Applied Data Science with Python

Master Python Programming for Data Science

65.9K Learners

Aligned to

Python

Group Enrollment with Friends or Colleagues |Get a quote

Aligned to

Python

Data Science with Python Course Overview

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

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!
  • 60+ hours of blended learning
  • 40+ Assisted practices and lesson-wise knowledge checks
  • Lifetime access to self-paced learning content
  • Industry-based projects for experiential learning
  • Interactive learning with Jupyter notebooks labs
  • Dedicated live sessions by faculty of industry experts
  • 60+ hours of blended learning
  • Industry-based projects for experiential learning
  • 40+ Assisted practices and lesson-wise knowledge checks
  • Interactive learning with Jupyter notebooks labs
  • Lifetime access to self-paced learning content
  • Dedicated live sessions by faculty of industry experts
  • 60+ hours of blended learning
  • Industry-based projects for experiential learning
  • 40+ Assisted practices and lesson-wise knowledge checks
  • Interactive learning with Jupyter notebooks labs
  • Lifetime access to self-paced learning content
  • Dedicated live sessions by faculty of industry experts

Skills Covered

  • 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

Begin your journey to success

Get lifetime access to self-paced e-learning content

Benefits

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

  • Designation
  • Annual Salary
  • Hiring Companies
  • Data Analyst
    Annual Salary
    $62KMin
    $82KAverage
    $96KMax
    Source: Glassdoor
    Hiring Companies
    Amazon
    JPMorgan Chase
    Genpact
    VMware
    LarsenAndTurbo
    Citi
    Accenture
    Source: Indeed
  • Data Scientist
    Annual Salary
    $100KMin
    $156KAverage
    $217KMax
    Source: Glassdoor
    Hiring Companies
    Accenture
    Oracle
    Microsoft
    Walmart
    Amazon
    Source: Indeed

Training Options

Self Paced Learning

  • Lifetime access to high-quality self-paced eLearning content curated by industry experts
  • 2 hands-on projects and 8+ demo projects to perfect the skills learned
  • 3 simulation test papers for self-assessment
  • Lab access to practice live during sessions
  • 24x7 learner assistance and support

23% Off$650$845

online Bootcamp

  • Everything in Self-Paced Learning, plus
  • Flexi Pass Enabled: Flexibility to reschedule your cohort within first 90 days of access.
  • Live, online classroom training by top instructors and practitioners
  • Batch starting from:
12th Jul, Weekend Class
21st Jul, Weekday Class
View All Schedules

33% Off$750$1,125

Corporate Training

Upskill or reskill your teams

  • Flexible pricing & billing options
  • Private cohorts available
  • Training progress dashboards
  • Skills assessment & benchmarking
  • Platform integration capabilities
  • Dedicated customer success manager

Data Science with Python Course Curriculum

Eligibility

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

Pre-requisites

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

Course Content

  • Data Science with Python

    Preview

    • Lesson 01 - Course Introduction

      Preview
      • 1.01 Course Introduction
        03:06
      • 1.02 What you will Learn
        01:53

    • Lesson 02 - Introduction to Data Science

      Preview
      • 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

      Preview
      • 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

      Preview
      • 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

      Preview
      • 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

      Preview
      • 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

      Preview
      • 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

      Preview
      • 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

      Preview
      • 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

      Preview
      • 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

      Preview
      • 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

      Preview
      • 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

      Preview
      • 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

  • 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

Contact Us

1-844-532-7688

( Toll Free )

Industry Projects

  • Project 1

    Sales Analysis for Business Growth

    Analyze the sales data of a retail clothing company and support the management in formulating their sales and growth strategy.

  • Project 2

    Marketing Campaign Analysis

    Perform exploratory data analysis and hypothesis testing to better understand the various factors contributing to customer acquisition.

  • Project 3

    Real Estate Data Visualization

    Analyze the housing dataset using various types of plots to gain insights into the data.

  • Project 4

    Housing Price Analysis

    Analyze housing data to uncover insights into house prices, comprehend the elements influencing house prices, and understand the impact of various house features on their price.

  • Project 5

    Customer Behaviour Analysis

    Utilize various probability distributions to analyze customer behaviors and store performance metrics using a custom dataset.

prevNext

Data Science with Python Exam & Certification

Applied Data Science with Python

  • Who provides data science with Python certification?

    The data science with Python certification is provided by Simplilearn. After completing the course, learners will receive a completion certificate. This industry-recognized course has lifelong validity. This certificate demonstrates your expertise in data science concepts using Python and acts as a valuable addition to your resume.

  • What do I need to unlock my Simplilearn certificate?

    The requirements depend on your chosen learning mode:

    • Online Classroom: Attend one complete batch of the data science with Python training and submit at least one completed project
    • Online Self-Learning: Complete at least 85% of the course content and submit at least one completed project

Data Science with Python Course Reviews

  • Mushtaque Ansari

    Mushtaque Ansari

    Senior Software Developer

    I thoroughly enjoyed my Data Science with Python course at Simplilearn. Vaishali's adept teaching, blending theory with practical applications in live sessions, made complex concepts clear. Grateful for the enriching experience!

  • Arvind Kumar

    Arvind Kumar

    Technology Lead

    Attending the data science with Python course was enriching. Vaishali, my instructor, adeptly led each session. All topics were explained with in-depth theory, real-time examples, and practical Python applications. Her teaching approach truly amplified the learning journey.

  • Vignesh Manikandan

    Vignesh Manikandan

    The data science with python course offered online was excellently structured, facilitating rapid learning. Vaishali's expertise and professionalism ensured each session was insightful. Grateful for the wealth of knowledge gained in such a short span.

  • Brian

    Brian

    Program Manager (iGPM RBEI)

    The training for the data science with Python course was meticulously organized, led by a seasoned instructor proficient in practical application. The trainer handled responses and queries efficiently with a good amount of patience, ensuring a smooth learning experience.

  • Darshan Gajjar

    Darshan Gajjar

    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

    Aashish Kumar

    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

    Nikhil Lohakare

    The sessions are very interesting and easy to understand. I enjoyed each and every one of them, thanks to the trainer, Prashant.

  • Mukesh Pandey

    Mukesh Pandey

    Simplilearn is an excellent platform for online learning. Their course curriculum is comprehensive and up to date. We get lifetime access to the recorded sessions in case we need to refresh our understanding. If you are looking to upskill, I suggest you sign up with Simplilearn. They offer classes in almost all disciplines.

  • Surendaran Baskaran

    Surendaran Baskaran

    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!!

  • Dastagiri Durgam

    Dastagiri Durgam

    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

    C Muthu Raman

    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.

  • Shiv Sharma

    Shiv Sharma

    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

    Akash Raj

    Technology Engineer

    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.

  • Rakshith Ramesh

    Rakshith Ramesh

    Software Program Lead Engineer

    I wanted to advance my career in the semiconductor industry, so I took a Data Science with Python course. Simplilearn's curriculum appealed to me, and my experience was enriching. The instructors were knowledgeable, and I learned both data science and Python. This certification led to a job at Lam Research with a 110% salary increase.

  • Satabdi Adhikary

    Satabdi Adhikary

    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.

prevNext

Why Join this Program

  • Develop skills for real career growthCutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills
  • Learn from experts active in their field, not out-of-touch trainersLeading practitioners who bring current best practices and case studies to sessions that fit into your work schedule.
  • Learn by working on real-world problemsCapstone projects involving real world data sets with virtual labs for hands-on learning
  • Structured guidance ensuring learning never stops24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts

Data Science with Python Training FAQs

  • What is Data Science with Python Certification?

    The Data Science with Python Certification showcases your expertise in using Python for data analysis, machine learning, and data visualization. It tests your knowledge in statistical analysis, data manipulation using libraries such as Pandas and NumPy, and machine learning tools. Simplilearn’s data science with Python course equips you to ace these skills to solve real-world data problems.

  • What skills should a data science expert know?

    With data science being a very in-demand role, an expert in this field should have the following skills:

    • Data wrangling
    • Data visualization
    • Web scraping
    • Python programming concepts
    • ScikitLearn package for Natural Language Processing
    • Data exploration
    • Mathematical computing

    Our data science with Python course will help you gain all the above skills and have a flourishing career as a data scientist.

  • Which industries use data science the most?

    Data science has applications in every industry. However, some industries use it more extensively. These include:

    • Retail
    • Healthcare
    • Banking and finance
    • Construction
    • Communications
    • Media and entertainment
    • Education
    • Energy and utility

  • Are there any other online courses Simplilearn offers under Data Science?

    Simplilearn is committed to helping learners upskill and boost their career opportunities. A wide range of master’s, post-graduate, and certification training courses are available that one can pursue. Similar programs that we offer are: 

  • Will missing a live class affect my ability to complete the course?

    No, missing a live class will not affect your ability to complete the Python data science course. With our ‘Flexi-Learn’ feature, you can watch the recording of a missed class anytime. Visit the Simplilearn learning platform, select the missed class, and watch the recording to mark your attendance.

  • What does a data scientist with Python skills do?

    A data scientist with Python skills uses the language to analyze large and complex data sets. They apply statistical methods and machine learning algorithms to uncover patterns and insights. Using Python libraries like Pandas, NumPy, and Scikit-learn, they clean data, build predictive models, and create visualizations to support data-driven decisions.

  • Who are the instructors for this data science with Python course, and how are they selected?

    The instructors for this data science with Python course are industry experts with extensive experience in the field. They are selected based on expertise, industry recognition, and teaching ability to ensure you receive top-quality education and insights.

  • How do I enroll in the data science with Python course?

    The admission process for the Python data science course consists of three simple steps: 

    • Candidates can apply through the online application form
    • An admission panel will shortlist the candidates based on their application
    • An offer of admission will be made to the selected candidates, which they can accept after paying the program fee
    • Candidates will receive a payment receipt and access information once payment is confirmed

  • What will be the expected salary range after earning a data science with Python certification?

    Data scientists with Python certifications are in high demand. In India, professionals with Python skills earn an average annual salary of INR 14.5 Lakhs, while in the United States, the average salary is approximately USD 102,123. Salaries may vary based on experience, location, and job role.

  • What is the career path after completing the Python Data Science Course?

    After completing the course, you can start as a data analyst, junior data scientist, or machine learning engineer. With experience, you can be promoted to senior roles such as senior data scientist, data engineer, or analytics consultant, where you can work on advanced data projects, develop sophisticated models, and contribute to strategic business decisions. As your skills and expertise grow, you may also move into leadership, research, or data strategy positions.

  • What job roles are available after obtaining a data science with Python certification?

    After getting a data science with Python certification, you can work as a:

    • Business Analyst
    • Database Administrator
    • Big Data Engineer or Data Architect
    • Data Analyst
    • ML Engineer
    • Business Intelligence (BI) Developer
    • Business Intelligence Analyst
    • Statistician
    • Data Scientist
    • Computer Vision(CV) Engineer
    • Natural Language Processing (NLP) Engineer
    • MLOps Engineer

  • Does Simplilearn have corporate training solutions?

    Simplilearn for Business works with Fortune 500 and mid-sized companies to provide their employees with digital skills for development. We offer diverse corporate training solutions, from short, skill-based certification training to role-based learning paths. We also offer Simplilearn Learning Hub+ - a learning library with unlimited live and interactive solutions for the entire organization. Our curriculum consultants work with each client to select and deploy the learning solutions that best meet their needs and objectives.

Related Programs

  • Acknowledgement
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, OPM3 and the PMI ATP seal are the registered marks of the Project Management Institute, Inc.