The Python for Data Science Training in Bangalore will help you understand the concepts of Python programming. Thanks to this Python for Data Science course in Bangalore, you can become skilled in Machine Learning (ML), NLP, Data Analysis, Web Scraping, Data Visualization.

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
- 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

Python for Data Science Training in Bangalore will help you master Python. There is expected to be 11.6 million data science jobs by 2026, going by the US Bureau of Labor Statistics, and professionals possessing the unique training and credentials provided by Simplilearn's Python for Data Science course in Bangalore will race ahead of their competition.

- Designation
- Annual Salary
- Hiring Companies

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

Python for Data Science training in Bangalore is a key element for successful Data Analytics professionals. The course will also benefit software and IT professionals who are interested in the field of analytics and anyone who has a genuine interest in the field of Data Science.

In order to get the most out of the Python for Data Science in Bangalore, we recommend that you begin with courses that cover subjects like Math Refresher, Statistics Essentials for Data Science, Data Science in Python, and Data Science in Real Life. These concepts are also covered in the course.

### 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

11:47##### Assisted Practice: Problem Statement

02:10##### Assisted Practice: Solution

09:37

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

After completing our Python for Data Science training in Bangalore successfully, you will have with you an industry-accepted and much sought-after certificate for completing Simplilearn Python for Data Science course in Bangalore which is valid for your entire career worldwide

### What do I need to unlock my Simplilearn certificate?

When you take the Python for Data Science training in Bangalore, you have two ways you can complete it: you can do the online classroom or the onlline self-learning option. To unlock your Simplilearn certificate for:

Online Classroom:

- Attend one complete batch of the Python
**for Data Science course in Bangalore**Submit at least one completed project

Online Self-Learning:

- Complete 85% of the
**Python for Data Science course in Bangalore** - Submit at least one completed project.

- Attend one complete batch of the Python
### Do you provide any practice tests as part of Data Science with Python course?

Yes, you get a practice test when you take the Python for Data Science training in Bangalore. This test helps in preparation for the actual certification exam. You can also try the Free Data Science with Python Practice Test to learn and understand the type of tests that are part of the Python for Data Science course in the Bangalore 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

### What is the importance of learning 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.

When learning about Data Science with Python, you will gain a clear understanding of Python topics like functions, classes, lists, dictionaries, sets, tuples, and various Python libraries. Further, you will go through concepts like mathematical computing, data visualization, data exploration, data analysis, web scraping, machine learning, and feature engineering.

### 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 Data Science with Python training in Bangalore 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 are the course objectives?

The achievement of Python lies in the fact that the trainers in the US now introduce computer science and programming using the language Python and not the long-renowned Java language. It is also estimated that Python has become a necessary skill to be mastered for 46% of data science jobs.The Data Science with Python training in Bangalore gives a complete overview of Python language and explores the various packages and libraries of Python that are necessary to implement natural language processing, web scraping, data analysis, machine learning, and data analysis.

### What skills will you learn in this Data Science with Python course in Bangalore?

At the end of this Data Science with Python course in Bangalore, candidates will be able to:

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

### Who should take this Python Data Science training in Bangalore?

The Python Data Science training in Bangalore comes with no prerequisite. Concerning the assistance for coding, there is an additional course on Python basics included in the main course.

The world of Data Science has the capability of generating potential job opportunities for data scientists, specifically for professionals like:

- Experienced professionals who would like to harness data science in their fields
- IT professionals interested in pursuing a career in analytics
- Analytics professionals who want to work with Python
- Anyone with a genuine interest in the field of data science
- Graduates looking to build a career in analytics and data science
- Software professionals looking to get into the field of analytics

### What projects included with this Python for Data Science certification course?

The Data Science with Python course in Bangalore incorporates 4 industry-oriented, real-life projects. The candidates need to complete one out of the four projects as part of the course completion process. The project will then be accessed by our subject matter experts. The projects are listed below:

**Project 1: NYC 311 Service Request Analysis****Telecommunication:**Perform a service request data analysis of New York City 311 calls. You will focus on data wrangling techniques to understand patterns in the data and visualize the major complaint types.**Project 2: MovieLens Dataset Analysis****Engineering:**The GroupLens Research Project is a research group in the Department of Computer Science and Engineering at the University of Minnesota. The researchers of this group are involved in several research projects in the fields of information filtering, collaborative filtering, and recommender systems. Here, we ask you to perform an analysis using the Exploratory Data Analysis technique for user datasets.**Project 3: Stock Market Data Analysis****Stock Market:**As a part of this project, you will import data using Yahoo data reader from the following companies: Yahoo, Apple, Amazon, Microsoft, and Google. You will perform fundamental analytics, including plotting, closing price, plotting stock trade by volume, performing daily return analysis, and using pair plot to show the correlation between all of the stocks.**Project 4: Titanic Dataset Analysis****Hazard:**On April 15, 1912, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This tragedy shocked the world and led to better safety regulations for ships. Here, we ask you to perform an analysis using the exploratory data analysis technique, in particular applying machine learning tools to predict which passengers survived the tragedy.

Bangalore, the state capital, is India's IT hub and the world's fourth largest technical cluster. Because of its industrial diversity, Bangalore has earned the title of "India's Garment Capital." In the region, there are 47 IT parks and three software parks. The establishment of dedicated IT investment zones helps the country's rapidly developing IT sector.

Bangalore has a tropical savanna climate with unique temperature and rainfall patterns. The city has a much warmer climate throughout the year due to its high elevation, although occasional severe heat may make summer unpleasant.

Bangalore metropolitan region's economy has ranged between $45 billion to $83 billion. In 2014, Bangalore generated $45 billion in IT shipments, accounting for 38% of India's total. In 2017, the IT and IT-enabled services businesses in Bengaluru engaged over 1.5 million people, out of a total workforce of around 4.36 million in India.

Bangalore is well-known as an IT hub, but it is also a tourism destination. The busy town attracts visitors with its lush greenery, earning it the moniker, "Garden City."

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