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

After you successfully complete your Python for Data Science training in Atlanta, Simplilearn will award you your prestigious certificate with lifetime validity, recognized industry-wide.

What do I need to unlock my Simplilearn certificate?

You can complete your Python for Data Science in Atlanta by choosing either online self-learning or attending the online classroom. To unlock the Simplilearn certificate for: Online Classroom: Participate and complete one entirebatch of classes included in this Python for Data Science course in Atlanta. Submit a minimum of one completed project Online Self-Learning: Finish 85% of the course Complete and submit a minimum of one project

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

As a useful and valuable extra to the Python Data Science training in Atlanta, we provide you with one practice test, a great certification exam prep tool! One can also attempt the Free Data Science with Python Practice Test to gain more understanding about the type of tests in the Python for Data Science course in Atlanta.

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.

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. Python has a rich set of libraries and frameworks that make it an excellent choice for Data Science like Pandas, NumPy, SciPy, Matplotlib.

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.

Data Science with Python Training in Atlanta

13330 Ratings

35759 Learners

The Python for Data Science Training in Atlanta is the best choice to learn about Python's Data Analytics tools and techniques. Knowledge of Python is essential, as it is one of the most widely used programming languages. The popular Python for Data Science course in Atlanta is an essential tool for programmers, as it teaches you the intricacies of the widely-used Python language.

World’s #1 Online Bootcamp

Data Science with Python Course Overview

The Python for Data Science Training in Atlanta course enables a candidate to master all the concepts related to Python programming. This Python for Data Science Course in Atlanta covers the topics that are most important to data science, such as Web Scraping, Data Analysis, NLP, Machine Learning (ML), and Data Visualization.

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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
Source: Indeed
Data Scientist
Annual Salary
$83KMin
$113KAverage
$154KMax
Source: Glassdoor
Hiring Companies
Source: Indeed

Training Options

Self-Paced Learning

$ 799

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

$ 999

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 Atlanta from:-

27th May: Weekday Class

4th Jun: 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 Python for Data Science training in Atlanta course is for those who are serious about a career pertaining to Data Science. This course is perfect for professionals in the fields of software or IT who are interested in pursuing a career in analytics, as well as people who are curious about Data Science.

Pre-requisites

Our Python for Data Science course in Atlanta will help create maximum impact for people with prior programming knowledge and possess knowledge in topics ranging from Data Science introduction, Math refreshers, real-life applications of data science and the essential statistics principles commonly used in Data Science with Python. These topics are also included in the course.

Course Content

Data Science with Python
Preview
- Lesson 00 - Course Overview
  07:45Preview
  - 0.001 Course Overview
    04:34
  - 0.002 Accessing Practice Lab
    03:11
- 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
  18:20
  - 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
  - 2.11 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
  23:58Preview
  - 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
  01:08:28Preview
  - 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
  - 8.9 Knowledge Check
  - 8.010 Supervised Learning Models - Linear Regression
    14:43
  - 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
    08:54
  - 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 Takeaways
    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
  40:30Preview
  - 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
    07:11
  - 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 Shape
    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
  19:46
  - 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 Techniques
    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 Intervals and Percentage 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 4: 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
  11:47Preview
  - Assisted Practice: Problem Statement
    02:10
  - Assisted Practice: Solution
    09:37

Download Syllabus

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.
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.
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.
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.
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.
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.

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Data Science with Python Exam & Certification

Who provides the certification and how long is it valid for?
After you successfully complete your Python for Data Science training in Atlanta, Simplilearn will award you your prestigious certificate with lifetime validity, recognized industry-wide.
What do I need to unlock my Simplilearn certificate?
You can complete your Python for Data Science in Atlanta by choosing either online self-learning or attending the online classroom. To unlock the Simplilearn certificate for:

Online Classroom:
- Participate and complete one entirebatch of classes included in this Python for Data Science course in Atlanta.
- Submit a minimum of one completed project
Online Self-Learning:
- Finish 85% of the course
- Complete and submit a minimum of one project
Do you provide any practice tests as part of Data Science with Python course?
As a useful and valuable extra to the Python Data Science training in Atlanta, we provide you with one practice test, a great certification exam prep tool! One can also attempt the Free Data Science with Python Practice Test to gain more understanding about the type of tests in the Python for Data Science course in Atlanta.

Data Science with Python Course Reviews

Mushtaque Ansari
Senior Software Developer, Bangalore
I had a wonderful experience learning Data Science with Python with Simplilearn. Thank you, Vaishali for explaining concepts theoretically and practically. The live sessions helped me easily understand the concepts.
Arvind Kumar
Technology Lead, Nagpur
It was a great learning experience. My trainer, Vaishali delivered each session well. All topics were explained with in-depth theory, real-time examples, and execution of the same in Python. Her teaching methodology enhanced the learning process.
Vignesh Manikandan
Bangalore
The online classes were well-paced and helped us learn a ton of stuff within a short amount of time. Vaishali is very knowledgeable and handled all the sessions with outstanding professionalism. Thanks for your expertise.
Brian
Program Manager (iGPM RBEI), Bangalore
The training was well-structured, and the trainer was experienced with hands-on know-how. The trainer handled responses and queries efficiently with a good amount of patience.
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
Patna
I completed this 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
Pune
The sessions are very interesting and easy to understand. I enjoyed each and every one of them, thanks to the trainer, Prashant.
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.
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.
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.
Surendaran Baskaran
Coimbatore
I took this 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!!
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.
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.
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.
Satabdi Adhikary
Bangalore
Simplilearn's courses are 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.
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.

Python has a rich set of libraries and frameworks that make it an excellent choice for Data Science like Pandas, NumPy, SciPy, Matplotlib.
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.
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.