The Data Science with Python course empowers you to excel in Python programming. In this course, you'll delve into data science, data analysis, data visualization, data wrangling, feature engineering, and statistics. Upon finishing the course, you'll excel in using essential data science tools with Python.

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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
- 40+ Assisted practices and lesson-wise knowledge checks
- Dedicated live sessions by faculty of industry experts
- Industry-based projects for experiential learning
- Lifetime access to self-paced learning content
- Practical skills and hands-on experience in applying Python to address data science challenges.
- 68 hours of blended learning
- Industry-based projects for experiential learning
- 40+ Assisted practices and lesson-wise knowledge checks
- Lifetime access to self-paced learning content
- Dedicated live sessions by faculty of industry experts
- Practical skills and hands-on experience in applying Python to address data science challenges.
- 68 hours of blended learning
- Industry-based projects for experiential learning
- 40+ Assisted practices and lesson-wise knowledge checks
- Lifetime access to self-paced learning content
- Dedicated live sessions by faculty of industry experts
- Practical skills and hands-on experience in applying Python to address data science challenges.

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

Get lifetime access to self-paced e-learning content

The Data Science Platform Market size is estimated at USD 10.15 billion in 2024 and is expected to reach USD 29.98 billion by 2029, growing at a CAGR of 23.5% during the forecast period (2024-2029).

- Designation
- Annual Salary
- Hiring Companies

- Annual SalarySource: GlassdoorHiring CompaniesSource: Indeed
- Annual SalarySource: GlassdoorHiring CompaniesSource: Indeed

The need for professionals skilled in data science with Python programming has surged, making this course suitable for participants at every experience level. Whether you're an analytics professional looking to delve into Python, a software or IT professional exploring analytics, or anyone with a genuine interest in data science, this course is designed for you.

Learners need to possess an undergraduate degree or a high school diploma. Additionally, a curiosity for data analysis and a desire to explore the applications of Python in the field of data science is highly encouraged.

### Data Science with Python

Preview#### Lesson 01 - Course Introduction

04:59Preview##### 1.01 Course Introduction

03:06##### 1.02 What you will Learn

01:53

#### Lesson 02 - Introduction to Data Science

08:16Preview##### 2.01 Introduction

00:44##### 2.02 Data Science and its Applications

02:41##### 2.03 The Data Science Process: Part 1

02:15##### 2.04 The Data Science Process: Part 2

02:02##### 2.05 Recap

00:34

#### Lesson 03 - Essentials of Python Programming

01:00:18Preview##### 3.01 Introduction

00:58##### 3.02 Setting Up Jupyter Notebook: Part 1

02:02##### 3.03 Setting Up Jupyter Notebook: Part 2

04:14##### 3.04 Python Functions

03:57##### 3.05 Python Types and Sequences

04:50##### 3.06 Python Strings Deep Dive

07:16##### 3.07 Python Demo: Reading and Writing csv files

06:25##### 3.08 Date and Time in Python

02:34##### 3.09 Objects in Python Map

07:42##### 3.10 Lambda and List Comprehension

03:53##### 3.11 Why Python for Data Analysis?

02:09##### 3.12 Python Packages for Data Science

02:44##### 3.13 StatsModels Package: Part 1

02:38##### 3.14 StatsModels Package: Part 2

03:29##### 3.15 Scipy Package

02:47##### 3.16 Recap

00:51##### 3.17 Spotlight

01:49

#### Lesson 04 - NumPy

28:19Preview##### 4.01 Introduction

00:51##### 4.02 Fundamentals of NumPy

02:49##### 4.03 Array shapes and axes in NumPy: Part A

03:27##### 4.04 NumPy Array Shapes and Axes: Part B

03:28##### 4.05 Arithmetic Operations

02:35##### 4.06 Conditional Logic

02:48##### 4.07 Common Mathematical and Statistical Functions in Numpy

04:29##### 4.08 Indexing And Slicing: Part 1

02:27##### 4.09 Indexing and Slicing: Part 2

02:28##### 4.10 File Handling

02:24##### 4.11 Recap

00:33

#### Lesson 05 - Linear Algebra

28:29Preview##### 5.01 Introduction

00:51##### 5.02 Introduction to Linear Algebra

02:46##### 5.03 Scalars and Vectors

01:50##### 5.04 Dot Product of Two Vectors

02:37##### 5.05 Linear independence of Vectors

01:05##### 5.06 Norm of a Vector

01:30##### 5.07 Matrix

03:28##### 5.08 Matrix Operations

03:14##### 5.09 Transpose of a Matrix

00:59##### 5.10 Rank of a Matrix

02:11##### 5.11 Determinant of a matrix and Identity matrix or operator

02:51##### 5.12 Inverse of a matrix and Eigenvalues and Eigenvectors

02:45##### 5.13 Calculus in Linear Algebra

01:34##### 5.14 Recap

00:48

#### Lesson 06 - Statistics Fundamentals

33:50Preview##### 6.01 Introduction

01:00##### 6.02 Importance of Statistics with Respect to Data Science

02:34##### 6.03 Common Statistical Terms

01:46##### 6.04 Types of Statistics

02:50##### 6.05 Data Categorization and Types

03:20##### 6.06 Levels of Measurement

02:37##### 6.07 Measures of Central Tendency

01:51##### 6.08 Measures of Central Tendency

01:48##### 6.09 Measures of Central Tendency

01:02##### 6.10 Measures of Dispersion

02:19##### 6.11 Random Variables

02:17##### 6.12 Sets

02:40##### 6.13 Measures of Shape (Skewness)

02:16##### 6.14 Measures of Shape (Kurtosis)

01:52##### 6.15 Covariance and Correlation

02:44##### 6.16 Recap

00:54

#### Lesson 07 - Probability Distribution

30:18Preview##### 7.01 Introduction

01:02##### 7.02 Probability,its Importance, and Probability Distribution

03:36##### 7.03 Probability Distribution : Binomial Distribution

02:53##### 7.04 Probability Distribution: Poisson Distribution

02:29##### 7.05 Probability Distribution: Normal Distribution

04:19##### 7.06 Probability Distribution: Uniform Distribution

01:30##### 7.07 Probability Distribution: Bernoulli Distribution

03:05##### 7.08 Probability Density Function and Mass Function

02:33##### 7.09 Cumulative Distribution Function

02:26##### 7.10 Central Limit Theorem

02:57##### 7.11 Estimation Theory

02:49##### 7.12 Recap

00:39

#### Lesson 08 - Advanced Statistics

01:07:21Preview##### 8.01 Introduction

01:07##### 8.02 Distribution

01:45##### 8.03 Kurtosis Skewness and Student's T-distribution

02:32##### 8.04 Hypothesis Testing and Mechanism

02:25##### 8.05 Hypothesis Testing Outcomes: Type I and II Errors

01:54##### 8.06 Null Hypothesis and Alternate Hypothesis

01:47##### 8.07 Confidence Intervals

02:01##### 8.08 Margins of error

01:49##### 8.09 Confidence Level

01:31##### 8.10 T - Test and P - values (Lab)

04:50##### 8.11 Z - Test and P - values

05:33##### 8.12 Comparing and Contrasting T test and Z test

03:45##### 8.13 Bayes Theorem

02:24##### 8.14 Chi Sqare Distribution

03:16##### 8.15 Chi Square Distribution : Demo

03:25##### 8.16 Chi Square Test and Goodness of Fit

02:46##### 8.17 Analysis of Variance or ANOVA

02:41##### 8.18 ANOVA Termonologies

02:08##### 8.19 Assumptions and Types of ANOVA

02:53##### 8.20 Partition of Variance using Python

03:06##### 8.21 F - Distribution

02:41##### 8.22 F - Distribution using Python

03:59##### 8.23 F - Test

03:09##### 8.24 Recap

01:19##### 8.25 Spotlight

02:35

#### Lesson 09 - Pandas

41:13Preview##### 9.01 Introduction

00:52##### 9.02 Introduction to Pandas

02:15##### 9.03 Pandas Series

03:37##### 9.04 Querying a Series

04:01##### 9.05 Pandas Dataframes

03:05##### 9.06 Pandas Panel

01:46##### 9.07 Common Functions In Pandas

02:56##### 9.08 Pandas Functions Data Statistical Function, Windows Function

02:18##### 9.09 Pandas Function Data and Timedelta

02:57##### 9.10 IO Tools Explain all the read function

03:15##### 9.11 Categorical Data

02:52##### 9.12 Working with Text Data

03:15##### 9.13 Iteration

02:37##### 9.14 Sorting

01:19##### 9.15 Plotting with Pandas

03:23##### 9.16 Recap

00:45

#### Lesson 10 - Data Analysis

32:25Preview##### 10.01 Introduction

00:46##### 10.02 Understanding Data

02:31##### 10.03 Types of Data Structured Unstructured Messy etc

02:35##### 10.04 Working with Data Choosing appropriate tools, Data collection, Data wrangling

02:53##### 10.05 Importing and Exporting Data in Python

02:42##### 10.06 Regular Expressions in Python

08:24##### 10.07 Manipulating text with Regular Expressions

06:04##### 10.08 Accessing databases in Python

03:32##### 10.09 Recap

00:50##### 10.10 Spotlight

02:08

#### Lesson 11 - Data Wrangling

34:24Preview##### 11.01 Introduction

00:58##### 11.02 Pandorable or Idiomatic Pandas Code

06:21##### 11.03 Loading Indexing and Reindexing

02:45##### 11.04 Merging

05:48##### 11.05 Memory Optimization in Python

03:01##### 11.06 Data Pre Processing: Data Loading and Dropping Null Values

02:34##### 11.07 Data Pre-processing Filling Null Values

02:32##### 11.08 Data Binning Formatting and Normalization

04:46##### 11.09 Data Binning Standardization

02:19##### 11.10 Describing Data

02:17##### 11.11 Recap

01:03

#### Lesson 12 - Data Visualization

42:55Preview##### 12.01 Introduction

00:58##### 12.02 Principles of information visualization

02:27##### 12.03 Visualizing Data using Pivot Tables

02:04##### 12.04 Data Visualization Libraries in Python Matplotlib

01:56##### 12.05 Graph Types

01:36##### 12.06 Data Visualization Libraries in Python Seaborn

01:15##### 12.07 Data Visualization Libraries in Python Seaborn

02:34##### 12.08 Data Visualization Libraries in Python Plotly

01:07##### 12.09 Data Visualization Libraries in Python Plotly

02:51##### 12.10 Data Visualization Libraries in Python Bokeh

02:16##### 12.11 Data Visualization Libraries in Python Bokeh

01:59##### 12.12 Using Matplotlib to Plot Graphs

03:32##### 12.13 Plotting 3D Graphs for Multiple Columns using Matplotlib

02:14##### 12.14 Using Matplotlib with other python packages

03:30##### 12.15 Using Seaborn to Plot Graphs

02:18##### 12.16 Using Seaborn to Plot Graphs

01:15##### 12.17 Plotting 3D Graphs for Multiple Columns Using Seaborn

03:16##### 12.18 Introduction to Plotly

03:29##### 12.19 Introduction to Bokeh

01:32##### 12.20 Recap

00:46

#### Lesson 13 - End to End Statistics Application with Python

35:34Preview##### 13.01 Introduction

01:05##### 13.02 Basic Statistics with Python Problem Statement

01:06##### 13.03 Basic Statistics with Python Solution

11:16##### 13.04 Scipy for Statistics Problem Statement

01:11##### 13.05 Scipy For Statistics Solution

06:10##### 13.06 Advanced Statistics Python

01:10##### 13.07 Advanced Statistics with Python Solution

10:56##### 13.08 Recap

00:29##### 13.09 Spotlight

02:11

- Free Course
### Math Refresher

Preview#### Lesson 01: Course Introduction

06:23Preview##### 1.01 About Simplilearn

00:28##### 1.02 Introduction to Mathematics

01:18##### 1.03 Types of Mathematics

02:39##### 1.04 Applications of Math in Data Industry

01:17##### 1.05 Learning Path

00:25##### 1.06 Course Components

00:16

#### Lesson 02: Probability and Statistics

32:38Preview##### 2.01 Learning Objectives

00:29##### 2.02 Basics of Statistics and Probability

03:08##### 2.03 Introduction to Descriptive Statistics

02:12##### 2.04 Measures of Central Tendencies

04:50##### 2.05 Measures of Asymmetry

02:24##### 2.06 Measures of Variability

04:55##### 2.07 Measures of Relationship

05:22##### 2.08 Introduction to Probability

08:36##### 2.09 Key Takeaways

00:42##### 2.10 Knowledge check

#### Lesson 03: Coordinate Geometry

06:31##### 2.01 Learning Objectives

00:29##### 2.02 Basics of Statistics and Probability

03:08##### 2.03 Introduction to Descriptive Statistics

02:12##### 2.04 Measures of Central Tendencies

04:50##### 2.05 Measures of Asymmetry

02:24##### 2.06 Measures of Variability

04:55##### 2.07 Measures of Relationship

05:22##### 2.08 Introduction to Probability

08:36##### 2.09 Key Takeaways

00:42##### 2.10 Knowledge check

#### Lesson 04: Linear Algebra

29:53Preview##### 4.01 Learning Objectives

00:29##### 4.02 Introduction to Linear Algebra

03:21##### 4.03 Forms of Linear Equation

05:21##### 4.04 Solving a Linear Equation

05:21##### 4.05 Introduction to Matrices

02:05##### 4.06 Matrix Operations

07:07##### 4.07 Introduction to Vectors

01:00##### 4.08 Types and Properties of Vectors

01:52##### 4.09 Vector Operations

02:39##### 4.10 Key Takeaways

00:38##### 4.11 Knowledge Check

#### Lesson 05: Eigenvalues Eigenvectors and Eigendecomposition

08:56Preview##### 5.01 Learning Objectives

00:29##### 5.02 Eigenvalues

01:19##### 5.03 Eigenvectors

04:09##### 5.04 Eigendecomposition

02:21##### 5.05 Key Takeaways

00:38##### 5.06 Knowledge Check

#### Lesson 06: Introduction to Calculus

09:47Preview##### 6.01 Learning Objectives

00:30##### 6.02 Basics of Calculus

01:20##### 6.03 Differential Calculus

03:01##### 6.04 Differential Formulas

01:01##### 6.05 Integral Calculus

02:33##### 6.06 Integration Formulas

00:47##### 6.07 Key Takeaways

00:35##### 6.08 Knowledge Check

- 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

25:49Preview##### 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

01:20##### 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 Applications of Statistics in Business: Time Series Forecasting

03:50##### 2.10 Applications of Statistics in Business Sales Forecasting

03:19##### 2.11 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

34:51Preview##### 4.01 Learning Objectives

01:26##### 4.02 Descriptive Statistics

02:03##### 4.03 Mathematical and Positional Averages

03:15##### 4.04 Measures of Central Tendancy: Part A

02:17##### 4.05 Measures of Central Tendancy: Part B

02:41##### 4.06 Measures of Dispersion

01:15##### 4.07 Range Outliers Quartiles Deviation

02:30##### 4.08 Mean Absolute Deviation (MAD) Standard Deviation Variance

03:37##### 4.09 Z Score and Empirical Rule

02:14##### 4.10 Coefficient of Variation and Its Application

02:06##### 4.11 Measures of Shape

02:39##### 4.12 Summarizing Data

02:03##### 4.13 Recap

00:54##### 4.14 Case Study One: Descriptive Statistics

05:51

#### Lesson 05: Data Visualization

23:36Preview##### 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 Data Visualization: Example

02:41##### 5.10 Recap

00:50##### 5.11 Case Study Two: Data Visualization

03:04

#### Lesson 06: Probability

21:51Preview##### 6.01 Learning Objectives

00:55##### 6.02 Introduction to Probability

03:10##### 6.03 Probability Example

02:02##### 6.04 Key Terms in Probability

02:25##### 6.05 Conditional Probability

02:11##### 6.06 Types of Events: Independent and Dependent

02:59##### 6.07 Addition Theorem of Probability

01:58##### 6.08 Multiplication Theorem of Probability

02:08##### 6.09 Bayes Theorem

03:10##### 6.10 Recap

00:53

#### Lesson 07: Probability Distributions

24:45Preview##### 7.01 Learning Objectives

00:52##### 7.02 Probability Distribution

01:25##### 7.03 Random Variable

02:21##### 7.04 Probability Distributions Discrete vs.Continuous: Part A

01:44##### 7.05 Probability Distributions Discrete vs.Continuous: Part B

01:45##### 7.06 Commonly Used Discrete Probability Distributions: Part A

03:18##### 7.07 Discrete Probability Distributions: Poisson

03:16##### 7.08 Binomial by Poisson Theorem

02:28##### 7.09 Commonly Used Continuous Probability Distribution

03:22##### 7.10 Application of Normal Distribution

02:49##### 7.11 Recap

01:25

#### Lesson 08: Sampling and Sampling Techniques

36:45Preview##### 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 Sampling Stratified: Sampling Example

04:35##### 8.14 Probability Sampling: Example

01:17##### 8.15 Recap

01:07##### 8.16 Case Study Three: Sample and Sampling Techniques

05:16##### 8.17 Spotlight

01:42

#### Lesson 09: Inferential Statistics

37:08Preview##### 9.01 Learning Objectives

01:04##### 9.02 Inferential Statistics

03:09##### 9.03 Hypothesis and Hypothesis Testing in Businesses

03:24##### 9.04 Null and Alternate Hypothesis

01:44##### 9.05 P Value

03:22##### 9.06 Levels of Significance

01:16##### 9.07 Type One and Two Errors

01:37##### 9.08 Z Test

02:24##### 9.09 Confidence Intervals and Percentage Significance Level: Part A

02:52##### 9.10 Confidence Intervals: Part B

01:20##### 9.11 One Tail and Two Tail Tests

04:43##### 9.12 Notes to Remember for Null Hypothesis

01:02##### 9.13 Alternate Hypothesis

01:51##### 9.14 Recap

00:56##### 9.15 Case Study 4: Inferential Statistics

06:24##### Hypothesis Testing

#### Lesson 10: Application of Inferential Statistics

27:20Preview##### 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

04:27##### 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

20:07Preview##### 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 Correlation Example

01:59##### 11.06 Spearmans Rank Correlation Coefficient

02:14##### 11.07 Causation

01:47##### 11.08 Example of Regression

02:28##### 11.09 Coefficient of Determination

01:12##### 11.10 Quantifying Quality

02:29##### 11.11 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

### Who provides the Python Data Science 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.

**Develop skills for real career growth**Cutting-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 trainers**Leading practitioners who bring current best practices and case studies to sessions that fit into your work schedule.**Learn by working on real-world problems**Capstone projects involving real world data sets with virtual labs for hands-on learning**Structured guidance ensuring learning never stops**24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts

### Why learn Python for Data Science?

Python is the most popular programming language for Data Science. Python is widely used to perform data analysis, data manipulation, and data visualization. The advantages of using Python for data science are:

- 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 in Data Science.
- 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.
- Python is a high-level programming language with an enormous community. Its flexibility is quite useful for any issues related to application development in Data Science.

### 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.

### Do I need coding experience to learn Python for Data Science?

If you have prior coding experience or familiarity with any other object-oriented programming language, it will be easier for you to learn Python for Data Science. 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 are the system requirements to install Python for Data Science?

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.

### Which is better for Data Science — R or Python?

Python and R are both popular languages among data scientists. While R is a statistical analysis language, Python is a general-purpose language that has a readable syntax and well-structured code. Data professionals prefer Python for its versatility and R for its better visualization capabilities. However, deciding on the best-suited programming language depends on the nature of the data analysis task you are working on.

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