Data Science with Python Course Description

  • Why learn Data Science with Python?

    Python is a multi-paradigm or versatile programming language that can be considered as a sort of swiss knife for the coding world. This is because it supports structured programming, Object Oriented Programming, and even functional programming patterns. The versatility of Python undoubtedly makes it the best-suited programming language for the data scientists. Here are some of the other advantages of python for data science, which will help you understand why you should learn data science with Python:

    • Python is a powerful open source programming language, which means that it’s free to use while having all the properties that a programming language should have.
    • It is a versatile programming language that supports Object-Oriented Programming, Structured Programming, and functional programming patterns.
    • Python has some 72,000 libraries in the Python Package Index that aid in scientific calculations and machine learning applications.
    • Python sports an easy to understand and readable syntax that ensures that the development time is cut into half when compared with other programming languages.
    • Python enables you to perform data analysis, data manipulation, and data visualization, which are very important in data science.

    All the above mentioned advantages of Python programming language make it ideal to be used for data science by the data scientists. Owing to the extensibility and general purpose nature, it is recommended that you learn data science with Python.

  • What are the course objectives?

    The Data Science with Python course will furnish you with in-depth knowledge of the various libraries and packages required to perform data analysis, data visualization, web scraping, machine learning and natural language processing using Python. 
     
    Python has surpassed Java as the top language used to introduce US students to programming and computer science, and 46 percent of data science jobs list Python as a required skill.

  • What skills will you learn?

    This Python for Data Science training course will enable you to:
    • 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
    • Install the required Python environment and other auxiliary tools and libraries
    • Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
    • Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions
    • Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave
    • Perform data analysis and manipulation using data structures and tools provided in the Pandas package
    • Gain expertise in machine learning using the Scikit-Learn package
    • 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
    • Use the Scikit-Learn package for natural language processing
    • Use the matplotlib library of Python for data visualization
    • Extract useful data from websites by performing web scrapping using Python
    • Integrate Python with Hadoop, Spark and MapReduce

  • Who should take this Python for Data Science course?

    There is a booming demand for skilled data scientists across all industries that make this course suited for participants at all levels of experience. We recommend this Data Science with Python training particularly for the following professionals:

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

    Prerequisites: There are no prerequisites for this Data Science with Python course. The Python basics course included with this program provides additional coding guidance.

  • What projects are included in this Python for Data Science certification course?

    The course includes four real-world, industry-based projects. Successful evaluation of one of the following projects is a part of the certification eligibility criteria:

    Project 1: Products rating prediction for Amazon

    Amazon, one of the leading US-based e-commerce companies, recommends products within the same category to customers based on their activity and reviews on other similar products. Amazon would like to improve this recommendation engine by predicting ratings for the non-rated products and add them to recommendations accordingly.

    Domain: E-commerce

    Project 2: Demand Forecasting for Walmart

    Predict accurate sales for 45 stores of Walmart, one of the US-based leading retail stores, considering the impact of promotional markdown events. Check if macroeconomic factors like CPI, unemployment rate, etc. have an impact on sales.

    Domain: Retail

    Project 3: Improving customer experience for Comcast

    Comcast, one of the US-based global telecommunication companies wants to improve customer experience by identifying and acting on problem areas that lower customer satisfaction if any. The company is also looking for key recommendations that can be implemented to deliver the best customer experience.

    Domain: Telecom

    Project 4: Attrition Analysis for IBM

    IBM, one of the leading US-based IT companies, would like to identify the factors that influence attrition of employees. Based on the parameters identified, the company would also like to build a logistics regression model that can help predict if an employee will churn or not.

    Domain: Workforce Analytics
     

    Project 5: NYC 311 Service Request Analysis

    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.

    Domain: Telecommunication
     
    Project 6: MovieLens Dataset Analysis

    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.

    Domain: Engineering
     
    Project 7: Stock Market Data Analysis

    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.

    Domain: Stock Market
     
    Project 8: Titanic Dataset Analysis

    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.

    Domain: Hazard

Data Science with Python Course Preview

    • Lesson 01 - Course Introduction

      • 1.01 Course Introduction
        03:06
      • 1.02 What you will Learn
        01:53
    • Lesson 02 - Introduction to Data Science

      • 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

      • 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

      • 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

      • 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

      • 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

      • 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

      • 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

      • 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

      • 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

      • 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

      • 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

      • 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
    • Lesson 01: Course Introduction

      07:05
      • 1.01 Course Introduction
        05:19
      • 1.02 What Will You Learn
        01:46
    • Lesson 02: Introduction to Statistics

      25:49
      • 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

      17:29
      • 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

      34:51
      • 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

      23:36
      • 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

      21:51
      • 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

      24:45
      • 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

      36:45
      • 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

      37:08
      • 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

      27:20
      • 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

      20:07
      • 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

      17:25
      • 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
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Data Science with Python Exam & Certification

  • 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

Solomon Olutu
Solomon Olutu Snr Principal QA Architect at Comcast

Simplilearn's Data Science with Python training was a great experience. Their trainers are the best that I have come across since I started learning with Silplilearn. He is always prepared for class with a well-documented note session which is also useful for hands-on learning after class to enhance the learning experience. Thanks Simplilearn. This is the best platform that I have come across.

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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!

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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.

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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.

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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.

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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.

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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.

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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.

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.

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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.

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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.

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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!!

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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.

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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.

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Kiran Kumar
Kiran Kumar

I recently enrolled in the Data Scientist Master’s Program at Simplilearn. The syllabus is systematically structured, and the Live sessions are explained with real-time examples. This makes the course more accessible to freshers with basic knowledge. Looking forward to completing it. Thanks, Simplilearn Team.

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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.

  • 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 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.

  • Can I use my employer’s tuition-reimbursement or tuition-assistance benefit to enroll in Simplilearn’s programmes?

    Yes, Simplilearn offers certification and skills-training programmes that are designed to align with employer tuition assistance/tuition reimbursement initiatives in the US. Many of our learners receive full or partial financial backing from their organisations.

  • 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

  • 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 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.

  • Why does the course focus on NumPy if I already know Python lists?

    Standard Python lists are slow and memory-heavy when handling massive datasets. In any professional data science workflow with Python, efficiency is non-negotiable. NumPy introduces "ndarrays," fixed-type arrays optimized at the C level for lightning-fast performance.


    You’ll master Vectorization, allowing you to perform complex mathematical operations on millions of data points simultaneously without a single for-loop. This skill is the backbone of high-performance Python programming for data science.

  • What is "Idiomatic Pandas" (Pandorable) code?

    “Idomatic Pandas” or Pandorable code is a coding practices that makes efficient use of the pandas library for data analysis in Python. This coding practices focuses on writing code that is clear, readable, and optimized for performance.
    You will learn to use method chaining and vectorized functions, such as .groupby(), to make your data pipelines run faster. Additionally, writing idiomatic code ensures your work is professional, scalable, and easy for other data scientists to read and maintain.
     

  • Why is Linear Algebra included in a Data Science course?

    Every modern dataset is essentially a matrix. We include Linear Algebra concepts such as Eigenvalues, Vectors, and Dot Products because they represent the language of data transformation. Understanding the rank and determinant of a matrix allows you to see how algorithms interpret your features. 

    Mastering these mathematical foundations is what allows a practitioner to understand why certain data dimensions are more important than others when building predictive models.
     

  • How does this course help handle "Missing Data" (NaN)?

    In professional data analytics, managing incomplete data is a vital skill. This data science with Python course teaches you practical data pre-processing methods to handle "NaN" entries through statistical imputation. 
    You will learn to protect the quality of your work by filling gaps with calculated measures. This ensures your predictive models rely on a solid and complete foundation instead of flawed or missing information.
     

  • What is the "End-to-End Statistics Application" in this course?

    Theoretical knowledge only has value when applied to practical problems. The final module of this Python programming for data science program walks you through a complete project lifecycle. 

    You will start with a raw problem statement, clean the data using Pandas, perform hypothesis testing with SciPy, and conclude by visualizing the final results. This process simulates a real-world business scenario, taking you from raw numbers to a finished data story.

  • Is Python still the best programming language for data science in 2026?

    Python is still the definitive leader in 2026, holding the top spot on the TIOBE Index with its highest rating to date. Its massive ecosystem, including libraries like Pandas and TensorFlow, makes it the standard for building AI-driven models.

    The language is favored for its readable syntax, allowing you to move from a prototype to a production-ready model faster than other languages. This versatility ensures Python stays at the core of digital transformation and modern automation projects across all major tech sectors.
     

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

    Yes, Simplilearn offers several other Data Science Courses. These include specialized certifications, master programs, and university courses tailored to different skill levels.

    Data Science Courses from Leading Global Universities and Institutions:

    Courses Duration
    AI Powered Data Analytics Course - UCSB PaCE 7 Months
    Data Scientist Certificate Course - Microsoft Azure 11 Months
    Data Strategy for Leaders - Imperial Executive Education 14 Weeks
    AI and Business Analytics Course - Oxford Saïd Business School 12 Weeks
    Data Analyst Course - Microsoft Azure 11 Months
    Power BI Certification Training Live + Self Paced
    SQL Certification Course Live + Self Paced
    Tableau Certification Course Live + Self Paced

    Data Science Courses from IITs & Indian Institutions:

    Courses Duration
    AI Powered Data Analytics Course - E&ICT Academy, IIT Kanpur 4 Months
    Certificate Program in Data Analytics, Generative AI and Adaptive Systems - IHFC, TIH of IIT Delhi 6 Months

  • 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.