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

Successfully finish your Python for Data Science training in London, and you will receive industry-wide recognition in the form of the certificate with lifetime validity, provided by Simplilearn.

What do I need to unlock my Simplilearn certificate?

There are two methods you can pick from in order to complete your Python for Data Science in London: going with the online classroom or picking the online self-learning method. To unlock the Simplilearn certificate for: Online Classroom: Take and finish an entire set of program module(s) which are part of our Python for Data Science course in London. 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?

Practice makes perfect, so to that end, in addition to everything else you get in the Python Data Science training in London, we offer you a one-shot practice test, a terrific way to practice for the real certification exam, 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 London.

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 in London 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 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.

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.

How do I enroll for Python Data Science course in London?

You can enroll for this Python for Data Science training in London 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.

What is the Salary of Data Science and Business Analytics in London?

The Data Science and Business analytics Jobs in London can be pursued with Python for data science training London. The average salary for data science and business analytics in London is expected to be £52,019 per year. The highest salary for this job role in London is £78,382 per year and the lowest salary for this in London is £34,523 per year.

Data Science with Python Training in London, UK

15893 Ratings

45945 Learners

The Python for Data Science Training in London 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. Programmers should take the Python for Data Science course in London because it instructs you in the basics of the extremely popular Python programming language.

World’s #1 Online Bootcamp

9/10 of our learners achieve their learning objectives after successful course completion*

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Data Science with Python Course Overview

The Python for Data Science Training in London course enables a candidate to master all the concepts related to Python programming. The Python for Data Science Course in London explores the subjects most vital to the field of data science, such as Data Visualization, Web Scraping, Data Analysis, NLP, and Machine Learning (ML).

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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

£ 645

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

£ 645

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 London from:-

4th Feb: 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 London course is for those who are serious about a career pertaining to Data Science. You won't find a better course choice for IT/software professionals who want a career in analytics, and anyone interested in learning about Data Science.

Pre-requisites

Simplilearn's Python for Data Science course in London provides the best results for those professionals who have a background in programming and have fundamental understanding of data science, including statistics essentials for Data Science with Python, and the real-life applications, plus a strong grasp of fundamental mathematics. These topics are also included in the course.

Course Content

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
  06:31
  - 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
  08:56Preview
  - 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
  18:40Preview
  - 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 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
  32:48Preview
  - 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
    03:37
  - 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
  19:49Preview
  - 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
    02:59
  - 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
  23:20Preview
  - 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
    02:28
  - 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
  27:20Preview
  - 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
  18:08Preview
  - 11.01 Learning Objectives
    01:06
  - 11.02 Correlation
    01:54
  - 11.03 Karl Pearson's Coefficient of Correlation
    02:36
  - 11.04 Karl Pearsons: Use Cases
    01:30
  - 11.05 Spearmans Rank Correlation Coefficient
    02:14
  - 11.06 Causation
    01:47
  - 11.07 Example of Regression
    02:28
  - 11.08 Coefficient of Determination
    01:12
  - 11.09 Quantifying Quality
    02:29
  - 11.10 Recap
    00:52
- Lesson 12: Application of Statistics in Business
  17:25Preview
  - 12.01 Learning Objectives
    00:53
  - 12.02 How to Use Statistics In Day to Day Business
    03:29
  - 12.03 Example: How to Not Lie With Statistics
    02:34
  - 12.04 How to Not Lie With Statistics
    01:49
  - 12.05 Lying Through Visualizations
    02:15
  - 12.06 Lying About Relationships
    03:31
  - 12.07 Recap
    01:06
  - 12.08 Spotlight
    01:48
- Lesson 13: Assisted Practice
  11:47
  - Assisted Practice: Problem Statement
    02:10
  - Assisted Practice: Solution
    09:37

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?
Successfully finish your Python for Data Science training in London, and you will receive industry-wide recognition in the form of the certificate with lifetime validity, provided by Simplilearn.
What do I need to unlock my Simplilearn certificate?
There are two methods you can pick from in order to complete your Python for Data Science in London: going with the online classroom or picking the online self-learning method. To unlock the Simplilearn certificate for:

Online Classroom:
- Take and finish an entire set of program module(s) which are part of our Python for Data Science course in London.
- 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?
Practice makes perfect, so to that end, in addition to everything else you get in the Python Data Science training in London, we offer you a one-shot practice test, a terrific way to practice for the real certification exam, 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 London.

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.
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.
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.
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.
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.
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.
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

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 in London 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 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.
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.
How do I enroll for Python Data Science course in London?
You can enroll for this Python for Data Science training in London 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.
What is the Salary of Data Science and Business Analytics in London?
The Data Science and Business analytics Jobs in London can be pursued with Python for data science training London. The average salary for data science and business analytics in London is expected to be £52,019 per year. The highest salary for this job role in London is £78,382 per year and the lowest salary for this in London is £34,523 per year.
What are the major companies hiring for Data Science and Business Analytics in London?
There are numerous major companies situated in London that are providing Data Science and Business Analytics in London for people with Python for data science training London. Let us have a look at some of the major companies hiring in London:
- Barclays
- NatWest Group
- Zoopla
- Facebook
- LEGO
What are the major Industries in London?
There are numerous industries in London which assists in improving its economy. Every day one or the other industry is rising high and each one of them require its own qualification. Here are some of the major industries in London:
- Business Sector- This sector is elevating with speed in London, the data science and business analytics is a major part of this sector. The Python for data science training London will help you learn more about this field.
- Technology
- Media
- Financial Services
- Tourism
How to become a Data Scientist and Business Analyst in London?
You can easily become a data scientist and business analyst easily by pursuing the best course like Python for data science training London online from the best institution. The most efficient courses will assist you in becoming the best data scientist and business Analyst in London.
How to find Data Science and Business Analytics courses in London?
You can find the Data Science and Business Analytics courses in London through the online platforms. The best institutes will suggest you the best courses to choose for this field like the Python for data science training London. You can surf the internet to know more about the courses that available in London.
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.
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 for Data Science?
Major companies like Google, Instagram, Goldman Sachs, Facebook, Quora, Netflix, Dropbox, and PayPal use Python for Data Science.
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.
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.
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 Professional Certificate Program in Data Science.
Disclaimer
The projects have been built leveraging real publicly available data-sets of the mentioned organizations.