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

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!

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

- 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 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

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

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.

Learners need to possess an undergraduate degree or a high school diploma. 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.

### Data Science with Python

Preview#### Lesson 01: Course Introduction

09:05Preview##### 1.01 Course Introduction

05:54##### 1.02 Demo Jupyter Lab Walk - Through

03:11

#### Lesson 02: Introduction to Data Science

09:10Preview##### 2.01 Learning Objectives

00:27##### 2.02 Data Science Methodology

01:20##### 2.03 From Business Understanding to Analytic Approach

01:02##### 2.04 From Requirements to Collection

01:06##### 2.05 From Understanding to Preparation

01:10##### 2.06 From Modeling to Evaluation

01:53##### 2.07 From Deployment to Feedback

01:52##### 2.08 Key Takeaways

00:20

#### Lesson 03: Python Libraries for Data Science

01:59:39Preview##### 3.01 Learning Objectives

00:34##### 3.02 Python Libraries for Data Science

01:51##### 3.03 Import Library into Python Program

01:05##### 3.04 Numpy

04:35##### 3.05 Demo Numpy

05:08##### 3.06 Fundamentals of Numpy

02:13##### 3.07 Numpy Array Shapes and axes Part A

02:48##### 3.08 Numpy Array Shapes and axes Part B

03:22##### 3.09 Arithmetic Operations

02:34##### 3.10 Conditional Statements in Python

02:44##### 3.11 Common Mathematical and Statistical Functions in NumPy

04:25##### 3.12 Indexing and Slicing in Python Part A

02:26##### 3.13 Indexing and Slicing in Python Part B

02:25##### 3.14 Introduction to Pandas

01:41##### 3.15 Introduction to Pandas Series

03:37##### 3.16 Querying a Series

03:54##### 3.17 Pandas Dataframe

02:53##### 3.18 Introduction to Pandas Panel

01:45##### 3.19 Common Functions in Pandas

02:20##### 3.20 Statistical Functions in Pandas

01:43##### 3.21 Date and Timedelta

02:18##### 3.22 IO Tools

02:36##### 3.23 Categorical Data

02:09##### 3.24 Working with Text Data

02:34##### 3.25 Iteration

01:54##### 3.26 Plotting with Pandas

03:23##### 3.27 Matplotlib

06:04##### 3.28 Demo Matplotlib

02:09##### 3.29 Data Visualization Libraries in Python Matplotlib

01:30##### 3.30 Graph Types

01:14##### 3.31 Using Matplotlib to Plot Graphs

03:32##### 3.32 Matplotlib for 3d Visualization

02:14##### 3.33 Using Matplotlib with Other Python Packages

01:02##### 3.34 Data Visualization Libraries in Python Seaborn An Introduction

00:58##### 3.35 Seaborn Visualization Features

02:13##### 3.36 Using Seaborn to Plot Graphs

01:40##### 3.37 Analysis using seaborn plots

00:53##### 3.38 Plotting 3D Graphs for Multiple Columns using Seaborn

03:16##### 3.39 SciPy

05:23##### 3.40 Demo Scipy

01:38##### 3.41 Scikit-learn

02:08##### 3.42 Scikit Models

01:25##### 3.43 Scikit Datasets

01:12##### 3.44 Preprocessing Data in Scikit Learn Part 1

01:28##### 3.45 Preprocessing Data in Scikit Learn Part 2

01:45##### 3.46 Preprocessing Data in Scikit Learn Part 3

02:04##### 3.47 Demo Scikit learn

06:20##### 3.48 Key Takeaways

00:34

#### Lesson 04: Statistics

02:29:57Preview##### 4.01 Learning Objectives

00:34##### 4.02 Introduction to Linear Algebra

02:09##### 4.03 Scalars and vectors

01:27##### 4.04 Dot product of Two Vectors

02:02##### 4.05 Linear Independence of Vectors

00:46##### 4.06 Norm of a Vector

01:33##### 4.07 Matrix

02:46##### 4.08 Matrix Operations

02:38##### 4.09 Transpose of a Matrix

00:47##### 4.10 Rank of a Matrix

01:45##### 4.11 Determinant of a matrix and Identity matrix or operator

02:15##### 4.12 Inverse of a matrix and Eigenvalues and Eigenvectors

02:10##### 4.13 Calculus in Linear Algebra

01:14##### 4.14 Importance of Statistics for Data Science

02:00##### 4.15 Common Statistical Terms

01:19##### 4.16 Types of Statistics

02:10##### 4.17 Data Categorization and types of data

02:40##### 4.18 Levels of Measurement

02:04##### 4.19 Measures of central tendency mean

01:33##### 4.20 Measures of Central Tendency Median

01:37##### 4.21 Measures of Central Tendency Mode

01:03##### 4.22 Measures of Dispersion

01:56##### 4.23 Variance

02:14##### 4.24 Random Variables

01:36##### 4.25 Sets

02:03##### 4.26 Measure of Shape Skewness

01:38##### 4.27 Measure of Shape Kurtosis

01:20##### 4.28 Covariance and corelation

02:11##### 4.29 Basic Statistics with Python Problem Statement

00:49##### 4.30 Basic Statistics with Python Solution

10:30##### 4.31 Probability its Importance and Probability Distribution

02:49##### 4.32 Probability Distribution Binomial Distribution

02:13##### 4.33 Binomial Distribution using Python

01:31##### 4.34 Probability Distribution Poisson Distribution

02:08##### 4.35 Poisson Distribution Using Python

01:20##### 4.36 Probability Distribution Normal Distribution

03:17##### 4.37 Probability Distribution Uniform Distribution

01:03##### 4.38 Probability Distribution Bernoulli Distribution

02:27##### 4.39 Probability Density Function and Mass Function

01:57##### 4.40 Cumulative Distribution Function

01:52##### 4.41 Central Limit Theorem

02:22##### 4.42 Bayes Theorem

01:50##### 4.43 Estimation Theory

02:09##### 4.44 Point Estimate using Python

00:45##### 4.45 Distribution

01:11##### 4.46 Kurtosis Skewness and Student's T- distribution

01:46##### 4.47 Hypothesis Testing and mechanism

01:59##### 4.48 Hypothesis Testing Outcomes Type I and II Errors

01:28##### 4.49 Null Hypothesis and Alternate Hypothesis

01:27##### 4.50 Confidence Intervals

01:32##### 4.51 Margin of Errors

01:21##### 4.52 Confidence Levels

01:05##### 4.53 T test and P values Using Python

04:39##### 4.54 Z test and P values Using Python

05:25##### 4.55 Comparing and Contrastin T test and Z-tests

02:54##### 4.56 Chi Squared Distribution

02:32##### 4.57 Chi Squared Distribution using Python

03:18##### 4.58 Chi squared Test and Goodness of Fit

02:16##### 4.59 ANOVA

02:05##### 4.60 ANOVA Terminologies

01:31##### 4.61 Assumptions and Types of ANOVA

02:19##### 4.62 Partition of Variance

02:32##### 4.63 F-distribution

02:01##### 4.64 F Distribution using Python

03:54##### 4.65 F-Test

02:32##### 4.66 Advanced Statistics with Python Problem Statement

00:54##### 4.67 Advanced Statistics with Python Solution

10:06##### 4.68 Key Takeaways

00:38

#### Lesson 05: Data Wrangling

31:32Preview##### 5.01 Learning Objectives

00:42##### 5.02 Data Exploration Loading Files Part A

02:53##### 5.03 Data Exploration Loading Files Part B

01:36##### 5.04 Data Exploration Techniques Part A

02:44##### 5.05 Data Exploration Techniques Part B

02:48##### 5.06 Seaborn

02:19##### 5.07 Demo Correlation Analysis

02:38##### 5.08 Data Wrangling

01:28##### 5.09 Missing Values in a Dataset

01:57##### 5.10 Outlier Values in a Dataset

01:50##### 5.11 Demo Outlier and Missing Value Treatment

04:12##### 5.12 Data Manipulation

00:49##### 5.13 Functionalities of Data Object in Python Part A

01:50##### 5.14 Functionalities of Data Object in Python Part B

01:34##### 5.15 Different Types of Joins

01:34##### 5.16 Key Takeaways

00:38

#### Lesson 06: Feature Engineering

06:57Preview##### 6.01 Learning Objectives

00:28##### 6.02 Introduction to Feature Engineering

01:50##### 6.03 Encoding of Catogorical Variables

00:27##### 6.04 Label Encoding

01:46##### 6.05 Techniques used for Encoding variables

02:11##### 6.06 Key Takeaways

00:15

#### Lesson 07: Exploratory Data Analysis

24:58##### 7.01 Learning Objectives

00:33##### 7.02 Types of Plots

09:38##### 7.03 Plots and Subplots

10:06##### 7.04 Assignment 01 Pairplot Demo

02:28##### 7.05 Assignment 02 Pie Chart Demo

01:52##### 7.06 Key Takeaways

00:21

#### Lesson 08: Feature Selection

06:15Preview##### 8.01 Learning Objectives

00:33##### 8.02 Feature Selection

01:28##### 8.03 Regression

00:54##### 8.04 Factor Analysis

01:58##### 8.05 Factor Analysis Process

01:07##### 8.06 Key Takeaways

00:15

- 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

06:31Preview##### 3.01 Learning Objectives

00:35##### 3.02 Introduction to Coordinate Geometry

03:16##### 3.03 Coordinate Geometry Formulas

01:51##### 3.04 Key Takeaways

00:49##### 3.05 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

08:56##### 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 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.

**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.

### 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 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.

### How much Python is required for 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.

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