Data Science with Python Skills you will learn

  • 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

Who should learn this Data Science with Python free course?

  • Analytics Professionals
  • Software Professionals
  • IT Professionals
  • Data Scientist
  • Data Analyst

What you will learn in this Data Science with Python free course?

  • Data Science with Python

    • Lesson 01 - Course Introduction

      04:59
      • 1.01 Course Introduction
        03:06
      • 1.02 What you will Learn
        01:53
    • Lesson 02 - Introduction to Data Science

      08:16
      • 2.01 Introduction
        00:44
      • 2.02 Data Science and its Applications
        02:41
      • 2.03 The Data Science Process: Part 1
        02:15
      • 2.04 The Data Science Process: Part 2
        02:02
      • 2.05 Recap
        00:34
    • Lesson 03 - Essentials of Python Programming

      01:00:18
      • 3.01 Introduction
        00:58
      • 3.02 Setting Up Jupyter Notebook: Part 1
        02:02
      • 3.03 Setting Up Jupyter Notebook: Part 2
        04:14
      • 3.04 Python Functions
        03:57
      • 3.05 Python Types and Sequences
        04:50
      • 3.06 Python Strings Deep Dive
        07:16
      • 3.07 Python Demo: Reading and Writing csv files
        06:25
      • 3.08 Date and Time in Python
        02:34
      • 3.09 Objects in Python Map
        07:42
      • 3.10 Lambda and List Comprehension
        03:53
      • 3.11 Why Python for Data Analysis?
        02:09
      • 3.12 Python Packages for Data Science
        02:44
      • 3.13 StatsModels Package: Part 1
        02:38
      • 3.14 StatsModels Package: Part 2
        03:29
      • 3.15 Scipy Package
        02:47
      • 3.16 Recap
        00:51
      • 3.17 Spotlight
        01:49
    • Lesson 04 - NumPy

      28:19
      • 4.01 Introduction
        00:51
      • 4.02 Fundamentals of NumPy
        02:49
      • 4.03 Array shapes and axes in NumPy: Part A
        03:27
      • 4.04 NumPy Array Shapes and Axes: Part B
        03:28
      • 4.05 Arithmetic Operations
        02:35
      • 4.06 Conditional Logic
        02:48
      • 4.07 Common Mathematical and Statistical Functions in Numpy
        04:29
      • 4.08 Indexing And Slicing: Part 1
        02:27
      • 4.09 Indexing and Slicing: Part 2
        02:28
      • 4.10 File Handling
        02:24
      • 4.11 Recap
        00:33
    • Lesson 05 - Linear Algebra

      28:29
      • 5.01 Introduction
        00:51
      • 5.02 Introduction to Linear Algebra
        02:46
      • 5.03 Scalars and Vectors
        01:50
      • 5.04 Dot Product of Two Vectors
        02:37
      • 5.05 Linear independence of Vectors
        01:05
      • 5.06 Norm of a Vector
        01:30
      • 5.07 Matrix
        03:28
      • 5.08 Matrix Operations
        03:14
      • 5.09 Transpose of a Matrix
        00:59
      • 5.10 Rank of a Matrix
        02:11
      • 5.11 Determinant of a matrix and Identity matrix or operator
        02:51
      • 5.12 Inverse of a matrix and Eigenvalues and Eigenvectors
        02:45
      • 5.13 Calculus in Linear Algebra
        01:34
      • 5.14 Recap
        00:48
    • Lesson 06 - Statistics Fundamentals

      33:50
      • 6.01 Introduction
        01:00
      • 6.02 Importance of Statistics with Respect to Data Science
        02:34
      • 6.03 Common Statistical Terms
        01:46
      • 6.04 Types of Statistics
        02:50
      • 6.05 Data Categorization and Types
        03:20
      • 6.06 Levels of Measurement
        02:37
      • 6.07 Measures of Central Tendency
        01:51
      • 6.08 Measures of Central Tendency
        01:48
      • 6.09 Measures of Central Tendency
        01:02
      • 6.10 Measures of Dispersion
        02:19
      • 6.11 Random Variables
        02:17
      • 6.12 Sets
        02:40
      • 6.13 Measures of Shape (Skewness)
        02:16
      • 6.14 Measures of Shape (Kurtosis)
        01:52
      • 6.15 Covariance and Correlation
        02:44
      • 6.16 Recap
        00:54
    • Lesson 07 - Probability Distribution

      30:18
      • 7.01 Introduction
        01:02
      • 7.02 Probability,its Importance, and Probability Distribution
        03:36
      • 7.03 Probability Distribution : Binomial Distribution
        02:53
      • 7.04 Probability Distribution: Poisson Distribution
        02:29
      • 7.05 Probability Distribution: Normal Distribution
        04:19
      • 7.06 Probability Distribution: Uniform Distribution
        01:30
      • 7.07 Probability Distribution: Bernoulli Distribution
        03:05
      • 7.08 Probability Density Function and Mass Function
        02:33
      • 7.09 Cumulative Distribution Function
        02:26
      • 7.10 Central Limit Theorem
        02:57
      • 7.11 Estimation Theory
        02:49
      • 7.12 Recap
        00:39
    • Lesson 08 - Advanced Statistics

      01:07:21
      • 8.01 Introduction
        01:07
      • 8.02 Distribution
        01:45
      • 8.03 Kurtosis Skewness and Student's T-distribution
        02:32
      • 8.04 Hypothesis Testing and Mechanism
        02:25
      • 8.05 Hypothesis Testing Outcomes: Type I and II Errors
        01:54
      • 8.06 Null Hypothesis and Alternate Hypothesis
        01:47
      • 8.07 Confidence Intervals
        02:01
      • 8.08 Margins of error
        01:49
      • 8.09 Confidence Level
        01:31
      • 8.10 T - Test and P - values (Lab)
        04:50
      • 8.11 Z - Test and P - values
        05:33
      • 8.12 Comparing and Contrasting T test and Z test
        03:45
      • 8.13 Bayes Theorem
        02:24
      • 8.14 Chi Sqare Distribution
        03:16
      • 8.15 Chi Square Distribution : Demo
        03:25
      • 8.16 Chi Square Test and Goodness of Fit
        02:46
      • 8.17 Analysis of Variance or ANOVA
        02:41
      • 8.18 ANOVA Termonologies
        02:08
      • 8.19 Assumptions and Types of ANOVA
        02:53
      • 8.20 Partition of Variance using Python
        03:06
      • 8.21 F - Distribution
        02:41
      • 8.22 F - Distribution using Python
        03:59
      • 8.23 F - Test
        03:09
      • 8.24 Recap
        01:19
      • 8.25 Spotlight
        02:35
    • Lesson 09 - Pandas

      41:13
      • 9.01 Introduction
        00:52
      • 9.02 Introduction to Pandas
        02:15
      • 9.03 Pandas Series
        03:37
      • 9.04 Querying a Series
        04:01
      • 9.05 Pandas Dataframes
        03:05
      • 9.06 Pandas Panel
        01:46
      • 9.07 Common Functions In Pandas
        02:56
      • 9.08 Pandas Functions Data Statistical Function, Windows Function
        02:18
      • 9.09 Pandas Function Data and Timedelta
        02:57
      • 9.10 IO Tools Explain all the read function
        03:15
      • 9.11 Categorical Data
        02:52
      • 9.12 Working with Text Data
        03:15
      • 9.13 Iteration
        02:37
      • 9.14 Sorting
        01:19
      • 9.15 Plotting with Pandas
        03:23
      • 9.16 Recap
        00:45
    • Lesson 10 - Data Analysis

      32:25
      • 10.01 Introduction
        00:46
      • 10.02 Understanding Data
        02:31
      • 10.03 Types of Data Structured Unstructured Messy etc
        02:35
      • 10.04 Working with Data Choosing appropriate tools, Data collection, Data wrangling
        02:53
      • 10.05 Importing and Exporting Data in Python
        02:42
      • 10.06 Regular Expressions in Python
        08:24
      • 10.07 Manipulating text with Regular Expressions
        06:04
      • 10.08 Accessing databases in Python
        03:32
      • 10.09 Recap
        00:50
      • 10.10 Spotlight
        02:08
    • Lesson 11 - Data Wrangling

      34:24
      • 11.01 Introduction
        00:58
      • 11.02 Pandorable or Idiomatic Pandas Code
        06:21
      • 11.03 Loading Indexing and Reindexing
        02:45
      • 11.04 Merging
        05:48
      • 11.05 Memory Optimization in Python
        03:01
      • 11.06 Data Pre Processing: Data Loading and Dropping Null Values
        02:34
      • 11.07 Data Pre-processing Filling Null Values
        02:32
      • 11.08 Data Binning Formatting and Normalization
        04:46
      • 11.09 Data Binning Standardization
        02:19
      • 11.10 Describing Data
        02:17
      • 11.11 Recap
        01:03
    • Lesson 12 - Data Visualization

      42:55
      • 12.01 Introduction
        00:58
      • 12.02 Principles of information visualization
        02:27
      • 12.03 Visualizing Data using Pivot Tables
        02:04
      • 12.04 Data Visualization Libraries in Python Matplotlib
        01:56
      • 12.05 Graph Types
        01:36
      • 12.06 Data Visualization Libraries in Python Seaborn
        01:15
      • 12.07 Data Visualization Libraries in Python Seaborn
        02:34
      • 12.08 Data Visualization Libraries in Python Plotly
        01:07
      • 12.09 Data Visualization Libraries in Python Plotly
        02:51
      • 12.10 Data Visualization Libraries in Python Bokeh
        02:16
      • 12.11 Data Visualization Libraries in Python Bokeh
        01:59
      • 12.12 Using Matplotlib to Plot Graphs
        03:32
      • 12.13 Plotting 3D Graphs for Multiple Columns using Matplotlib
        02:14
      • 12.14 Using Matplotlib with other python packages
        03:30
      • 12.15 Using Seaborn to Plot Graphs
        02:18
      • 12.16 Using Seaborn to Plot Graphs
        01:15
      • 12.17 Plotting 3D Graphs for Multiple Columns Using Seaborn
        03:16
      • 12.18 Introduction to Plotly
        03:29
      • 12.19 Introduction to Bokeh
        01:32
      • 12.20 Recap
        00:46
    • Lesson 13 - End to End Statistics Application with Python

      35:34
      • 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

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Getting Started with Data Science with Python

Why you should learn Data Science with Python?

46% of jobs

In the data science field require Python

1581% by 2020

Growth in demand for data science professionals

Career Opportunities

  • Average Salary

    $62K - $96K Per Annum

    Hiring Companies
    Amazon
    JPMorgan Chase
    Genpact
    VMware
    LarsenAndTurbo
    Citi
    Accenture
  • Average Salary

    $100K - $217K Per Annum

    Hiring Companies
    Accenture
    Oracle
    Microsoft
    Walmart
    Amazon

FAQs

  • Why is Python popular in data science?

    Python's popularity in data science may be attributed to its ease of use, readability, and abundance of tools that facilitate data handling.

  • Is there a cost associated with this free Applied Data Science with Python course?

    No, this course is free and has no hidden charges or registration fees.

  • What are the prerequisites to learn this free course?

    There are no prerequisites to learning this course; the only requirement is your interest in learning.

  • When can I expect to receive my certificate?

    You'll receive your certificate as soon as you complete your course.

  • What is the duration of my access to the course?

    You will have access to the course for 90 Days. 

  • Can I learn data science only with Python?

    You can learn data science only with Python.

  • How challenging is this free course?

    The course is easy. 

  • Who can benefit from a data science with Python course?

    Anyone from aspiring data scientists and analysts to programmers, business professionals, students, and career changers can benefit from data science with a Python course. It's a versatile skill set applicable across various industries and career stages.

  • Do I need a strong programming background to learn data science with Python?

    No, you don't need a strong programming background to learn data science with Python.

Learner Review

  • Ashish KC Khatri

    Ashish KC Khatri

    I learned some new interesting python content, from Simplilearn's Data Science course. Looking forward to learn more.

  • Abhimanyu Chandgude

    Abhimanyu Chandgude

    Thank you Simplilearn for providing such an amazing and valuable course!

  • Mohit

    Mohit

    3rd year ECE(B.E) , PUSSGRC,Hoshiarpur,

    The Data Science with Python courses helped me a lot in improving my understanding of Python skills. I really enjoyed learning it.

  • Kipngetich Evans

    Kipngetich Evans

    The course is well-structured. I loved learning this course because it introduced me to a whole new world of Data Science.

  • Pooja Rohiwal

    Pooja Rohiwal

    The entire syllabus for this course was explained well. The best part was the exercises which helped a lot in understanding python better.

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