAverage
    $154KMax
    Source: Glassdoor
    Hiring Companies
    Accenture hiring for Data Scientist professionals in Philadelphia
    Oracle hiring for Data Scientist professionals in Philadelphia
    Microsoft hiring for Data Scientist professionals in Philadelphia
    Walmart hiring for Data Scientist professionals in Philadelphia
    Amazon hiring for Data Scientist professionals in Philadelphia
    Source: Indeed

Training Options

Self-Paced Learning

$ 549

  • Lifetime access to high-quality self-paced eLearning content curated by industry experts
  • 4 hands-on projects to perfect the skills learnt
  • 3 simulation test papers for self-assessment
  • Lab access to practice live during sessions
  • 24x7 learner assistance and support

online Bootcamp

$ 599

  • Everything in Self-Paced Learning, plus
  • 90 days of flexible access to online classes
  • Live, online classroom training by top instructors and practitioners
  • Classes starting in Philadelphia from:-
6th Aug: Weekday Class
14th Aug: Weekend Class
Show all classes

Corporate Training

Customized to your team's needs

  • Blended learning delivery model (self-paced eLearning and/or instructor-led options)
  • Flexible pricing options
  • Enterprise grade Learning Management System (LMS)
  • Enterprise dashboards for individuals and teams
  • 24x7 learner assistance and support

Data Science with Python Course Curriculum

Eligibility

The demand for Data Science with Python programming professionals has surged, making this course well-suited for participants at all levels of experience. This Data Science with Python course is beneficial for analytics professionals willing to work with Python, Software, and IT professionals interested in the field of analytics, and anyone with a genuine interest in Data Science.
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Pre-requisites

To best understand the Python Data Science course, it is recommended that you begin with the courses including, Introduction to Data Science in Python, Math Refresher, Data Science in Real Life, and Statistics Essentials for Data Science. These courses are offered as free companions with this training.
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Course Content

  • Data Science with Python

    Preview

    • Lesson 00 - Course Overview

      04:34Preview
      • 0.001 Course Overview
        04:34

    • Lesson 01 - Data Science Overview

      20:27Preview
      • 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

      18:20Preview
      • 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
      • 2.11 Quiz
      • 2.012 Key Takeaways
        00:57
      • 2.10 Knowledge Check

    • Lesson 03 - Statistical Analysis and Business Applications

      23:53Preview
      • 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)

      30:31Preview
      • 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
      • 5.010 Analyse GDP of Countries
      • 5.011 Assignment 01 Demo
        03:55
      • 5.012 Analyse London Olympics Dataset
      • 5.013 Assignment 02 Demo
        03:16
      • 5.14 Quiz
      • 5.015 Key Takeaways
        00:55

    • Lesson 06 - Scientific computing with Python (Scipy)

      23:32Preview
      • 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
      • 6.008 Solving Linear Algebra problem using SciPy
      • 6.009 Assignment 01 Demo
        01:20
      • 6.010 Perform CDF and PDF using Scipy
      • 6.011 Assignment 02 Demo
        00:52
      • 6.12 Quiz
      • 6.013 Key Takeaways
        01:10

    • Lesson 07 - Data Manipulation with Pandas

      47:34Preview
      • 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
      • 7.011 Analyse the Federal Aviation Authority Dataset using Pandas
      • 7.012 Assignment 01 Demo
        04:09
      • 7.013 Analyse NewYork city fire department Dataset
      • 7.014 Assignment 02 Demo
        02:34
      • 7.15 Quiz
      • 7.016 Key Takeaways
        01:34

    • Lesson 08 - Machine Learning with Scikit–Learn

      01:01:55Preview
      • 8.001 Machine Learning Approach
        03:57
      • 8.002 Steps One and Two
        01:00
      • 8.3 Steps Three and Four
      • 8.004 How it Works
        01:24
      • 8.005 Steps Five and Six
        01:54
      • 8.006 Supervised Learning Model Considerations
        00:30
      • 8.008 ScikitLearn
        02:10
      • 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
        02:38
      • 8.014 Model Persistence and Evaluation
        05:45
      • 8.15 Knowledge Check
      • 8.016 Analysing Ad Budgets for different media channels
      • 8.017 Assignment One
        05:45
      • 8.018 Building a model to predict Diabetes
      • 8.019 Assignment Two
        04:58
      • Knowledge Check
      • 8.021 Key Takeaways
        01:12

    • Lesson 09 - Natural Language Processing with Scikit Learn

      49:03Preview
      • 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
      • 9.007 Analysing Spam Collection Data
      • 9.008 Demo Assignment 01
        06:32
      • 9.009 Sentiment Analysis using NLP
      • 9.010 Demo Assignment 02
        08:00
      • 9.11 Quiz
      • 9.012 Key Takeaway
        01:03

    • Lesson 10 - Data Visualization in Python using matplotlib

      32:43Preview
      • 10.001 Introduction to Data Visualization
        08:01
      • 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
        09:32
      • 10.007 Draw a pair plot using seaborn library
      • 10.008 Assignment 01 Demo
        02:23
      • 10.009 Analysing Cause of Death
      • 10.010 Assignment 02 Demo
        01:47
      • 10.11 Quiz
      • 10.012 Key Takeaways
        00:59

    • Lesson 11 - Web Scraping with BeautifulSoup

      52:26Preview
      • 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
        05:37
      • 11.008 Parsing and Printing the Document
        09:05
      • 11.009 Web Scraping of Simplilearn Website
      • 11.010 Assignment 01 Demo
        01:55
      • 11.011 Web Scraping of Simplilearn Website Resource page
      • 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

      40:39Preview
      • 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
      • 12.009 Determine the wordcount
      • 12.010 Assignment 01 Demo
        02:47
      • 12.011 Display all the airports based in New York using PySpark
      • 12.012 Assignment 02 Demo
        03:30
      • 12.13 Quiz
      • 12.014 Key takeaways
        01:20

    • Practice Projects

      • IBM HR Analytics Employee Attrition Modeling.
  • Free Course

  • Math Refresher

    Preview

    • Math Refresher

      30:35Preview
      • Math Refresher
        30:35
  • Free Course

  • Statistics Essential for Data Science

    Preview

    • Lesson 01: Course Introduction

      07:05Preview
      • 1.01 Course Introduction
        05:19
      • 1.02 What Will You Learn
        01:46

    • Lesson 02: Introduction to Statistics

      18:41Preview
      • 2.01 Learning Objectives
        01:16
      • 2.02 What Is Statistics
        01:50
      • 2.03 Why Statistics
        02:06
      • 2.04 Difference between Population and Sample
        01:21
      • 2.05 Different Types of Statistics
        02:42
      • 2.06 Importance of Statistical Concepts in Data Science
        03:20
      • 2.07 Application of Statistical Concepts in Business
        02:11
      • 2.08 Case Studies of Statistics Usage in Business
        03:09
      • 2.09 Recap
        00:46

    • Lesson 03: Understanding the Data

      17:29Preview
      • 3.01 Learning Objectives
        01:12
      • 3.02 Types of Data in Business Contexts
        02:11
      • 3.03 Data Categorization and Types of Data
        03:13
      • 3.03 Types of Data Collection
        02:14
      • 3.04 Types of Data
        02:01
      • 3.05 Structured vs. Unstructured Data
        01:46
      • 3.06 Sources of Data
        02:17
      • 3.07 Data Quality Issues
        01:38
      • 3.08 Recap
        00:57

    • Lesson 04: Descriptive Statistics

      32:03Preview
      • 4.01 Learning Objectives
        01:26
      • 4.02 Mathematical and Positional Averages
        03:15
      • 4.03 Measures of Central Tendancy: Part A
        02:17
      • 4.04 Measures of Central Tendancy: Part B
        02:41
      • 4.05 Measures of Dispersion
        01:15
      • 4.06 Range Outliers Quartiles Deviation
        02:30
      • 4.07 Mean Absolute Deviation (MAD) Standard Deviation Variance
        02:52
      • 4.08 Z Score and Empirical Rule
        02:14
      • 4.09 Coefficient of Variation and Its Application
        02:06
      • 4.10 Measures of Shapes
        02:39
      • 4.11 Summarizing Data
        02:03
      • 4.12 Recap
        00:54
      • 4.13 Case Study One: Descriptive Statistics
        05:51

    • Lesson 05: Data Visualization

      20:55Preview
      • 5.01 Learning Objectives
        00:57
      • 5.02 Data Visualization
        02:15
      • 5.03 Basic Charts
        01:52
      • 5.04 Advanced Charts
        02:19
      • 5.05 Interpretation of the Charts
        02:57
      • 5.06 Selecting the Appropriate Chart
        02:25
      • 5.07 Charts Do's and Dont's
        02:47
      • 5.08 Story Telling With Charts
        01:29
      • 5.09 Recap
        00:50
      • 5.10 Case Study Two: Data Visualization
        03:04

    • Lesson 06: Probability

      19:46Preview
      • 6.01 Learning Objectives
        00:55
      • 6.02 Introduction to Probability
        03:10
      • 6.03 Key Terms in Probability
        02:25
      • 6.04 Conditional Probability
        02:11
      • 6.05 Types of Events Independent and Dependent
        02:56
      • 6.06 Addition Theorem of Probability
        01:58
      • 6.07 Multiplication Theorem of Probability
        02:08
      • 6.08 Bayes Theorem
        03:10
      • 6.09 Recap
        00:53

    • Lesson 07: Probability Distributions

      22:29Preview
      • 7.01 Learning Objectives
        00:52
      • 7.02 Random Variable
        02:21
      • 7.03 Probability Distributions Discrete vs.Continuous: Part A
        01:44
      • 7.04 Probability Distributions Discrete vs.Continuous: Part B
        01:45
      • 7.05 Commonly Used Discrete Probability Distributions: Part A
        03:18
      • 7.06 Discrete Probability Distributions Poisson
        03:16
      • 7.07 Binomial by Poisson Theorem
        01:37
      • 7.08 Commonly Used Continuous Probability Distribution
        03:22
      • 7.09 Applicaton of Normal Distribution
        02:49
      • 7.10 Recap
        01:25

    • Lesson 08: Sampling and Sampling Techniques

      30:53Preview
      • 8.01 Learnning Objectives
        00:51
      • 8.02 Introduction to Sampling and Sampling Errors
        03:05
      • 8.03 Advantages and Disadvantages of Sampling
        01:31
      • 8.04 Probability Sampling Methods: Part A
        02:32
      • 8.05 Probability Sampling Methods: Part B
        02:27
      • 8.06 Non-Probability Sampling Methods: Part A
        01:42
      • 8.07 Non-Probability Sampling Methods: Part B
        01:25
      • 8.08 Uses of Probability Sampling and Non Probability Sampling
        02:08
      • 8.09 Sampling
        01:08
      • 8.10 Probability Distribution
        02:53
      • 8.11 Theorem Five Point One
        00:52
      • 8.12 Center Limit Theorem
        02:14
      • 8.13 Recap
        01:07
      • 8.14 Case Study Three: Sample and Sampling Technique
        05:16
      • 8.15 Spotlight
        01:42

    • Lesson 09: Inferential Statistics

      33:59Preview
      • 9.01 Learning Objectives
        01:04
      • 9.02 Hypothesis and Hypothesis Testing in Businesses
        03:24
      • 9.03 Null and Alternate Hypothesis
        01:44
      • 9.04 P Value
        03:22
      • 9.05 Levels of Significance
        01:16
      • 9.06 Type One and Two Errors
        01:37
      • 9.07 Z Test
        02:24
      • 9.08 Confidence Level Significance Level: Part A
        02:52
      • 9.09 Confidence Intervals: Part B
        01:20
      • 9.10 One Tail and Two Tail Tests
        04:43
      • 9.11 Notes to Remember for Null Hypothesis
        01:02
      • 9.12 Alternate Hypothesis
        01:51
      • 9.13 Recap
        00:56
      • 9.14 Case Study Four: Inferential Statistics
        06:24
      • Hypothesis Testing

    • Lesson 10: Application of Inferential Statistics

      27:07Preview
      • 10.01 Learning Objectives
        00:50
      • 10.02 Bivariate Analysis
        02:01
      • 10.03 Selecting the Appropriate Test for EDA
        02:29
      • 10.04 Parametric vs. Non-parametric Tests
        01:54
      • 10.05 Test of Significance
        01:38
      • 10.06 Z Test
        04:14
      • 10.07 T Test
        00:54
      • 10.08 Parametric Tests ANOVA
        03:26
      • 10.09 Chi-Square Test
        02:31
      • 10.10 Sign Test
        01:58
      • 10.11 Kruskal Wallis Test
        01:04
      • 10.12 Mann Whitney Wilcoxon Test
        01:18
      • 10.13 Run Test for Randomness
        01:53
      • 10.14 Recap
        00:57

    • Lesson 11: Relation between Variables

      18:08Preview
      • 11.01 Learning Objectives
        01:06
      • 11.02 Correlation
        01:54
      • 11.03 Karl Pearson's Coefficient of Correlation
        02:36
      • 11.04 Karl Pearsons: Use Cases
        01:30
      • 11.05 Spearmans Rank Correlation Coefficient
        02:14
      • 11.06 Causation
        01:47
      • 11.07 Example of Regression
        02:28
      • 11.08 Coefficient of Determination
        01:12
      • 11.09 Quantifying Quality
        02:29
      • 11.10 Recap
        00:52

    • Lesson 12: Application of Statistics in Business

      17:25Preview
      • 12.01 Learning Objectives
        00:53
      • 12.02 How to Use Statistics In Day to Day Business
        03:29
      • 12.03 Example: How to Not Lie With Statistics
        02:34
      • 12.04 How to Not Lie With Statistics
        01:49
      • 12.05 Lying Through Visualizations
        02:15
      • 12.06 Lying About Relationships
        03:31
      • 12.07 Recap
        01:06
      • 12.08 Spotlight
        01:48

    • Lesson 13: Assisted Practice

      11:47
      • Assisted Practice: Problem Statement
        02:10
      • Assisted Practice: Solution
        09:37

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Industry Project

  • Project 1

    Products rating prediction for Amazon

    Help Amazon, a US-based e-commerce company, improve its recommendation engine by predicting ratings for the non-rated products and adding them to recommendations accordingly.

    Products rating prediction for Amazon
  • Project 2

    Demand Forecasting for Walmart

    Predict accurate sales for 45 Walmart stores, considering the impact of promotional markdown events. Check if macroeconomic factors have an impact on sales.

    Demand Forecasting for Walmart
  • Project 3

    Improving customer experience for Comcast

    Provide Comcast, a US-based global telecom company, key recommendations to improve customer experience by identifying and improving problem areas that lower customer satisfaction.

    Improving customer experience for Comcast
  • Project 4

    Attrition Analysis for IBM

    IBM, a leading US-based IT company, wants to identify the factors that influence employee attrition by building a logistics regression model that can help predict employee churn.

    Attrition Analysis for IBM
  • Project 5

    NYC 311 Service Request Analysis

    Perform a service request data analysis of New York City 3-1-1 calls. Focus on data wrangling techniques to understand patterns in the data and visualize the major complaint types.

    NYC 311 Service Request Analysis
  • Project 6

    MovieLens Dataset Analysis

    A research team is working on information filtering, collaborative filtering, and recommender systems. Perform analysis using Exploratory Data Analysis technique for user datasets.

    MovieLens Dataset Analysis
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Data Science with Python Exam & Certification

Data Science with Python Training in Philadelphia

  • Who provides the certification and how long is it valid for?

    Once you successfully complete the Data Science with Python training, Simplilearn will provide you with an industry-recognized course completion certificate which will have a lifelong validity.

  • What do I need to unlock my Simplilearn certificate?

    Online Classroom:

    • Attend one complete batch of Data Science with Python training.
    • Submit at least one completed project.

    Online Self-Learning:

    • Complete 85% of the course
    • Submit at least one completed project.

  • Do you provide any practice tests as part of Data Science with Python course?

    Yes, we provide 1 practice test as part of our Data Science with Python course to help you prepare for the actual certification exam. You can try this Free Data Science with Python Practice Test to understand the type of tests that are part of the course curriculum.  

Data Science with Python Course Reviews

  • Darshan Gajjar

    Darshan Gajjar

    Gandhinagar

    I learned a lot about Python, Numpy, Pandas, Visualization. The instructor, Swagat was excellent in explaining things clearly. The support team is also accommodative and resolves issues instantly.

  • Aashish Kumar

    Aashish Kumar

    Patna

    I completed the Data Science with Python course at Simplilearn. The faculty, Prashanth Nair, was extremely knowledgeable, and the entire class appreciated his way of teaching. Simplilearn's support team was very accommodating and quick in providing responses. I was able to grab a 30% hike in my salary after getting certified.

  • Nikhil Lohakare

    Nikhil Lohakare

    Pune

    The sessions are very interesting and easy to understand. I enjoyed each and every one of them, thanks to the trainer, Prashant.

  • Mukesh Pandey

    Mukesh Pandey

    Hyderabad

    Simplilearn is an excellent platform for online learning. Their course curriculum is comprehensive and up to date. We get lifetime access to the recorded sessions in case we need to refresh our understanding. If you are looking to upskill, I suggest you sign up with Simplilearn. They offer classes in almost all disciplines.

  • C Muthu Raman

    C Muthu Raman

    Pune

    Simplilearn facilitates a brilliant platform to acquire new & relevant skills at ease. Well laid out course content and expert faculty ensure an excellent learning experience.

  • Dastagiri Durgam

    Dastagiri Durgam

    Hyderabad

    Incredible mentorship, and amazing and unique lectures. Simplilearn provides a great way to learn with self-paced videos and recordings of online sessions. Thanks, Simplilearn, for providing quality education.

  • Surendaran Baskaran

    Surendaran Baskaran

    Coimbatore

    I took the Data Science with Python course with Simplilearn. The instructor is knowledgeable and shares their skills and knowledge. My learning experience has been outstanding with Simplilearn. The practice labs and materials are helpful for better learning. Thank you, Simplilearn. Happy Learning!!

  • Akash Raj

    Akash Raj

    Technology Engineer, Bangalore

    The instructor not only delivers the lecture but also focuses on practical aspects related to the subject. This is something about the course that really impressed me.

  • Shiv Sharma

    Shiv Sharma

    Mumbai

    Prashant Nair is an awesome faculty. The way he simplifies, relates and explains topics is outstanding. I would love to enroll for and attend all his classes.

  • Kiran Kumar

    Kiran Kumar

    Bangalore

    I recently enrolled in the Data Scientist Master’s Program at Simplilearn. The syllabus is systematically structured, and the Live sessions are explained with real-time examples. This makes the course more accessible to freshers with basic knowledge. Looking forward to completing it. Thanks, Simplilearn Team.

  • Shweta Chauhan

    Shweta Chauhan

    Bangalore

    Thanks a lot, Sunny, for the immense support and guidance throughout the project, and for your patience while calmly helping me fix both small and big problems. You have excellent and in-depth knowledge about Python and the alternative options you taught me. I'm delighted to share my opinion about my experience.

  • Rajesh Dubey

    Rajesh Dubey

    Bangalore

    Simplilearn is a great learning platform for aspirants. It provides online learning with global intellectual faculties. It offers unparallel service and is the best place to attain professional knowledge and grow in one's career. I am very, very thankful to Simplilearn.

  • V S Baanu Prasanna Ramesh

    V S Baanu Prasanna Ramesh

    Bangalore

    Simplilearn helped me in learning Data Science with Python. It gave me the required confidence to restart my career after a break. The best part was its flexibility and the freedom to learn at your own place and pace. Thank you so much!

  • 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.

  • Satabdi Adhikary

    Satabdi Adhikary

    Bangalore

    Simplilearn's Data Science course was affordable and helped me learn something new during the lockdown. Moreover, I also got to add a Well-Known Global Name like Simplilearn to my resume. I could choose the trainer as well as enroll for multiple sessions using the Flexible Pass.

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Why Online Bootcamp

  • Develop skills for real career growthCutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills
  • Learn from experts active in their field, not out-of-touch trainersLeading practitioners who bring current best practices and case studies to sessions that fit into your work schedule.
  • Learn by working on real-world problemsCapstone projects involving real world data sets with virtual labs for hands-on learning
  • Structured guidance ensuring learning never stops24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts

Data Science with Python Training FAQs

  • 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.

  • Do I need coding experience to learn Python?

    If you have prior coding experience or familiarity with any other object-oriented programming language, it will be easier for you to learn Python. However, it is not compulsory.

  • I have familiarity in other programming languages like C++/Java. Will the Data Science with Python course help me to switch to Python?

    Python has simple syntax and is easy to understand. Knowledge of Java or C++ language helps in learning Python faster. This is because Python is also object-oriented and many of its prototypes are similar to Java. So you can easily migrate to Python with this comprehensive course.

  • What level of expertise in Python do I need to reach to learn data science?

    Python is used for a variety of applications and you don’t need to be familiar with all of its libraries and modules. Even if you know the basics of Python, this Data Science with Python certification covers the popular libraries of Python that are used in data science projects.

  • Does Python support any open-source libraries?

    Yes, Python supports a lot of open-source libraries like SciPy, NumPy, Scikit-Learn, TensorFlow, Matplotlib, and Pandas.

  • Does the knowledge imparted through this Data Science with Python certification apply to Machine Learning and Data Science projects?

    Yes, our Data Science with Python course is specifically designed to impart industry-oriented skills. The course material, practice with integrated labs, and real-world projects enhance your practical knowledge and help you apply them to Data Science projects.

  • How can I get started with this Data Science with Python course?

    It is beneficial if you brush up your skills in core math, statistics, and programming basics to get started with this Data Science with Python course.

  • Which companies use Python?

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

  • How do Data Scientists use Python in daily work?

    Data scientists handle a variety of tasks in their day-to-day routine. They gather, merge, and analyze data and identify trends and patterns. They also build and test new algorithms to simplify data problems. Python is used along with other tools to perform all these tasks.

  • Why is Python preferred for Data Science?

    Python is a high-level programming language with an enormous community. Its flexibility is quite useful for any issues related to application development. It has a rich set of libraries and frameworks that make it an excellent choice for Data Science like Pandas, NumPy, SciPy, Matplotlib.

  • 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 instructions on how to install them.

  • Who are our instructors and how are they selected?

    All of our highly qualified Data Science 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 Data Science course?

    Live Virtual Classroom or Online Classroom: In online classroom training, you have the convenience of attending the Python Data Science 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.

  • Is this live training, or will I watch pre-recorded videos?

    If you enroll in the self-paced e-learning training program, you will have access to pre-recorded videos. However, if you enroll for the Online Classroom Flexi-Pass, you will have access to both instructor-led Data Science with Python training conducted online as well as the pre-recorded videos.

  • 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.

  • 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 group discounts.

  • How do I enroll for Python Data Science course?

    You can enroll for this Data Science with Python certification 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.

  • Whom should I contact to learn more about this Python Data Science course?

    Contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link. Our customer service representatives can provide you with more details.

  • What is Global Teaching Assistance?

    Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in Data Science on 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 Python Data Science course with us.

  • What is the recommended learning path after completing Data Science with Python course?

    You can either enroll in our Data Scientist Course or if you are looking to get a University certificate, you can enroll in the Post Graduate Program in Data Science.

  • * Disclaimer

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

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